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89bfcf3044826128da463bebc190b036606f8e19 | jennybc/purrr | man/list_modify.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/list-modify.R
\name{list_modify}
\alias{list_modify}
\alias{list_merge}
\alias{update_list}
\title{Modify a list}
\usage{
list_modify(.x, ...)
list_merge(.x, ...)
}
\arguments{
\item{.x}{List to modify.}
\item{...}{New values of a list. Use \code{NULL} to remove values.
Use a formula to evaluate in the context of the list values.
These dots have \link[rlang:dots_list]{splicing semantics}.}
}
\description{
\code{list_modify()} and \code{list_merge()} recursively combine two lists, matching
elements either by name or position. If an sub-element is present in
both lists \code{list_modify()} takes the value from \code{y}, and \code{list_merge()}
concatenates the values together.
\code{update_list()} handles formulas and quosures that can refer to
values existing within the input list. Note that this function
might be deprecated in the future in favour of a \code{dplyr::mutate()}
method for lists.
}
\examples{
x <- list(x = 1:10, y = 4, z = list(a = 1, b = 2))
str(x)
# Update values
str(list_modify(x, a = 1))
# Replace values
str(list_modify(x, z = 5))
str(list_modify(x, z = list(a = 1:5)))
# Remove values
str(list_modify(x, z = NULL))
# Combine values
str(list_merge(x, x = 11, z = list(a = 2:5, c = 3)))
# All these functions take dots with splicing. Use !!! or UQS() to
# splice a list of arguments:
l <- list(new = 1, y = NULL, z = 5)
str(list_modify(x, !!! l))
# In update_list() you can also use quosures and formulas to
# compute new values. This function is likely to be deprecated in
# the future
str(update_list(x, z1 = ~z[[1]]))
str(update_list(x, z = rlang::quo(x + y)))
}
| 1,684 | gpl-3.0 |
45a4fa997ab725351641537096881b3e3c7ed69f | kalibera/rexp | src/library/base/man/file.info.Rd | % File src/library/base/man/file.info.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2014 R Core Team
% Distributed under GPL 2 or later
\name{file.info}
\alias{file.info}
\alias{file.mode}
\alias{file.mtime}
\alias{file.size}
\title{Extract File Information}
\description{
Utility function to extract information about files on the user's
file systems.
}
\usage{
file.info(\dots, extra_cols = TRUE)
file.mode(\dots)
file.mtime(\dots)
file.size(\dots)
}
\arguments{
\item{\dots}{character vectors containing file paths. Tilde-expansion
is done: see \code{\link{path.expand}}.}
\item{extra_cols}{Logical: return all cols rather than just the
first six.}
}
\details{
What constitutes a \sQuote{file} is OS-dependent but includes
directories. (However, directory names must not include a trailing
backslash or slash on Windows.) See also the section in the help for
\code{\link{file.exists}} on case-insensitive file systems.
The file \sQuote{mode} follows POSIX conventions, giving three octal
digits summarizing the permissions for the file owner, the owner's
group and for anyone respectively. Each digit is the logical
\emph{or} of read (4), write (2) and execute/search (1) permissions.
#ifdef unix
On most systems symbolic links are followed, so information is given
about the file to which the link points rather than about the link.
#endif
#ifdef windows
File modes are probably only useful on NTFS file systems, and it seems
all three digits refer to the file's owner.
The execute/search bits are set for directories, and for files based
on their extensions (e.g., \file{.exe}, \file{.com}, \file{.cmd}
and \file{.bat} files). \code{\link{file.access}} will give a more
reliable view of read/write access availability to the \R process.
UTF-8-encoded file names not valid in the current locale can be used.
Junction points and symbolic links are followed, so information is
given about the file/directory to which the link points rather than
about the link.
#endif
}
\value{
For \code{file.info}, data frame with row names the file names and columns
\item{size}{double: File size in bytes.}
\item{isdir}{logical: Is the file a directory?}
\item{mode}{integer of class \code{"octmode"}. The file permissions,
printed in octal, for example \code{644}.}
\item{mtime, ctime, atime}{integer of class \code{"POSIXct"}:
file modification, \sQuote{last status change} and last access times.}
#ifdef unix
\item{uid}{integer: the user ID of the file's owner.}
\item{gid}{integer: the group ID of the file's group.}
\item{uname}{character: \code{uid} interpreted as a user name.}
\item{grname}{character: \code{gid} interpreted as a group name.}
Unknown user and group names will be \code{NA}.
#endif
#ifdef windows
\item{exe}{character: what sort of executable is this? Possible
values are \code{"no"}, \code{"msdos"}, \code{"win16"},
\code{"win32"}, \code{"win64"} and \code{"unknown"}. Note that a
file (e.g. a script file) can be executable according to the mode
bits but not executable in this sense.}
#endif
If \code{extra_cols} is false, only the first six columns are
returned: as these can all be found from a single C system call this
can be faster.
Entries for non-existent or non-readable files will be \code{NA}.
#ifdef unix
The \code{uid}, \code{gid}, \code{uname} and \code{grname} columns
may not be supplied on a non-POSIX Unix-alike system, and will not be
on Windows.
#endif
What is meant by the three file times depends on the OS and file
system. On Windows native file systems \code{ctime} is the file
creation time (something which is not recorded on most Unix-alike file
systems). What is meant by \sQuote{file access} and hence the
\sQuote{last access time} is system-dependent.
The times are reported to an accuracy of seconds, and perhaps more on
some systems. However, many file systems only record times in
seconds, and some (e.g. modification time on FAT systems) are recorded
in increments of 2 or more seconds.
\code{file.mode}, \code{file.mtime} and \code{file.mode} are
convenience wrappers returning just one of the columns.
}
#ifdef unix
\note{
Some systems allow files of more than 2Gb to be created but
not accessed by the \code{stat} system call. Such files will show up
as non-readable (and very likely not be readable by any of \R's input
functions) -- fortunately such file systems are becoming rare.
}
#endif
\seealso{
\code{\link{Sys.readlink}} to find out about symbolic links,
\code{\link{files}}, \code{\link{file.access}},
\code{\link{list.files}},
and \code{\link{DateTimeClasses}} for the date formats.
\code{\link{Sys.chmod}} to change permissions.
}
\examples{
ncol(finf <- file.info(dir())) # at least six
\donttest{finf # the whole list}
## Those that are more than 100 days old :
finf <- file.info(dir(), extra_cols = FALSE)
finf[difftime(Sys.time(), finf[,"mtime"], units = "days") > 100 , 1:4]
file.info("no-such-file-exists")
}
\keyword{file}
| 5,105 | gpl-2.0 |
55c03f58102e022f55479f21bdc8fd88fe0eb358 | nickmckay/LiPD-utilities | R/deprecated/man/merge_csv_columns.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/jsons_merge.R
\name{merge_csv_columns}
\alias{merge_csv_columns}
\title{Merge values into each column}
\usage{
merge_csv_columns(csvs, meta)
}
\arguments{
\item{list}{meta: Table metadata, sorted by column}
}
\value{
list meta: Table metadata
}
\description{
Merge values into each column
}
\keyword{internal}
| 388 | gpl-2.0 |
07df5702b2a3d966f9766831f6c351885bee4d21 | surh/AMOR | man/write.qiime.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/write.qiime.r
\name{write.qiime}
\alias{write.qiime}
\alias{write.qiime.default}
\alias{write.qiime.Dataset}
\title{Write a QIIME abundance table file}
\usage{
write.qiime(x, file)
\method{write.qiime}{default}(x, file)
\method{write.qiime}{Dataset}(x, file)
}
\arguments{
\item{x}{Either an abundance matrix or a Dataset}
\item{file}{Path to the file to write}
}
\description{
Writes a file compatible with QIIME
}
\examples{
data(Rhizo)
# The following are equivalent
write.qiime(Rhizo,'myfile.txt')
write.qiime(create_dataset(Rhizo),'myfile.txt')
}
\author{
Sur Herrera Paredes
}
| 665 | gpl-3.0 |
5229f33d2785608658fec5bbaec8837588d671df | swarm-lab/Rvision | man/inPlaceComparison.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/comparisons.R, R/zzz.R
\name{inPlaceComparison}
\alias{inPlaceComparison}
\alias{\%i>\%}
\alias{\%i<\%}
\alias{\%i>=\%}
\alias{\%i<=\%}
\alias{\%i==\%}
\alias{\%i!=\%}
\title{In Place Comparison Operators for Images}
\usage{
e1 \%i>\% e2
e1 \%i<\% e2
e1 \%i>=\% e2
e1 \%i<=\% e2
e1 \%i==\% e2
e1 \%i!=\% e2
}
\arguments{
\item{e1, e2}{Either 2 \code{\link{Image}} objects or 1 \code{\link{Image}}
object and 1 numeric value/vector. If a vector and its length is less than
the number of channels of the image, then it is recycled to match it.}
}
\value{
These operators do not return anything. They modify the image in
place (destructive operation). If 2 images are passed to the operators, only
the one of the left side of the operator is modified; the other is left
untouched.
}
\description{
In Place Comparison Operators for Images
}
\examples{
balloon1 <- image(system.file("sample_img/balloon1.png", package = "Rvision"))
balloon2 <- image(system.file("sample_img/balloon2.png", package = "Rvision"))
balloon1 \%i>\% balloon2
}
\seealso{
\code{\link{Image}}
}
\author{
Simon Garnier, \email{[email protected]}
}
| 1,203 | gpl-3.0 |
523a3e1a74edce0f73b9f3025cf660c7644d0a0a | olli0601/rBEAST | man/treeannotator.plot.immu.timeline.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fun.treeannotator.R
\name{treeannotator.plot.immu.timeline}
\alias{treeannotator.plot.immu.timeline}
\title{No description yet}
\usage{
treeannotator.plot.immu.timeline(ph, ph.immu.timeline, immu.min = 150,
immu.max = 800, immu.legend = c(200, 350, 500, immu.max),
width.yinch = 0.15, add.yinch = -0.005, col.bg = cols[3],
col.legend = cols[4], cex.txt = 0.2, lines.lwd = 0.2)
}
\description{
No description yet
}
\author{
Oliver Ratmann
}
| 525 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | WelkinGuan/r-source | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | rho-devel/rho | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
424e55aef96d8d55e2a347703bb094fb6f99bcdb | wStockhausen/rTCSAM2015 | man/plotTCSAM2015I.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotTCSAM2015I.R
\name{plotTCSAM2015I}
\alias{plotTCSAM2015I}
\title{Plot TCSAM2015 model output}
\usage{
plotTCSAM2015I(repObj = NULL, prsObj = NULL, stdObj = NULL,
objList = NULL, ggtheme = theme_grey(), showPlot = TRUE, pdf = NULL,
width = 14, height = 8, verbose = FALSE)
}
\arguments{
\item{repObj}{- tcsam2015.rep object based on sourcing a TCSAM2015 model report file. can be NULL.}
\item{prsObj}{- tcsam2015.prs object based on reading a TCSAM2015 active parameters csv file. can be NULL.}
\item{stdObj}{- tcsam2015.std object based on reading a TCSAM2015 std file. can be NULL.}
\item{objList}{- list with optional elements repObj, prsObj, stdObj (an optional way to provide the Obj's)}
\item{ggtheme}{- a ggplot2 theme to use with ggplot2 plots}
\item{showPlot}{- flag to show plots immediately}
\item{pdf}{- filename for pdf output (optional)}
\item{width}{- pdf page width (in inches)}
\item{height}{- pdf page width (in inches)}
\item{verbose}{- flag (T/F) to print diagnostic info}
}
\value{
multi-level list of ggplot2 objects
}
\description{
Function to plot data and results from a TCSAM2015 model run.
}
\details{
none
}
| 1,231 | mit |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | cxxr-devel/cxxr | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | jeroenooms/r-source | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | SurajGupta/r-source | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
d67c7891a58753ffc245ab11ce18b46849069e5f | janezhango/BigDataMachineLearning | R/h2o-package/man/h2o.exportFile.Rd | \name{h2o.exportFile}
\alias{h2o.exportFile}
\title{
Export H2O Data Frame to a File.
}
\description{
Export an H2O Data Frame (which can be either VA or FV) to a file. This file may be on the H2O instance's local filesystem, or to HDFS (preface the path with hdfs://) or to S3N (preface the path with s3n://).
}
\usage{
## Default method:
h2o.exportFile(data, path, force = FALSE)
}
\arguments{
\item{data}{
An \code{\linkS4class{H2OParsedData}} or \code{\linkS4class{H2OParsedDataVA}} data frame.
}
\item{path}{
The path to write the file to. Must include the directory and filename. May be prefaced with hdfs:// or s3n://. Each row of data appears as one line of the file.
}
\item{force}{
(Optional) If \code{force = TRUE} any existing file will be overwritten. Otherwise if the file already exists the operation will fail.
}
}
\value{
None. (The function will stop if it fails.)
}
\examples{
\dontrun{
library(h2o)
localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
irisPath = system.file("extdata", "iris.csv", package = "h2o")
iris.hex = h2o.importFile(localH2O, path = irisPath)
h2o.exportFile(iris.hex, path = "/path/on/h2o/server/filesystem/iris.csv")
h2o.exportFile(iris.hex, path = "hdfs://path/in/hdfs/iris.csv")
h2o.exportFile(iris.hex, path = "s3n://path/in/s3/iris.csv")
}
} | 1,329 | apache-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | Mouseomics/R | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | andy-thomason/r-source | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | kmillar/cxxr | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | RevolutionAnalytics/RRO | R-src/src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | LeifAndersen/R | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | krlmlr/r-source | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | allr/timeR | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
2c62e026fa4ceb7ea96890d725352016b5b36825 | petercraigmile/dwt | dwt/man/plot.dwt.Rd | \name{plot.dwt}
\alias{plot.dwt}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Display the DWT decomposition graphically
}
\description{
%% ~~ A concise (1-5 lines) description of what the function does. ~~
}
\usage{
plot.dwt(x, xlab = "time", col = "black", bg = "white", bdcol = "black", ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{
%% ~~Describe \code{x} here~~
}
\item{xlab}{
%% ~~Describe \code{xlab} here~~
}
\item{col}{
%% ~~Describe \code{col} here~~
}
\item{bg}{
%% ~~Describe \code{bg} here~~
}
\item{bdcol}{
%% ~~Describe \code{bdcol} here~~
}
\item{\dots}{
%% ~~Describe \code{\dots} here~~
}
}
\details{
%% ~~ If necessary, more details than the description above ~~
}
\value{
%% ~Describe the value returned
%% If it is a LIST, use
%% \item{comp1 }{Description of 'comp1'}
%% \item{comp2 }{Description of 'comp2'}
%% ...
}
\references{
%% ~put references to the literature/web site here ~
}
\author{
%% ~~who you are~~
}
\note{
%% ~~further notes~~
}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
%% ~~objects to See Also as \code{\link{help}}, ~~~
}
\examples{
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
%\keyword{ ~kwd1 }
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
| 1,385 | gpl-3.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | minux/R | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | jimhester/r-source | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
7f73cdebe2722c61303378df02bee0e65299af24 | fcampelo/Lattes-XML-to-HTML | man/print_phd_theses.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/print_phd_theses.R
\name{print_phd_theses}
\alias{print_phd_theses}
\title{Print PhD theses}
\usage{
print_phd_theses(x, language = c("EN", "PT"))
}
\arguments{
\item{x}{data frame containing information on published phd theses}
\item{language}{Language to use in section headers}
}
\description{
Prints PhD theses defended
}
| 405 | gpl-3.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | ArunChauhan/cxxr | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | mathematicalcoffee/r-source | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | krlmlr/cxxr | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | MouseGenomics/R | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
418ccaab0a332a1666c0d46d57c23e07725c76fd | mcdelaney/fbRads | man/fb_insights.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/fb_insights.R
\name{fb_insights}
\alias{fb_insights}
\title{Insights}
\usage{
fb_insights(fbacc, target = fbacc$acct_path, job_type = c("sync", "async"),
...)
}
\arguments{
\item{fbacc}{(optional) \code{FB_Ad_account} object, which defaults to the last returned object of \code{\link{fbad_init}}.}
\item{target}{ad account id (default), campaign id, adset id or ad id}
\item{job_type}{synchronous or asynchronous request. If the prior fails with "please reduce the amount of data", it will fall back to async request.}
\item{...}{named arguments passed to the API, like time range, fields, filtering etc.}
}
\value{
list
}
\description{
Insights
}
\examples{
\dontrun{
fb_insights(fbacc)
## process results
l <- fb_insights(fbacc, date_preset = 'today', level = 'adgroup')
library(rlist)
list.stack(list.select(l, date_start, date_stop, adgroup_id, total_actions, total_unique_actions, total_action_value, impressions, unique_impressions, social_impressions, unique_social_impressions, clicks, unique_clicks, social_clicks, unique_social_clicks, spend, frequency, deeplink_clicks, app_store_clicks, website_clicks, reach, social_reach, ctr, unique_ctr, cpc, cpm, cpp, cost_per_total_action, cost_per_unique_click, relevance_score = relevance_score$score))
}
}
\references{
\url{https://developers.facebook.com/docs/marketing-api/insights/v2.3}
}
| 1,439 | agpl-3.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | SensePlatform/R | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | kmillar/rho | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | aviralg/R-dyntrace | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | wch/r-source | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | abiyug/r-source | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
b3b5b43d22db3088317cc51a1c2dc2a1db2c74a5 | rsachse/luess | man/matchInputGrid.Rd | \name{matchInputGrid}
\alias{matchInputGrid}
\title{Identify positions of output grid cells in the input grid}
\usage{
matchInputGrid(grid.in, grid.out)
}
\arguments{
\item{grid.in}{array with 2 dimensions or object of class
SpatialGrid providing longitude and lattitude of cell
centers of the input grid}
\item{grid.out}{array with 2 dimensions or object of
class SpatialGrid providing longitude and lattitude of
cell centers of the output grid}
}
\value{
vector of integers giving the positions of the output grid
cells in the input grid
}
\description{
The function identifies positions of cells of the output
grid within the input grid. This is helpful in cases the
output grid has a smaller number of cells than the input
grid.
}
\examples{
\dontrun{
mygrid <- generate_grid()
pos_in_input <- matchInputGrid(coordinates(mygrid), lpj_short_outgrid)
plot(coordinates(mygrid)[pos_in_input,], col="green", pch=".")
}
}
\author{
Rene Sachse \email{[email protected]}
}
\keyword{CLUMondo}
\keyword{LPJ,}
\keyword{LPJml,}
\keyword{cells,}
\keyword{grid}
\keyword{grid,}
\keyword{positions,}
| 1,124 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | allr/r-instrumented | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | nathan-russell/r-source | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
15ed80f235ac3d5359dd30afe7fb7ca197e759d0 | cran/readGenalex | man/as.loci.genalex.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/readGenalex-pegas.R
\name{as.loci.genalex}
\alias{as.loci.genalex}
\title{Convert class \code{'genalex'} object to data frame of class \code{'loci'} from package \code{'pegas'}}
\usage{
as.loci.genalex(x, phased = FALSE, check.annotation = TRUE, ...)
}
\arguments{
\item{x}{Annotated data frame of class \code{'genalex'}}
\item{phased}{Still some details to work out. Default \code{FALSE}. If
\code{FALSE}, assumes alleles in \code{x} are unphased so that a genotype
of \code{101/107} is identical to a genotype of \code{107/101}. This
results in the use of \code{"/"} as the allele separator. If \code{TRUE},
uses \code{"|"} as the allele separator, and assumes alleles are phased so
that a genotype of \code{101|107} is different from a genotype of
\code{107/101}.}
\item{check.annotation}{If \code{TRUE}, the annotations for the dataset
are checked using \code{is.genalex(x, force = TRUE, skip.strings = TRUE)}
prior to conversion. If that returns \code{FALSE}, nothing is converted
and an error is generated.}
\item{\dots}{Additional arguments, currently ignored}
}
\value{
\code{x} as an object of class \code{'loci'}, which is a data
frame with the genotype of each locus encoded as factors. Additional
changes apply, see Details.
}
\description{
Converts an object of class \code{'genalex'} to a data frame of class
\code{'loci'} from the
\href{http://cran.r-project.org/web/packages/pegas/index.html}{pegas}
package. This data frame is similar to one of class \code{'genalex'},
in that it mixes genetic and other data in the same data frame, but
its conversion of multiple allele columns to single genotype columns
is similar to the result of the \code{\link{as.genetics}} function
of this package.
}
\details{
Like class \code{'genalex'}, class \code{'loci'} can encode genotypes of
any ploidy. Once a class \code{'genalex'} object is converted to class
\code{'loci'}, it may be further converted to other data structures for
analysis with
\href{http://cran.r-project.org/web/packages/pegas/index.html}{pegas}
and
\href{http://cran.r-project.org/web/packages/adegenet/index.html}{adegenet}.
The specific changes that occur to an object of
class \code{'genalex'} for it to become an object of class \code{'loci'}:
\itemize{
\item Row names are set from sample names
\item The first column of sample names is removed
\item The population column is renamed \code{"population"}, with
its original name retained in the \code{"pop.title"} attribute.
It is also encoded as a factor.
\item The individual alleles of a locus are merged into a single
column, with alleles separated by \code{"/"}
(\code{phased = FALSE}) or \code{"|"} (\code{phased = TRUE}).
These columns are encoded as factors.
\item The \code{"locus.columns"} attribute is updated to reflect that
all alleles at a locus are now joined into a single column
\item A new attribute \code{"locicol"} required by class \code{'loci'}
is added, with a value identical to the \code{"locus.columns"}
attribute
\item The \code{class} is changed from \code{c('genalex', 'data.frame')}
to \code{c('loci', 'data.frame')}
}
Because of the removal of the sample name column and the additional allele
columns, the number of columns will be reduced by 1 plus the number of loci.
For further details of the structure of class \code{\link[pegas]{loci}}, see
\url{http://ape-package.ird.fr/pegas/DefinitionDataClassesPegas.pdf}.
Because class \code{'loci'} can readily encode additional columns, the
extra columns of a class \code{'genalex'} object can be bound with
\code{cbind} as additional columns.
This is a specialised wrapper around the function
\code{\link[pegas]{as.loci.data.frame}} from the
\href{http://cran.r-project.org/web/packages/pegas/index.html}{pegas}
package.
}
\examples{
suppressPackageStartupMessages(require(pegas))
data(Qagr_pericarp_genotypes)
dd <- as.genalex(head(Qagr_pericarp_genotypes, 15), force = TRUE)
as.loci(dd)
str(as.loci(dd, phased = TRUE))
}
\author{
Douglas G. Scofield
}
\seealso{
\code{\link[pegas]{as.loci}}, \code{\link{joinGenotypes}}
}
| 4,235 | lgpl-3.0 |
e8c47411697f2a6559c3d0eab90fa5e89bae96d3 | reactorlabs/gnur | src/library/stats/man/ts-methods.Rd | % File src/library/stats/man/ts-methods.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{ts-methods}
\alias{diff.ts}
\alias{na.omit.ts}
\title{Methods for Time Series Objects}
\description{
Methods for objects of class \code{"ts"}, typically the result of
\code{\link{ts}}.
}
\usage{
\method{diff}{ts}(x, lag = 1, differences = 1, \dots)
\method{na.omit}{ts}(object, \dots)
}
\arguments{
\item{x}{an object of class \code{"ts"} containing the values to be
differenced.}
\item{lag}{an integer indicating which lag to use.}
\item{differences}{an integer indicating the order of the difference.}
\item{object}{a univariate or multivariate time series.}
\item{\dots}{further arguments to be passed to or from methods.}
}
\details{
The \code{na.omit} method omits initial and final segments with
missing values in one or more of the series. \sQuote{Internal}
missing values will lead to failure.
}
\value{
For the \code{na.omit} method, a time series without missing values.
The class of \code{object} will be preserved.
}
\seealso{
\code{\link{diff}};
\code{\link{na.omit}}, \code{\link{na.fail}},
\code{\link{na.contiguous}}.
}
\keyword{ts}
| 1,255 | gpl-2.0 |
313ca166e122ef1c41b51a89f9d1b8a555c55131 | ARCCSS-extremes/climpact2 | pcic_packages/climdex.pcic.ncdf/man/get.climdex.variable.metadata.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{get.climdex.variable.metadata}
\alias{get.climdex.variable.metadata}
\title{Returns metadata for specified Climdex variables}
\usage{
get.climdex.variable.metadata(vars.list, template.filename)
}
\arguments{
\item{vars.list}{The list of variables, as returned by \code{\link{get.climdex.variable.list}}.}
\item{template.filename}{The filename template to be used when generating filenames.}
}
\value{
A data frame containing the following:
\itemize{
\item{long.name}{Long names for the variable}
\item{var.name}{Variable name for use in the file}
\item{units}{Units for the variable}
\item{annual}{Whether the variable is annual}
\item{base.period.attr}{Whether to include a base period attribute}
\item{standard.name}{Standard name to use for the variable}
\item{filename}{Filename to be written out}
}
}
\description{
Returns metadata for specified Climdex variables.
}
\details{
This function returns metadata suitable for use in NetCDF files for the specified variables.
}
\examples{
## Get metadata (including filenames) for specified variables.
fn <- "pr_day_BCCAQ+ANUSPLIN300+MRI-CGCM3_historical+rcp85_r1i1p1_19500101-21001231.nc"
var.list2 <- get.climdex.variable.list("prec", time.resolution="annual")
md <- get.climdex.variable.metadata(var.list2, fn)
}
| 1,326 | gpl-3.0 |
560945de8b7f9135f06b5142f5720a58cdbbd897 | karchjd/gppmr | man/coef.GPPM.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/extractors.R
\name{coef.GPPM}
\alias{coef.GPPM}
\title{Point Estimates}
\usage{
\method{coef}{GPPM}(object, ...)
}
\arguments{
\item{object}{object of class GPPM. Must be fitted, that is, a result from \code{\link{fit.GPPM}}.}
\item{...}{additional arguments (currently not used).}
}
\value{
Point estimates for all parameters as a named numeric vector.
}
\description{
Extracts point estimates for all parameters from a fitted GPPM.
}
\examples{
\donttest{
data("demoLGCM")
lgcm <- gppm('muI+muS*t','varI+covIS*(t+t#)+varS*t*t#+(t==t#)*sigma',
demoLGCM,'ID','y')
lgcmFit <- fit(lgcm)
paraEsts <- coef(lgcmFit)
}
}
\seealso{
Other functions to extract from a GPPM: \code{\link{SE}},
\code{\link{confint.GPPM}}, \code{\link{covf}},
\code{\link{datas}}, \code{\link{fitted.GPPM}},
\code{\link{getIntern}}, \code{\link{logLik.GPPM}},
\code{\link{maxnObs}}, \code{\link{meanf}},
\code{\link{nObs}}, \code{\link{nPars}},
\code{\link{nPers}}, \code{\link{nPreds}},
\code{\link{parEsts}}, \code{\link{pars}},
\code{\link{preds}}, \code{\link{vcov.GPPM}}
}
| 1,153 | gpl-3.0 |
92bd6705d0abc8590a3abdd067de32fa4d418d5d | DanielKneipp/DNAr | man/get_dsd_buff_str.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dsd.R
\name{get_dsd_buff_str}
\alias{get_dsd_buff_str}
\title{Instantiate a buffer module in the DSD script}
\usage{
get_dsd_buff_str(qs, qmax, Cmax, Cii, d, domains)
}
\arguments{
\item{qs}{String representing a variable name for the \code{qs}
parameter of the module signature.}
\item{qmax}{String representing a variable name for the \code{qmax}
parameter of the module signature.}
\item{Cmax}{String representing a variable name for the \code{Cmax}
parameter of the module signature.}
\item{Cii}{String representing a variable name for the \code{Cii}
parameter of the module signature.}
\item{d}{String representing a variable name for the \code{d}
parameter of the module signature.}
\item{domains}{Vector of strings representing the domains of
the input species.}
}
\value{
A string representing the instantiation of a \code{Buff()} module.
}
\description{
This function returns a string representing an addition of
a buffer reaction module in the DSD script. It creates a \code{Buff()}
module in the script, replacing all the parameter strings by the ones
specified in this function.
}
| 1,176 | agpl-3.0 |
6b1d0fc8e030a82ca033459534f1d9ea399d8f07 | predictive-technology-laboratory/sensus | SensusR/RProject/man/plot.BatteryDatum.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SensusR.R
\name{plot.BatteryDatum}
\alias{plot.BatteryDatum}
\title{Plot battery data.}
\usage{
\method{plot}{BatteryDatum}(x, pch = ".", type = "l",
main = "Battery", ...)
}
\arguments{
\item{x}{Battery data.}
\item{pch}{Plotting character.}
\item{type}{Line type.}
\item{main}{Main title.}
\item{...}{Other plotting parameters.}
}
\value{
None
}
\description{
Plot battery data.
}
| 467 | apache-2.0 |
2302bd708914df821c0fa9497393d65fe6ad73e3 | letiR/letiRmisc | man/meanAlong.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/meanAlong.R
\name{meanAlong}
\alias{meanAlong}
\title{Compute the mean along a vector}
\usage{
meanAlong(vec, n)
}
\arguments{
\item{vec}{a vector of numeric.}
\item{n}{an integer indicating the size of the window.}
}
\description{
This function is a simple moving window function.
}
\examples{
meanAlong(1:10, 2)
}
| 395 | gpl-3.0 |
066283e8b31946018ef78e0a7109f852327b0171 | BiGCAT-UM/RRegrs | RRegrs/man/RFRFEreg.Rd | \name{RFRFEreg}
\alias{RFRFEreg}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Fitting Recursive Feature Elimination - Random Forest Models.
}
\description{
RFRFEreg fits RFE RF regression models and returns resampling based performance measure using the train function by caret package.
}
\usage{
RFRFEreg(my.datf.train,my.datf.test,sCV,iSplit=1,
fDet=F,outFile="")
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{my.datf.train}{the training data set; an object where samples are in rows and features are in columns. The first column should be a numeric or factor vector containing the outcome for each sample.}
\item{my.datf.test}{the test data set.}
\item{sCV}{A string or a character vector specifying which resampling method to use. See details below.}
\item{iSplit}{a number indicating from which splitting of the data, the above train and test sets are derived. The default value is 1.}
\item{fDet}{A logical value for saving model statistics; the default value is FALSE. See below for details.}
\item{outFile}{A string specifying the output file (could include path) for details (fDet=TRUE).}
}
\details{
RMSE is the summary metric used to select the optimal model.
RFE Random Forest uses the RFE function of caret and combined with the rffunctions and randomForest modeling to obtaing the best model with the best feature set.
To control the computational nuances of the train function, trainControl is used; number of folds or resampling iterations is set to 10, and the number of completed set of folds is set to 10 (for repeated k-fold cross-validation).
sCV can take the following values: boot, boot632, cv, repeatedcv, LOOCV, LGOCV (for repeated training/test splits), none (only fits one model to the entire training set), oob (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models), "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV".
If fDet=TRUE, the following output is produced: a CSV file with detailed statistics about the regression model (Regression method, splitting number, cross-validation type, Training set summary, Test set summary, Fitting summary, List of predictors, Training predictors, Test predictors, resampling statistics, features importance, residuals of the fitted model, assessment of applicability domain (leverage analysis, Cook`s distances, points influence)), 5-12 plots for fitting statistics as a PDF file for each splitting and cross-validation method (Training Yobs-Ypred, Test Yobs-Ypred, Feature Importance, Fitted vs. Residuals for Fitted Model, Leverage for Fitted Model, Cook`s Distance for Fitted Model, 6 standard fitting plots using plot function with cutoff.Cook).
}
\value{
A list is returned containing:
\item{stat.values}{model`s statistics}
\item{model}{the full rferf model, i.e. a list of class train}
}
\examples{
\dontrun{
fDet <- FALSE
iSeed <- i
# the fraction of training set from the entire dataset;
trainFrac <- 0.75
# dataset folder for input and output files
PathDataSet <- 'DataResults'
# upload data set
ds <- read.csv(ds.Housing,header=T)
# split the data into training and test sets
dsList <- DsSplit(ds,trainFrac,fDet,PathDataSet,iSeed)
ds.train<- dsList$train
ds.test <- dsList$test
# types of cross-validation methods
CVtypes <- c('repeatedcv','LOOCV')
outLM<- 'RFRFEoutput.csv'
RFRFE.fit <- RFRFEreg(ds.train,ds.test,CVtypes[1],iSplit=1,fDet=F,outFile=outLM)
}
}
\author{
Jose A. Seoane, Carlos Fernandez-Lozano
} | 4,519 | bsd-2-clause |
9d4829d8a171238ff83b22a636bd614581aa1c27 | jpshanno/Ecohydro | man/time_seq.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Helper_Functions.R
\name{time_seq}
\alias{time_seq}
\title{Create time sequence with custom intervals}
\usage{
time_seq(start, end, step, units = "days")
}
\arguments{
\item{start}{a start date or time as a character string ("yyyy-mm-dd", "yyyy-mm-dd HH:MM", "yyyy-mm-dd HH:MM:SS)}
\item{end}{a start date or time as a character string ("yyyy-mm-dd", "yyyy-mm-dd HH:MM", "yyyy-mm-dd HH:MM:SS)}
\item{step}{a number representing the length of time in units between each step}
\item{units}{the units of step: "seconds", "minutes", "days", "months", "years"}
}
\value{
A POSIXt vector
}
\description{
Creates a POSIXt vector from the start time to the end time with N number of
steps, based on the interval length choosen.
}
\examples{
time_seq("2016-11-01", "2016-11-03", step = 15, units = "minutes")
}
| 883 | gpl-2.0 |
b3c9e883ed06880da9a356965c3285affc158699 | cran/zipfR | man/productivity_measures.Rd | \name{productivity.measures}
\alias{productivity.measures}
\alias{productivity.measures.tfl}
\alias{productivity.measures.spc}
\alias{productivity.measures.vgc}
\alias{productivity.measures.default}
\title{Measures of Productivity and Lexical Richness (zipfR)}
\encoding{UTF-8}
\description{
Compute various measures of productivity and lexical richness from
an observed frequency spectrum, or type-frequency list, from an
observed vocabulary growth curve or from a vector of tokens.
}
\usage{
productivity.measures(obj, measures, ...)
\method{productivity.measures}{tfl}(obj, measures, ...)
\method{productivity.measures}{spc}(obj, measures, ...)
\method{productivity.measures}{vgc}(obj, measures, ...)
\method{productivity.measures}{default}(obj, measures, ...)
}
\arguments{
\item{obj}{a suitable data object from which productivity measures
can be computed. Currently either a frequency spectrum
(of class \code{spc}), a type-frequency list (of class \code{tfl}),
a vocabulary growth curve (of class \code{vgc}), or a token vector.}
\item{measures}{character vector naming the productivity measures to
be computed (see "Productivity Measures" below).
Names may be abbreviated as long as they remain unique.
If unspecified, all supported measures are computed.}
\item{...}{additional arguments passed on to the method implementations
(currently, no further arguments are recognized)}
}
\value{
If \code{obj} is a frequency spectrum, type-frequency list or token vector:
A numeric vector of the same length as \code{measures} with the corresponding observed values of the productivity measures.
If \code{obj} is a vocabulary growth curves:
A numeric matrix with columns corresponding to the selected productivity measures and rows corresponding to the sample sizes of the vocabulary growth curve.
}
\details{
This function computes productivity measures based on an observed frequency spectrum, type-frequency list or vocabulary growth curve.
If an \emph{expected} spectrum or VGC is passed, the expectations \eqn{E[V]}, \eqn{E[V_m]} will simply be substituted for the sample values \eqn{V}, \eqn{V_m} in the equations. In most cases, this does \emph{not} yield the expected value of the productivity measure!
Some measures can only be computed from a complete frequency spectrum. They will return \code{NA} if \code{obj} is an incomplete spectrum or type-frequency list, an expected spectrum or a vocabulary growth curve is passed.
Some other measures can only be computed is a sufficient number of spectrum elements is included in a vocabulary growth curve (usually at least
\eqn{V_1} and \eqn{V_2}), and will return \code{NA} otherwise.
Such limitations are indicated in the list of measures below (unless spectrum elements \eqn{V_1} and \eqn{V_2} are sufficient).
For an expected frequency spectrum or vocabulary growth curve, accuracte expectations can be computed for the measures
\eqn{R}, \eqn{C}, \eqn{P}, TTR and \eqn{V}. For \eqn{S}, \eqn{H} and Hapaxes, the expecations are often reasonably
good approximations (based on a normal approximation of the ratio \eqn{V_m / V} derived from Evert
(2004b, Lemma A.8) using an (incorrect) independence assumption for \eqn{V_m} and \eqn{V - V_m}).
}
\section{Productivity Measures}{
The following productivity measures are currently supported:
\describe{
\item{\code{K}:}{
Yule's (1944) \eqn{K = 10000 \cdot \frac{ \sum_m m^2 V_m - N}{ N^2 }}{K = 10000 * (SUM(m) m^2 Vm - N) / N^2} \cr
(only for complete observed frequency spectrum)
}
\item{\code{D}:}{
Simpson's (1949) \eqn{D = \sum_m V_m \frac{m}{N}\cdot \frac{m-1}{N-1}}{D = SUM(m) Vm * (m / N) * ((m - 1) / (N - 1))} \cr
(only for complete observed frequency spectrum)
}
\item{\code{R}:}{
Guiraud's (1954) \eqn{R = V / \sqrt{N}}
}
\item{\code{S}:}{
Sichel's (1975) \eqn{S = V_2 / V}{S = V2 / V}, i.e. the proportion of \emph{dis legomena}
}
\item{\code{H}:}{
Honoré's (1979) \eqn{H = 100 \frac{ \log N }{ 1 - V_1 / V }}{H = 100 * log(N) / (1 - V1 / V)}, a transformation of the proportion of \emph{hapax legomena} adjusted for sample size
}
\item{\code{C}:}{
Herdan's (1964) \eqn{C = \frac{ \log V }{ \log N }}{C = log(V) / log(N)}
}
\item{\code{P}:}{
Baayen's (1991) productivity index \eqn{P = \frac{V_1}{N}}{P = V1 / N}, which corresponds to the slope of the vocabulary growth curve (under random sampling assumptions)
}
\item{\code{TTR}:}{
the type-token ratio TTR = \eqn{V / N}
}
\item{\code{Hapax}:}{
the proportion of \emph{hapax legomena} \eqn{\frac{V_1}{V}}{V1 / V}
}
\item{\code{V}:}{
the total number of types \eqn{V}
}
%% \item{\code{}:}{}
}
}
\references{
Evert, Stefan (2004b). \emph{The Statistics of Word Cooccurrences: Word
Pairs and Collocations.} PhD Thesis, IMS, University of Stuttgart.
URN urn:nbn:de:bsz:93-opus-23714
\url{http://elib.uni-stuttgart.de/opus/volltexte/2005/2371/}
}
\seealso{
\code{\link{lnre.bootstrap}} and \code{\link{bootstrap.confint}} for parametric bootstrapping experiments,
which help to determine the true expectations and sampling distributions of all productivity measures.
}
\keyword{ methods }
\keyword{ univar }
\examples{
## TODO
}
| 5,389 | gpl-3.0 |
5e653dd1250afe71b84b0921bf9e8c27c13144ff | jcfisher/latentnetDiffusion | man/tidyDegrootList.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tidy_degroot_list.R
\name{tidyDegrootList}
\alias{tidyDegrootList}
\title{Wrapper function for tidyDegroot that calls tidyDegroot on a list of degroot
matrices}
\usage{
tidyDegrootList(pred, id)
}
\arguments{
\item{pred:}{a list of matrices, generally produced by ergmmDegroot}
\item{id:}{a vector of ID values that will be added as a column to the
resulting data.frame}
}
\value{
a data.frame (in tibble form) with 4 columns: the 3 columns produced
by tidyDegroot, plus an additional column for the number of draws, which
denotes the index from the original list
}
\description{
Wrapper function for tidyDegroot that calls tidyDegroot on a list of degroot
matrices
}
| 772 | gpl-3.0 |
066283e8b31946018ef78e0a7109f852327b0171 | enanomapper/RRegrs | RRegrs/man/RFRFEreg.Rd | \name{RFRFEreg}
\alias{RFRFEreg}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Fitting Recursive Feature Elimination - Random Forest Models.
}
\description{
RFRFEreg fits RFE RF regression models and returns resampling based performance measure using the train function by caret package.
}
\usage{
RFRFEreg(my.datf.train,my.datf.test,sCV,iSplit=1,
fDet=F,outFile="")
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{my.datf.train}{the training data set; an object where samples are in rows and features are in columns. The first column should be a numeric or factor vector containing the outcome for each sample.}
\item{my.datf.test}{the test data set.}
\item{sCV}{A string or a character vector specifying which resampling method to use. See details below.}
\item{iSplit}{a number indicating from which splitting of the data, the above train and test sets are derived. The default value is 1.}
\item{fDet}{A logical value for saving model statistics; the default value is FALSE. See below for details.}
\item{outFile}{A string specifying the output file (could include path) for details (fDet=TRUE).}
}
\details{
RMSE is the summary metric used to select the optimal model.
RFE Random Forest uses the RFE function of caret and combined with the rffunctions and randomForest modeling to obtaing the best model with the best feature set.
To control the computational nuances of the train function, trainControl is used; number of folds or resampling iterations is set to 10, and the number of completed set of folds is set to 10 (for repeated k-fold cross-validation).
sCV can take the following values: boot, boot632, cv, repeatedcv, LOOCV, LGOCV (for repeated training/test splits), none (only fits one model to the entire training set), oob (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models), "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV".
If fDet=TRUE, the following output is produced: a CSV file with detailed statistics about the regression model (Regression method, splitting number, cross-validation type, Training set summary, Test set summary, Fitting summary, List of predictors, Training predictors, Test predictors, resampling statistics, features importance, residuals of the fitted model, assessment of applicability domain (leverage analysis, Cook`s distances, points influence)), 5-12 plots for fitting statistics as a PDF file for each splitting and cross-validation method (Training Yobs-Ypred, Test Yobs-Ypred, Feature Importance, Fitted vs. Residuals for Fitted Model, Leverage for Fitted Model, Cook`s Distance for Fitted Model, 6 standard fitting plots using plot function with cutoff.Cook).
}
\value{
A list is returned containing:
\item{stat.values}{model`s statistics}
\item{model}{the full rferf model, i.e. a list of class train}
}
\examples{
\dontrun{
fDet <- FALSE
iSeed <- i
# the fraction of training set from the entire dataset;
trainFrac <- 0.75
# dataset folder for input and output files
PathDataSet <- 'DataResults'
# upload data set
ds <- read.csv(ds.Housing,header=T)
# split the data into training and test sets
dsList <- DsSplit(ds,trainFrac,fDet,PathDataSet,iSeed)
ds.train<- dsList$train
ds.test <- dsList$test
# types of cross-validation methods
CVtypes <- c('repeatedcv','LOOCV')
outLM<- 'RFRFEoutput.csv'
RFRFE.fit <- RFRFEreg(ds.train,ds.test,CVtypes[1],iSplit=1,fDet=F,outFile=outLM)
}
}
\author{
Jose A. Seoane, Carlos Fernandez-Lozano
} | 4,519 | bsd-2-clause |
9d4829d8a171238ff83b22a636bd614581aa1c27 | jpshanno/ecoFlux | man/time_seq.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Helper_Functions.R
\name{time_seq}
\alias{time_seq}
\title{Create time sequence with custom intervals}
\usage{
time_seq(start, end, step, units = "days")
}
\arguments{
\item{start}{a start date or time as a character string ("yyyy-mm-dd", "yyyy-mm-dd HH:MM", "yyyy-mm-dd HH:MM:SS)}
\item{end}{a start date or time as a character string ("yyyy-mm-dd", "yyyy-mm-dd HH:MM", "yyyy-mm-dd HH:MM:SS)}
\item{step}{a number representing the length of time in units between each step}
\item{units}{the units of step: "seconds", "minutes", "days", "months", "years"}
}
\value{
A POSIXt vector
}
\description{
Creates a POSIXt vector from the start time to the end time with N number of
steps, based on the interval length choosen.
}
\examples{
time_seq("2016-11-01", "2016-11-03", step = 15, units = "minutes")
}
| 883 | gpl-2.0 |
066283e8b31946018ef78e0a7109f852327b0171 | muntisa/RRegrs | RRegrs/man/RFRFEreg.Rd | \name{RFRFEreg}
\alias{RFRFEreg}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Fitting Recursive Feature Elimination - Random Forest Models.
}
\description{
RFRFEreg fits RFE RF regression models and returns resampling based performance measure using the train function by caret package.
}
\usage{
RFRFEreg(my.datf.train,my.datf.test,sCV,iSplit=1,
fDet=F,outFile="")
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{my.datf.train}{the training data set; an object where samples are in rows and features are in columns. The first column should be a numeric or factor vector containing the outcome for each sample.}
\item{my.datf.test}{the test data set.}
\item{sCV}{A string or a character vector specifying which resampling method to use. See details below.}
\item{iSplit}{a number indicating from which splitting of the data, the above train and test sets are derived. The default value is 1.}
\item{fDet}{A logical value for saving model statistics; the default value is FALSE. See below for details.}
\item{outFile}{A string specifying the output file (could include path) for details (fDet=TRUE).}
}
\details{
RMSE is the summary metric used to select the optimal model.
RFE Random Forest uses the RFE function of caret and combined with the rffunctions and randomForest modeling to obtaing the best model with the best feature set.
To control the computational nuances of the train function, trainControl is used; number of folds or resampling iterations is set to 10, and the number of completed set of folds is set to 10 (for repeated k-fold cross-validation).
sCV can take the following values: boot, boot632, cv, repeatedcv, LOOCV, LGOCV (for repeated training/test splits), none (only fits one model to the entire training set), oob (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models), "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV".
If fDet=TRUE, the following output is produced: a CSV file with detailed statistics about the regression model (Regression method, splitting number, cross-validation type, Training set summary, Test set summary, Fitting summary, List of predictors, Training predictors, Test predictors, resampling statistics, features importance, residuals of the fitted model, assessment of applicability domain (leverage analysis, Cook`s distances, points influence)), 5-12 plots for fitting statistics as a PDF file for each splitting and cross-validation method (Training Yobs-Ypred, Test Yobs-Ypred, Feature Importance, Fitted vs. Residuals for Fitted Model, Leverage for Fitted Model, Cook`s Distance for Fitted Model, 6 standard fitting plots using plot function with cutoff.Cook).
}
\value{
A list is returned containing:
\item{stat.values}{model`s statistics}
\item{model}{the full rferf model, i.e. a list of class train}
}
\examples{
\dontrun{
fDet <- FALSE
iSeed <- i
# the fraction of training set from the entire dataset;
trainFrac <- 0.75
# dataset folder for input and output files
PathDataSet <- 'DataResults'
# upload data set
ds <- read.csv(ds.Housing,header=T)
# split the data into training and test sets
dsList <- DsSplit(ds,trainFrac,fDet,PathDataSet,iSeed)
ds.train<- dsList$train
ds.test <- dsList$test
# types of cross-validation methods
CVtypes <- c('repeatedcv','LOOCV')
outLM<- 'RFRFEoutput.csv'
RFRFE.fit <- RFRFEreg(ds.train,ds.test,CVtypes[1],iSplit=1,fDet=F,outFile=outLM)
}
}
\author{
Jose A. Seoane, Carlos Fernandez-Lozano
} | 4,519 | bsd-2-clause |
066283e8b31946018ef78e0a7109f852327b0171 | egonw/RRegrs | RRegrs/man/RFRFEreg.Rd | \name{RFRFEreg}
\alias{RFRFEreg}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Fitting Recursive Feature Elimination - Random Forest Models.
}
\description{
RFRFEreg fits RFE RF regression models and returns resampling based performance measure using the train function by caret package.
}
\usage{
RFRFEreg(my.datf.train,my.datf.test,sCV,iSplit=1,
fDet=F,outFile="")
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{my.datf.train}{the training data set; an object where samples are in rows and features are in columns. The first column should be a numeric or factor vector containing the outcome for each sample.}
\item{my.datf.test}{the test data set.}
\item{sCV}{A string or a character vector specifying which resampling method to use. See details below.}
\item{iSplit}{a number indicating from which splitting of the data, the above train and test sets are derived. The default value is 1.}
\item{fDet}{A logical value for saving model statistics; the default value is FALSE. See below for details.}
\item{outFile}{A string specifying the output file (could include path) for details (fDet=TRUE).}
}
\details{
RMSE is the summary metric used to select the optimal model.
RFE Random Forest uses the RFE function of caret and combined with the rffunctions and randomForest modeling to obtaing the best model with the best feature set.
To control the computational nuances of the train function, trainControl is used; number of folds or resampling iterations is set to 10, and the number of completed set of folds is set to 10 (for repeated k-fold cross-validation).
sCV can take the following values: boot, boot632, cv, repeatedcv, LOOCV, LGOCV (for repeated training/test splits), none (only fits one model to the entire training set), oob (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models), "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV".
If fDet=TRUE, the following output is produced: a CSV file with detailed statistics about the regression model (Regression method, splitting number, cross-validation type, Training set summary, Test set summary, Fitting summary, List of predictors, Training predictors, Test predictors, resampling statistics, features importance, residuals of the fitted model, assessment of applicability domain (leverage analysis, Cook`s distances, points influence)), 5-12 plots for fitting statistics as a PDF file for each splitting and cross-validation method (Training Yobs-Ypred, Test Yobs-Ypred, Feature Importance, Fitted vs. Residuals for Fitted Model, Leverage for Fitted Model, Cook`s Distance for Fitted Model, 6 standard fitting plots using plot function with cutoff.Cook).
}
\value{
A list is returned containing:
\item{stat.values}{model`s statistics}
\item{model}{the full rferf model, i.e. a list of class train}
}
\examples{
\dontrun{
fDet <- FALSE
iSeed <- i
# the fraction of training set from the entire dataset;
trainFrac <- 0.75
# dataset folder for input and output files
PathDataSet <- 'DataResults'
# upload data set
ds <- read.csv(ds.Housing,header=T)
# split the data into training and test sets
dsList <- DsSplit(ds,trainFrac,fDet,PathDataSet,iSeed)
ds.train<- dsList$train
ds.test <- dsList$test
# types of cross-validation methods
CVtypes <- c('repeatedcv','LOOCV')
outLM<- 'RFRFEoutput.csv'
RFRFE.fit <- RFRFEreg(ds.train,ds.test,CVtypes[1],iSplit=1,fDet=F,outFile=outLM)
}
}
\author{
Jose A. Seoane, Carlos Fernandez-Lozano
} | 4,519 | bsd-2-clause |
9db9839531d77ad456b1de6672ddd12dbf3cf820 | IQSS/Zelig4 | man/is.zelig.package.Rd | \name{is.zelig.package}
\alias{is.zelig.package}
\title{Wether an Installed R-Pack Depends on Zelig}
\usage{
is.zelig.package(package = "")
}
\arguments{
\item{package}{a character-string naming a package}
}
\value{
whether this package depends on Zelig
}
\description{
Wether an Installed R-Pack Depends on Zelig
}
\note{
This package was used internally to determine whether an
R-package is Zelig compliant, but is now likely
deprecated. This test is useless if not paired with
}
| 497 | gpl-2.0 |
9eef49e683fdfe92fe8936bfd510327e7fd290e5 | cran/lossDev | man/rateOfDecay-comma-BreakAnnualAggLossDevModelOutput-dash-method.Rd | \name{rateOfDecay,BreakAnnualAggLossDevModelOutput-method}
\alias{rateOfDecay,BreakAnnualAggLossDevModelOutput-method}
\title{A method to plot and/or return the esimtated rate of decay vs development year time for break models.}
\description{A method to plot and/or return the esimtated rate of decay vs development year time for break models.}
\details{The simplest definition of the rate of decay is the exponentiated first difference of the \link[=consumptionPath]{consumption path}.
The break model has two rates of decay. One which applies to exposure years prior to a structural break. And another which applies after the break.
This is a method to allow for the retrieval and illustration of these rates of decay.
Because the model is Bayesian, the estimated rates of decay come as distributions; only the medians are plotted and/or returned.}
\value{Mainly called for the side effect of plotting. Also returns the plotted statistics. Returned invisibly.}
\docType{methods}
\seealso{\code{\link{rateOfDecay}}
\code{\link[=rateOfDecay,StandardAnnualAggLossDevModelOutput-method]{rateOfDecay("StandardAnnualAggLossDevModelOutput")}}
\code{\link{consumptionPath}}}
\arguments{\item{object}{The object from which to plot and/or return the estimated rate of decay.}
\item{plot}{A logical value. If \code{TRUE}, then the plot is generated and the statistics are returned; otherwise only the statistics are returned.}}
| 1,424 | gpl-3.0 |
cbc85aee136817989fbdc64f9d7f49bdc7e038e6 | mirror/r | src/library/stats/man/pp.test.Rd | % File src/library/stats/man/pp.test.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2008 R Core Team
% Distributed under GPL 2 or later
\name{PP.test}
\alias{PP.test}
\title{Phillips-Perron Test for Unit Roots}
\usage{
PP.test(x, lshort = TRUE)
}
\arguments{
\item{x}{a numeric vector or univariate time series.}
\item{lshort}{a logical indicating whether the short or long version
of the truncation lag parameter is used.}
}
\description{
Computes the Phillips-Perron test for the null hypothesis that
\code{x} has a unit root against a stationary alternative.
}
\details{
The general regression equation which incorporates a constant and a
linear trend is used and the corrected t-statistic for a first order
autoregressive coefficient equals one is computed. To estimate
\code{sigma^2} the Newey-West estimator is used. If \code{lshort}
is \code{TRUE}, then the truncation lag parameter is set to
\code{trunc(4*(n/100)^0.25)}, otherwise
\code{trunc(12*(n/100)^0.25)} is used. The p-values are
interpolated from Table 4.2, page 103 of Banerjee \emph{et al}
(1993).
Missing values are not handled.
}
\value{
A list with class \code{"htest"} containing the following components:
\item{statistic}{the value of the test statistic.}
\item{parameter}{the truncation lag parameter.}
\item{p.value}{the p-value of the test.}
\item{method}{a character string indicating what type of test was
performed.}
\item{data.name}{a character string giving the name of the data.}
}
\references{
A. Banerjee, J. J. Dolado, J. W. Galbraith, and D. F. Hendry (1993)
\emph{Cointegration, Error Correction, and the Econometric Analysis
of Non-Stationary Data}, Oxford University Press, Oxford.
P. Perron (1988) Trends and random walks in macroeconomic time
series. \emph{Journal of Economic Dynamics and Control} \bold{12},
297--332.
}
\author{A. Trapletti}
\examples{
x <- rnorm(1000)
PP.test(x)
y <- cumsum(x) # has unit root
PP.test(y)
}
\keyword{ts}
| 2,023 | gpl-2.0 |
f6da763a929eb50ca46247011f4fde5054c46634 | jillianderson8/cydr | man/pass_end_turns.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PassEndTurns.R
\name{pass_end_turns}
\alias{pass_end_turns}
\title{Identify pass-end turns}
\usage{
pass_end_turns(data, remove=FALSE, short_angle=45, long_angle=178, short_offset =5, long_offset=20)
}
\arguments{
\item{data}{a dataframe standardized and outputted from AgLeader.}
\item{remove}{a boolean. Defaults to \code{FALSE}. Indicates whether to remove
identified errors.}
\item{short_angle}{a number between 0 and 180. Defaults to 45. Used as the angle
to determine whether an observation is within a short-turn. If the difference between
compared points is greater than this angle, a short-turn is identified.}
\item{long_angle}{a number between 0 and 180. Defaults to 178. Used as the angle
to determine whether an observation is within a long-turn. If the difference between
compared points is greater than this angle, a long-turn is identified.}
\item{short_offset}{a number greater than 0 such that \code{short_offset < long_offset}.
Defaults to 5. Used to determine the pair of numbers which will be compared to determine
whether a short-turn is occurring. For example, a \code{short_offset} of 5 will compare
the point 5 prior and the point 5 past the point of interest. If the difference in these
points' directions is greater than \code{short_angle}, a short-turn is occurring.}
\item{long_offset}{a number greater than 0 such that \code{short_offset < long_offset}.
Defaults to 20. Used to determine the pair of numbers which will be compared to determine
whether a long-turn is occurring. For example, a \code{long_offset} of 20 will compare
the point 20 prior and the point 20 past the point of interest. If the difference in these
points' directions is greater than \code{long_angle}, a long-turn is occurring.}
}
\value{
A dataframe with an added column called \code{cydr_PassEndError}. This column
will be set to \code{TRUE} if it meets the criteria for an erroneous observation.
}
\description{
Adds a column called \code{cydr_PassEndError} to a dataframe to
identify observations occurring within pass-end turns. These observations are
identified by comparing two pairs of points occurring before and after the point
of interest. If the difference in direction between both pairs of points is
above their respective thresholds, the observation is identified as pass-end
turn error.
The first pair identifies whether the point is within a "short-turn", by default
checking if the points 5 before and 5 after have a difference in direction equal
to or greater than 45 degrees. The second pair identifies whether the point is
within a "long-turn", by default checking whether the points 20 before and 20
after have a difference in direction of 178 degrees or greater.
The thresholds used to determine short- and long-turns can all be customized using
the provided arguments.
}
\examples{
pass_end_turns(data)
pass_end_turns(data, remove=TRUE) # Removes all identified errors
pass_end_turns(data, long_angle=170) # Identifies differences in long_offset points
of > 170 as erroneous.
}
\seealso{
Other core functions: \code{\link{narrow_passes}},
\code{\link{residual_outliers}}, \code{\link{speed}}
}
| 3,226 | gpl-3.0 |
cbc85aee136817989fbdc64f9d7f49bdc7e038e6 | kalibera/rexp | src/library/stats/man/pp.test.Rd | % File src/library/stats/man/pp.test.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2008 R Core Team
% Distributed under GPL 2 or later
\name{PP.test}
\alias{PP.test}
\title{Phillips-Perron Test for Unit Roots}
\usage{
PP.test(x, lshort = TRUE)
}
\arguments{
\item{x}{a numeric vector or univariate time series.}
\item{lshort}{a logical indicating whether the short or long version
of the truncation lag parameter is used.}
}
\description{
Computes the Phillips-Perron test for the null hypothesis that
\code{x} has a unit root against a stationary alternative.
}
\details{
The general regression equation which incorporates a constant and a
linear trend is used and the corrected t-statistic for a first order
autoregressive coefficient equals one is computed. To estimate
\code{sigma^2} the Newey-West estimator is used. If \code{lshort}
is \code{TRUE}, then the truncation lag parameter is set to
\code{trunc(4*(n/100)^0.25)}, otherwise
\code{trunc(12*(n/100)^0.25)} is used. The p-values are
interpolated from Table 4.2, page 103 of Banerjee \emph{et al}
(1993).
Missing values are not handled.
}
\value{
A list with class \code{"htest"} containing the following components:
\item{statistic}{the value of the test statistic.}
\item{parameter}{the truncation lag parameter.}
\item{p.value}{the p-value of the test.}
\item{method}{a character string indicating what type of test was
performed.}
\item{data.name}{a character string giving the name of the data.}
}
\references{
A. Banerjee, J. J. Dolado, J. W. Galbraith, and D. F. Hendry (1993)
\emph{Cointegration, Error Correction, and the Econometric Analysis
of Non-Stationary Data}, Oxford University Press, Oxford.
P. Perron (1988) Trends and random walks in macroeconomic time
series. \emph{Journal of Economic Dynamics and Control} \bold{12},
297--332.
}
\author{A. Trapletti}
\examples{
x <- rnorm(1000)
PP.test(x)
y <- cumsum(x) # has unit root
PP.test(y)
}
\keyword{ts}
| 2,023 | gpl-2.0 |
cbc85aee136817989fbdc64f9d7f49bdc7e038e6 | skyguy94/R | src/library/stats/man/pp.test.Rd | % File src/library/stats/man/pp.test.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2008 R Core Team
% Distributed under GPL 2 or later
\name{PP.test}
\alias{PP.test}
\title{Phillips-Perron Test for Unit Roots}
\usage{
PP.test(x, lshort = TRUE)
}
\arguments{
\item{x}{a numeric vector or univariate time series.}
\item{lshort}{a logical indicating whether the short or long version
of the truncation lag parameter is used.}
}
\description{
Computes the Phillips-Perron test for the null hypothesis that
\code{x} has a unit root against a stationary alternative.
}
\details{
The general regression equation which incorporates a constant and a
linear trend is used and the corrected t-statistic for a first order
autoregressive coefficient equals one is computed. To estimate
\code{sigma^2} the Newey-West estimator is used. If \code{lshort}
is \code{TRUE}, then the truncation lag parameter is set to
\code{trunc(4*(n/100)^0.25)}, otherwise
\code{trunc(12*(n/100)^0.25)} is used. The p-values are
interpolated from Table 4.2, page 103 of Banerjee \emph{et al}
(1993).
Missing values are not handled.
}
\value{
A list with class \code{"htest"} containing the following components:
\item{statistic}{the value of the test statistic.}
\item{parameter}{the truncation lag parameter.}
\item{p.value}{the p-value of the test.}
\item{method}{a character string indicating what type of test was
performed.}
\item{data.name}{a character string giving the name of the data.}
}
\references{
A. Banerjee, J. J. Dolado, J. W. Galbraith, and D. F. Hendry (1993)
\emph{Cointegration, Error Correction, and the Econometric Analysis
of Non-Stationary Data}, Oxford University Press, Oxford.
P. Perron (1988) Trends and random walks in macroeconomic time
series. \emph{Journal of Economic Dynamics and Control} \bold{12},
297--332.
}
\author{A. Trapletti}
\examples{
x <- rnorm(1000)
PP.test(x)
y <- cumsum(x) # has unit root
PP.test(y)
}
\keyword{ts}
| 2,023 | gpl-2.0 |
cbc85aee136817989fbdc64f9d7f49bdc7e038e6 | hxfeng/R-3.1.2 | src/library/stats/man/pp.test.Rd | % File src/library/stats/man/pp.test.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2008 R Core Team
% Distributed under GPL 2 or later
\name{PP.test}
\alias{PP.test}
\title{Phillips-Perron Test for Unit Roots}
\usage{
PP.test(x, lshort = TRUE)
}
\arguments{
\item{x}{a numeric vector or univariate time series.}
\item{lshort}{a logical indicating whether the short or long version
of the truncation lag parameter is used.}
}
\description{
Computes the Phillips-Perron test for the null hypothesis that
\code{x} has a unit root against a stationary alternative.
}
\details{
The general regression equation which incorporates a constant and a
linear trend is used and the corrected t-statistic for a first order
autoregressive coefficient equals one is computed. To estimate
\code{sigma^2} the Newey-West estimator is used. If \code{lshort}
is \code{TRUE}, then the truncation lag parameter is set to
\code{trunc(4*(n/100)^0.25)}, otherwise
\code{trunc(12*(n/100)^0.25)} is used. The p-values are
interpolated from Table 4.2, page 103 of Banerjee \emph{et al}
(1993).
Missing values are not handled.
}
\value{
A list with class \code{"htest"} containing the following components:
\item{statistic}{the value of the test statistic.}
\item{parameter}{the truncation lag parameter.}
\item{p.value}{the p-value of the test.}
\item{method}{a character string indicating what type of test was
performed.}
\item{data.name}{a character string giving the name of the data.}
}
\references{
A. Banerjee, J. J. Dolado, J. W. Galbraith, and D. F. Hendry (1993)
\emph{Cointegration, Error Correction, and the Econometric Analysis
of Non-Stationary Data}, Oxford University Press, Oxford.
P. Perron (1988) Trends and random walks in macroeconomic time
series. \emph{Journal of Economic Dynamics and Control} \bold{12},
297--332.
}
\author{A. Trapletti}
\examples{
x <- rnorm(1000)
PP.test(x)
y <- cumsum(x) # has unit root
PP.test(y)
}
\keyword{ts}
| 2,023 | gpl-2.0 |
b47754e22e2c4d2c77382de9ab42c5f48e7b61a4 | radfordneal/pqR | src/library/graphics/man/stripchart.Rd | % File src/library/graphics/man/stripchart.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{stripchart}
\title{1-D Scatter Plots}
\alias{stripchart}
\alias{stripchart.default}
\alias{stripchart.formula}
\description{
\code{stripchart} produces one dimensional scatter plots (or dot
plots) of the given data. These plots are a good alternative to
\code{\link{boxplot}}s when sample sizes are small.
}
\usage{
stripchart(x, \dots)
\method{stripchart}{formula}(x, data = NULL, dlab = NULL, \dots,
subset, na.action = NULL)
\method{stripchart}{default}(x, method = "overplot", jitter = 0.1, offset = 1/3,
vertical = FALSE, group.names, add = FALSE,
at = NULL, xlim = NULL, ylim = NULL,
ylab=NULL, xlab=NULL, dlab="", glab="",
log = "", pch = 0, col = par("fg"), cex = par("cex"),
axes = TRUE, frame.plot = axes, \dots)
}
\arguments{
\item{x}{the data from which the plots are to be produced. In the
default method the data can be specified as a single numeric
vector, or as list of numeric vectors, each corresponding to
a component plot. In the \code{formula} method, a symbolic
specification of the form \code{y ~ g} can be given,
indicating the observations in the vector \code{y} are to be
grouped according to the levels of the factor
\code{g}. \code{NA}s are allowed in the data.}
\item{data}{a data.frame (or list) from which the variables in
\code{x} should be taken.}
\item{subset}{an optional vector specifying a subset of observations
to be used for plotting.}
\item{na.action}{a function which indicates what should happen
when the data contain \code{NA}s. The default is to ignore missing
values in either the response or the group.}
\item{\dots}{additional parameters passed to the default method, or by
it to \code{plot}, \code{points}, \code{axis} and \code{title} to
control the appearance of the plot.}
\item{method}{the method to be used to separate coincident points.
The default method \code{"overplot"} causes such points to be
overplotted, but it is also possible to specify \code{"jitter"} to
jitter the points, or \code{"stack"} have coincident points
stacked. The last method only makes sense for very granular data.}
\item{jitter}{when \code{method="jitter"} is used, \code{jitter}
gives the amount of jittering applied.}
\item{offset}{when stacking is used, points are stacked this many
line-heights (symbol widths) apart.}
\item{vertical}{when vertical is \code{TRUE} the plots are drawn
vertically rather than the default horizontal.}
\item{group.names}{group labels which will be printed alongside
(or underneath) each plot.}
\item{add}{logical, if true \emph{add} the chart to the current plot.}
\item{at}{numeric vector giving the locations where the charts should
be drawn, particularly when \code{add = TRUE};
defaults to \code{1:n} where \code{n} is the number of boxes.}
\item{ylab, xlab}{labels: see \code{\link{title}}.}
\item{dlab, glab}{alternate way to specify axis labels: see \sQuote{Details}.}
\item{xlim, ylim}{plot limits: see \code{\link{plot.window}}.}
\item{log}{on which axes to use a log scale: see
\code{\link{plot.default}}}
\item{pch, col, cex}{Graphical parameters: see \code{\link{par}}.}
\item{axes, frame.plot}{Axis control: see \code{\link{plot.default}}}
}
\details{
Extensive examples of the use of this kind of plot can be found in
Box, Hunter and Hunter or Seber and Wild.
The \code{dlab} and \code{glab} labels may be used instead of \code{xlab}
and \code{ylab} if those are not specified. \code{dlab} applies to the
continuous data axis (the X axis unless \code{vertical} is \code{TRUE}),
\code{glab} to the group axis.
}
\examples{
x <- stats::rnorm(50)
xr <- round(x, 1)
stripchart(x) ; m <- mean(par("usr")[1:2])
text(m, 1.04, "stripchart(x, \"overplot\")")
stripchart(xr, method = "stack", add = TRUE, at = 1.2)
text(m, 1.35, "stripchart(round(x,1), \"stack\")")
stripchart(xr, method = "jitter", add = TRUE, at = 0.7)
text(m, 0.85, "stripchart(round(x,1), \"jitter\")")
stripchart(decrease ~ treatment,
main = "stripchart(OrchardSprays)",
vertical = TRUE, log = "y", data = OrchardSprays)
stripchart(decrease ~ treatment, at = c(1:8)^2,
main = "stripchart(OrchardSprays)",
vertical = TRUE, log = "y", data = OrchardSprays)
}
\keyword{hplot}
| 4,544 | gpl-2.0 |
2fa55ff00005da1f59ec74a1a7d03b03792a146f | wStockhausen/rsimTCSAM | man/calcStateTransitionMatrices.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/calcStateTransitionMatrices.R
\name{calcStateTransitionMatrices}
\alias{calcStateTransitionMatrices}
\title{Calculate the state transition matrices for a single sex.}
\usage{
calcStateTransitionMatrices(mc, S1_msz, P_sz, Th_sz, T_szz, S2_msz)
}
\arguments{
\item{mc}{- model configuration list object}
\item{S1_msz}{- 3d array of pr(survival) from start of year to mating/molting by maturity state, shell condition, size class}
\item{P_sz}{- 2d array of the probability by size class of molting for immature crab, by shell condition}
\item{Th_sz}{- 2d array with pr(molt to maturity|size, molt) for immature crab by shell condition}
\item{T_szz}{- 3d array with size transition matrix for growth by immature crab by shell condition}
\item{S2_msz}{- 3d array of pr(survival) from mating/molting to end of year by maturity state, shell condition, size class}
}
\value{
list with state transition matrices as named elements:
A : imm, new -> imm, new
B : imm, old -> imm, new
C : imm, new -> imm, old
D : imm, old -> imm, old
E : imm, new -> mat, new
F : imm, old -> mat, new
G : mat, new -> mat, old
H : mat, old -> mat, old
}
\description{
Function to calculate the state transition matrices for a single sex.
}
| 1,302 | mit |
309762b2b015d10cc4012f750ed9fb61372c8f9f | surbut/matrix_ash | man/compute.hm.train.bma.only.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/main.R
\name{compute.hm.train.bma.only}
\alias{compute.hm.train.bma.only}
\title{compute.hm.train.bma.only}
\usage{
compute.hm.train.bma.only(train.b, se.train, covmat, A)
}
| 252 | gpl-2.0 |
17febd4a716342e5c690ba376acbad870043a8b9 | pniessen/Segment_Helper | web/weights 2/man/wtd.chi.sq.Rd | \name{wtd.chi.sq}
\alias{wtd.chi.sq}
\title{
Produces weighted chi-squared tests.
}
\description{
\code{wtd.chi.sq} produces weighted chi-squared tests for two- and three-variable contingency tables. Decomposes parts of three-variable contingency tables as well. Note that weights run with the default parameters here treat the weights as an estimate of the precision of the information. A prior version of this software was set to default to \code{mean1=FALSE}.}
\usage{
wtd.chi.sq(var1, var2, var3=NULL, weight=NULL, na.rm=TRUE,
drop.missing.levels=TRUE, mean1=TRUE)
}
\arguments{
\item{var1}{
\code{var1} is a vector of values which the researcher would like to use to divide any data set.
}
\item{var2}{
\code{var2} is a vector of values which the researcher would like to use to divide any data set.
}
\item{var3}{
\code{var3} is an optional additional vector of values which the researcher would like to use to divide any data set.
}
\item{weight}{
\code{weight} is an optional vector of weights to be used to determine the weighted chi-squared for all analyses.
}
\item{na.rm}{
\code{na.rm} removes missing data from analyses.
}
\item{drop.missing.levels}{
\code{drop.missing.levels} drops missing levels from variables.
}
\item{mean1}{
\code{mean1} is an optional parameter for determining whether the weights should be forced to have an average value of 1. If this is set as false, the weighted correlations will be produced with the assumption that the true N of the data is equivalent to the sum of the weights.
}
}
\value{
A two-way chi-squared produces a vector including a single chi-squared value, degrees of freedom measure, and p-value for each analysis.
A three-way chi-squared produces a matrix with a single chi-squared value, degrees of freedom measure, and p-value for each of seven analyses. These include: (1) the values using a three-way contingency table, (2) the values for a two-way contingency table with each pair of variables, and (3) assessments for whether the relations between each pair of variables are significantly different across levels of the third variable.
}
\author{
Josh Pasek, Assistant Professor of Communication Studies at the University of Michigan (www.joshpasek.com).
}
\seealso{
\code{\link{wtd.cor}}
\code{\link{wtd.t.test}}
}
\examples{
var1 <- c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3)
var2 <- c(1,1,2,2,3,3,1,1,2,2,3,3,1,1,2)
var3 <- c(1,2,3,1,2,3,1,2,3,1,2,3,1,2,3)
weight <- c(.5,.5,.5,.5,.5,1,1,1,1,1,2,2,2,2,2)
wtd.chi.sq(var1, var2)
wtd.chi.sq(var1, var2, weight=weight)
wtd.chi.sq(var1, var2, var3)
wtd.chi.sq(var1, var2, var3, weight=weight)
}
\keyword{ ~contingency tables }
\keyword{ ~chisquared }
\keyword{ ~decompose}
| 2,695 | mit |
83e4b5c56b3dc656d999529e3675e3edd898d61f | dieterich-lab/JACUSA | JacusaHelper/man/JacusaHelper.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/common.R
\docType{package}
\name{JacusaHelper}
\alias{JacusaHelper}
\alias{JacusaHelper-package}
\title{JacusaHelper: A package for post-processing JACUSA result files.}
\description{
The JacusaHelper package provides three categories of important functions:
InputOutput, AddInfo, and Filter.
}
\details{
When calling RDDs where the RNA-Seq sample has been generated with stranded sequencing library,
base change(s) can be directly inferred and if necessary base calls can be inverted.
Per default DNA need to be provided as sample 1 and cDNA as sample 2!
Warning: Some function do not support replicates or are exclusively applicable on RDD or RRD
result files!
use l <- Read(l) to read the data
and then sample <- Samples(l, 1) to extract the sample specific data
then continue with ToMatrix(sample)
}
\section{InputOutput functions}{
The functions Read, and Write facilitate input and output operations on JACUSA output files.
See:
\itemize{
\item Read
\item Write
}
}
\section{AddInfo functions}{
This functions calculate and add additional information such as read depth or base changes.
This includes functions convert base counts that are encoded as character vectors to base count
matrices.
See:
\itemize{
\item AddCoverageInfo
\item AddBaseInfo
\item AddBaseChangeInfo
\item AddEditingFreqInfo
}
}
\section{Filter functions}{
This function set enables processing of JACUSA output files such as filtering by read coverage
or enforcing a minimal number of variant base calls per sample.
See:
\itemize{
\item FilterByCoverage
\item FilterByStat
\item FilterResult
\item FilterByMinVariantCount
}
}
| 1,716 | gpl-3.0 |
1cc41351ff9ad36382c8b7b0e92d3a3594701ce5 | nickmckay/LiPD-utilities | R/deprecated/man/merge_csv_table.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/jsons_merge.R
\name{merge_csv_table}
\alias{merge_csv_table}
\title{Merge CSV data into each table}
\usage{
merge_csv_table(tables, crumbs, csvs)
}
\arguments{
\item{char}{crumbs: Crumbs}
\item{list}{csvs: CSV data}
}
\value{
list models: Metadata
}
\description{
Merge CSV data into each table
}
\keyword{internal}
| 395 | gpl-2.0 |
4d9c7be2cb1bd59c67572b7233b375fe28243b38 | mm0hgw/ballot | ballot/man/regionTag.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/regionTag.R
\name{regionTag}
\alias{regionTag}
\title{regionTag}
\usage{
regionTag(x, rTitle = NULL, rLayerTag = NULL, rMask = NULL)
}
\arguments{
\item{x}{a 'regionTag' or 'character'}
\item{rTitle}{a 'character' title}
\item{rLayerTag}{a 'layerTag' key}
\item{rMask}{a 'vector' defining the subset of the layerTag layer that forms the region.}
}
\description{
regionTag
}
| 455 | gpl-2.0 |
17febd4a716342e5c690ba376acbad870043a8b9 | pniessen/Segment_Helper | web/weights/man/wtd.chi.sq.Rd | \name{wtd.chi.sq}
\alias{wtd.chi.sq}
\title{
Produces weighted chi-squared tests.
}
\description{
\code{wtd.chi.sq} produces weighted chi-squared tests for two- and three-variable contingency tables. Decomposes parts of three-variable contingency tables as well. Note that weights run with the default parameters here treat the weights as an estimate of the precision of the information. A prior version of this software was set to default to \code{mean1=FALSE}.}
\usage{
wtd.chi.sq(var1, var2, var3=NULL, weight=NULL, na.rm=TRUE,
drop.missing.levels=TRUE, mean1=TRUE)
}
\arguments{
\item{var1}{
\code{var1} is a vector of values which the researcher would like to use to divide any data set.
}
\item{var2}{
\code{var2} is a vector of values which the researcher would like to use to divide any data set.
}
\item{var3}{
\code{var3} is an optional additional vector of values which the researcher would like to use to divide any data set.
}
\item{weight}{
\code{weight} is an optional vector of weights to be used to determine the weighted chi-squared for all analyses.
}
\item{na.rm}{
\code{na.rm} removes missing data from analyses.
}
\item{drop.missing.levels}{
\code{drop.missing.levels} drops missing levels from variables.
}
\item{mean1}{
\code{mean1} is an optional parameter for determining whether the weights should be forced to have an average value of 1. If this is set as false, the weighted correlations will be produced with the assumption that the true N of the data is equivalent to the sum of the weights.
}
}
\value{
A two-way chi-squared produces a vector including a single chi-squared value, degrees of freedom measure, and p-value for each analysis.
A three-way chi-squared produces a matrix with a single chi-squared value, degrees of freedom measure, and p-value for each of seven analyses. These include: (1) the values using a three-way contingency table, (2) the values for a two-way contingency table with each pair of variables, and (3) assessments for whether the relations between each pair of variables are significantly different across levels of the third variable.
}
\author{
Josh Pasek, Assistant Professor of Communication Studies at the University of Michigan (www.joshpasek.com).
}
\seealso{
\code{\link{wtd.cor}}
\code{\link{wtd.t.test}}
}
\examples{
var1 <- c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3)
var2 <- c(1,1,2,2,3,3,1,1,2,2,3,3,1,1,2)
var3 <- c(1,2,3,1,2,3,1,2,3,1,2,3,1,2,3)
weight <- c(.5,.5,.5,.5,.5,1,1,1,1,1,2,2,2,2,2)
wtd.chi.sq(var1, var2)
wtd.chi.sq(var1, var2, weight=weight)
wtd.chi.sq(var1, var2, var3)
wtd.chi.sq(var1, var2, var3, weight=weight)
}
\keyword{ ~contingency tables }
\keyword{ ~chisquared }
\keyword{ ~decompose}
| 2,695 | mit |
d4f8748aa1f35418d0585b7da26e3adadc7acdb4 | KWB-R/kwb.wtaq | man/modelFitnessAggregated.Rd | null | 326 | mit |
daa1d16619773985ee0ab58a2f22fdb9cef47912 | cran/Zelig | man/Zelig-ls-class.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/model-ls.R
\docType{class}
\name{Zelig-ls-class}
\alias{Zelig-ls-class}
\alias{zls}
\title{Least Squares Regression for Continuous Dependent Variables}
\arguments{
\item{formula}{a symbolic representation of the model to be
estimated, in the form \code{y ~ x1 + x2}, where \code{y} is the
dependent variable and \code{x1} and \code{x2} are the explanatory
variables, and \code{y}, \code{x1}, and \code{x2} are contained in the
same dataset. (You may include more than two explanatory variables,
of course.) The \code{+} symbol means ``inclusion'' not
``addition.'' You may also include interaction terms and main
effects in the form \code{x1*x2} without computing them in prior
steps; \code{I(x1*x2)} to include only the interaction term and
exclude the main effects; and quadratic terms in the form
\code{I(x1^2)}.}
\item{model}{the name of a statistical model to estimate.
For a list of other supported models and their documentation see:
\url{http://docs.zeligproject.org/articles/}.}
\item{data}{the name of a data frame containing the variables
referenced in the formula or a list of multiply imputed data frames
each having the same variable names and row numbers (created by
\code{Amelia} or \code{\link{to_zelig_mi}}).}
\item{...}{additional arguments passed to \code{zelig},
relevant for the model to be estimated.}
\item{by}{a factor variable contained in \code{data}. If supplied,
\code{zelig} will subset
the data frame based on the levels in the \code{by} variable, and
estimate a model for each subset. This can save a considerable amount of
effort. You may also use \code{by} to run models using MatchIt
subclasses.}
\item{cite}{If is set to 'TRUE' (default), the model citation will be printed
to the console.}
}
\value{
Depending on the class of model selected, \code{zelig} will return
an object with elements including \code{coefficients}, \code{residuals},
and \code{formula} which may be summarized using
\code{summary(z.out)} or individually extracted using, for example,
\code{coef(z.out)}. See
\url{http://docs.zeligproject.org/articles/getters.html} for a list of
functions to extract model components. You can also extract whole fitted
model objects using \code{\link{from_zelig_model}}.
}
\description{
Least Squares Regression for Continuous Dependent Variables
}
\details{
Additional parameters avaialable to this model include:
\itemize{
\item \code{weights}: vector of weight values or a name of a variable in the dataset
by which to weight the model. For more information see:
\url{http://docs.zeligproject.org/articles/weights.html}.
\item \code{bootstrap}: logical or numeric. If \code{FALSE} don't use bootstraps to
robustly estimate uncertainty around model parameters due to sampling error.
If an integer is supplied, the number of boostraps to run.
For more information see:
\url{http://docs.zeligproject.org/articles/bootstraps.html}.
}
}
\section{Methods}{
\describe{
\item{\code{zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)}}{The zelig function estimates a variety of statistical models}
}}
\examples{
library(Zelig)
data(macro)
z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "ls", data = macro,
cite = FALSE)
summary(z.out1)
}
\seealso{
Vignette: \url{http://docs.zeligproject.org/articles/zelig_ls.html}
}
| 3,405 | gpl-2.0 |
329202135377df6370c2896e745957da5ef045d1 | amsantac/TOC | man/uncertainty.Rd | \name{uncertainty}
\alias{uncertainty}
\title{
Uncertainty in AUC calculation
}
\description{
TOC internal function. It calculates uncertainty in AUC calculation
}
\usage{
uncertainty(index, tocd)
}
\arguments{
\item{index}{
index vector
}
\item{tocd}{
data.frame output from \code{roctable}
}
}
\note{
This function is not meant to be called by users directly
}
\value{
a numeric value representing uncertainty in AUC calculation
}
\keyword{ spatial }
| 490 | gpl-2.0 |
567b9316ffc57b4e9a5e8ced39120505bc434e3d | davidnipperess/PDcalc | man/phylocurve.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/phylocurve.R
\name{phylocurve}
\alias{phylocurve}
\title{Generate a rarefaction curve of Phylogenetic Diversity}
\usage{
phylocurve(x, phy, stepm = 1, subsampling = "individual", replace = FALSE)
}
\arguments{
\item{x}{is the community data given as a \code{data.frame} or \code{matrix}
with species/OTUs as columns and samples/sites as rows (like in the
\code{vegan} package). Columns are labelled with the names of the
species/OTUs. Rows are labelled with the names of the samples/sites. Data
can be either abundance or incidence (0/1). Column labels must match tip
labels in the phylogenetic tree exactly!}
\item{phy}{is a rooted phylogenetic tree with branch lengths stored as a
phylo object (as in the \code{ape} package) with terminal nodes labelled
with names matching those of the community data table. Note that the
function trims away any terminal taxa not present in the community data
table, so it is not necessary to do this beforehand.}
\item{stepm}{is the size of the interval in a sequence of numbers of
individuals, sites or species to which \code{x} is to be rarefied.}
\item{subsampling}{indicates whether the subsampling will be by
\code{'individual'} (default), \code{'site'} or \code{'species'}. When
there are multiple sites, rarefaction by individuals or species is done by
first pooling the sites.}
\item{replace}{is a \code{logical} indicating whether subsampling should be done
with (\code{TRUE}) or without (\code{FALSE} - default) replacement.}
}
\value{
a \code{matrix} object of three columns giving the expected PD values
(mean and variance) for each value of \code{m}
}
\description{
Calculates a rarefaction curve giving expected phylogenetic diversity (mean
and variance) for multiple values of sampling effort. Sampling effort can be
defined in terms of the number of individuals, sites or species. Expected
phylogenetic diversity is calculated using an exact analytical formulation
(Nipperess & Matsen 2013) that is both more accurate and more computationally
efficient than randomisation methods.
}
\details{
\code{phylocurve} takes community data and a rooted phylogenetic
tree (with branch lengths) and calculates expected mean and variance of
Phylogenetic Diversity (PD) for every specified value of \code{m}
individuals, sites or species. \code{m} will range from 1 to the total
number of individuals/sites/species in increments given by \code{stepm}.
Calculations are done using the exact analytical formulae (Nipperess &
Matsen, 2013) generalised from the classic equation of Hurlbert (1971).
When there are multiple sites in the community data and rarefaction is by
individuals or species, sites are first pooled.
}
\references{
\itemize{ \item{Hurlbert (1971) The nonconcept of Species
Diversity: a critique and alternative parameters. \emph{Ecology} 52:
577-586.} \item{Nipperess & Matsen (2013) The mean and variance of
phylogenetic diversity under rarefaction. \emph{Methods in Ecology &
Evolution} 4: 566-572.}}
}
| 3,091 | gpl-3.0 |
94070b867e20f6a304d8809171526be90c19e34c | RPGOne/Skynet | xgboost-master/R-package/man/callbacks.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/callbacks.R
\name{callbacks}
\alias{callbacks}
\title{Callback closures for booster training.}
\description{
These are used to perform various service tasks either during boosting iterations or at the end.
This approach helps to modularize many of such tasks without bloating the main training methods,
and it offers .
}
\details{
By default, a callback function is run after each boosting iteration.
An R-attribute \code{is_pre_iteration} could be set for a callback to define a pre-iteration function.
When a callback function has \code{finalize} parameter, its finalizer part will also be run after
the boosting is completed.
WARNING: side-effects!!! Be aware that these callback functions access and modify things in
the environment from which they are called from, which is a fairly uncommon thing to do in R.
To write a custom callback closure, make sure you first understand the main concepts about R envoronments.
Check either R documentation on \code{\link[base]{environment}} or the
\href{http://adv-r.had.co.nz/Environments.html}{Environments chapter} from the "Advanced R"
book by Hadley Wickham. Further, the best option is to read the code of some of the existing callbacks -
choose ones that do something similar to what you want to achieve. Also, you would need to get familiar
with the objects available inside of the \code{xgb.train} and \code{xgb.cv} internal environments.
}
\seealso{
\code{\link{cb.print.evaluation}},
\code{\link{cb.evaluation.log}},
\code{\link{cb.reset.parameters}},
\code{\link{cb.early.stop}},
\code{\link{cb.save.model}},
\code{\link{cb.cv.predict}},
\code{\link{xgb.train}},
\code{\link{xgb.cv}}
}
| 1,730 | bsd-3-clause |
0fd1d191ed6156670544d76c2ff972ac86a50c50 | glycerine/bigbird | r-3.0.2/src/library/grid/man/grid.layout.Rd | % File src/library/grid/man/grid.layout.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{grid.layout}
\alias{grid.layout}
\title{Create a Grid Layout}
\description{
This function returns a Grid layout, which describes a subdivision
of a rectangular region.
}
\usage{
grid.layout(nrow = 1, ncol = 1,
widths = unit(rep(1, ncol), "null"),
heights = unit(rep(1, nrow), "null"),
default.units = "null", respect = FALSE,
just="centre")
}
\arguments{
\item{nrow}{An integer describing the number of rows in the layout.}
\item{ncol}{An integer describing the number of columns in the layout.}
\item{widths}{A numeric vector or unit object
describing the widths of the columns
in the layout.}
\item{heights}{A numeric vector or unit object
describing the heights of the rows
in the layout.}
\item{default.units}{A string indicating the default units to use
if \code{widths} or \code{heights} are only given as numeric vectors.}
\item{respect}{A logical value or a numeric matrix.
If a logical, this indicates whether
row heights and column widths should respect each other.
If a matrix, non-zero values indicate that the corresponding
row and column should be respected (see examples below).
}
\item{just}{A string or numeric
vector specifying how the
layout should be
justified if it is not the same size as its parent viewport.
If there are two values, the first
value specifies horizontal justification and the second value specifies
vertical justification. Possible string values are: \code{"left"},
\code{"right"}, \code{"centre"}, \code{"center"}, \code{"bottom"},
and \code{"top"}. For numeric values, 0 means left alignment
and 1 means right alignment. NOTE that in this context,
\code{"left"}, for example, means align the left
edge of the left-most layout column with the left edge of the
parent viewport.}
}
\details{
The unit objects given for the \code{widths} and \code{heights}
of a layout may use a special \code{units} that only has
meaning for layouts. This is the \code{"null"} unit, which
indicates what relative fraction of the available width/height the
column/row occupies. See the reference for a better description
of relative widths and heights in layouts.
}
\section{WARNING}{
This function must NOT be confused with the base R graphics function
\code{layout}. In particular, do not use \code{layout} in
combination with Grid graphics. The documentation for
\code{layout} may provide some useful information and this
function should behave identically in comparable situations. The
\code{grid.layout}
function has \emph{added} the ability to specify a broader range
of units for row heights and column widths, and allows for nested
layouts (see \code{viewport}).
}
\value{
A Grid layout object.
}
\references{Murrell, P. R. (1999), Layouts: A Mechanism for Arranging
Plots on a Page, \emph{Journal of Computational and Graphical
Statistics}, \bold{8}, 121--134.}
\author{Paul Murrell}
\seealso{
\link{Grid},
\code{\link{grid.show.layout}},
\code{\link{viewport}},
\code{\link{layout}}}
\examples{
## A variety of layouts (some a bit mid-bending ...)
layout.torture()
## Demonstration of layout justification
grid.newpage()
testlay <- function(just="centre") {
pushViewport(viewport(layout=grid.layout(1, 1, widths=unit(1, "inches"),
heights=unit(0.25, "npc"),
just=just)))
pushViewport(viewport(layout.pos.col=1, layout.pos.row=1))
grid.rect()
grid.text(paste(just, collapse="-"))
popViewport(2)
}
testlay()
testlay(c("left", "top"))
testlay(c("right", "top"))
testlay(c("right", "bottom"))
testlay(c("left", "bottom"))
testlay(c("left"))
testlay(c("right"))
testlay(c("bottom"))
testlay(c("top"))
}
\keyword{dplot}
| 3,964 | bsd-2-clause |
0fd1d191ed6156670544d76c2ff972ac86a50c50 | lajus/customr | src/library/grid/man/grid.layout.Rd | % File src/library/grid/man/grid.layout.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{grid.layout}
\alias{grid.layout}
\title{Create a Grid Layout}
\description{
This function returns a Grid layout, which describes a subdivision
of a rectangular region.
}
\usage{
grid.layout(nrow = 1, ncol = 1,
widths = unit(rep(1, ncol), "null"),
heights = unit(rep(1, nrow), "null"),
default.units = "null", respect = FALSE,
just="centre")
}
\arguments{
\item{nrow}{An integer describing the number of rows in the layout.}
\item{ncol}{An integer describing the number of columns in the layout.}
\item{widths}{A numeric vector or unit object
describing the widths of the columns
in the layout.}
\item{heights}{A numeric vector or unit object
describing the heights of the rows
in the layout.}
\item{default.units}{A string indicating the default units to use
if \code{widths} or \code{heights} are only given as numeric vectors.}
\item{respect}{A logical value or a numeric matrix.
If a logical, this indicates whether
row heights and column widths should respect each other.
If a matrix, non-zero values indicate that the corresponding
row and column should be respected (see examples below).
}
\item{just}{A string or numeric
vector specifying how the
layout should be
justified if it is not the same size as its parent viewport.
If there are two values, the first
value specifies horizontal justification and the second value specifies
vertical justification. Possible string values are: \code{"left"},
\code{"right"}, \code{"centre"}, \code{"center"}, \code{"bottom"},
and \code{"top"}. For numeric values, 0 means left alignment
and 1 means right alignment. NOTE that in this context,
\code{"left"}, for example, means align the left
edge of the left-most layout column with the left edge of the
parent viewport.}
}
\details{
The unit objects given for the \code{widths} and \code{heights}
of a layout may use a special \code{units} that only has
meaning for layouts. This is the \code{"null"} unit, which
indicates what relative fraction of the available width/height the
column/row occupies. See the reference for a better description
of relative widths and heights in layouts.
}
\section{WARNING}{
This function must NOT be confused with the base R graphics function
\code{layout}. In particular, do not use \code{layout} in
combination with Grid graphics. The documentation for
\code{layout} may provide some useful information and this
function should behave identically in comparable situations. The
\code{grid.layout}
function has \emph{added} the ability to specify a broader range
of units for row heights and column widths, and allows for nested
layouts (see \code{viewport}).
}
\value{
A Grid layout object.
}
\references{Murrell, P. R. (1999), Layouts: A Mechanism for Arranging
Plots on a Page, \emph{Journal of Computational and Graphical
Statistics}, \bold{8}, 121--134.}
\author{Paul Murrell}
\seealso{
\link{Grid},
\code{\link{grid.show.layout}},
\code{\link{viewport}},
\code{\link{layout}}}
\examples{
## A variety of layouts (some a bit mid-bending ...)
layout.torture()
## Demonstration of layout justification
grid.newpage()
testlay <- function(just="centre") {
pushViewport(viewport(layout=grid.layout(1, 1, widths=unit(1, "inches"),
heights=unit(0.25, "npc"),
just=just)))
pushViewport(viewport(layout.pos.col=1, layout.pos.row=1))
grid.rect()
grid.text(paste(just, collapse="-"))
popViewport(2)
}
testlay()
testlay(c("left", "top"))
testlay(c("right", "top"))
testlay(c("right", "bottom"))
testlay(c("left", "bottom"))
testlay(c("left"))
testlay(c("right"))
testlay(c("bottom"))
testlay(c("top"))
}
\keyword{dplot}
| 3,964 | gpl-2.0 |
374949dc1d3e3e7944c56ae5491da5ce60d74ff6 | o-/Rexperiments | src/library/stats/man/convolve.Rd | % File src/library/stats/man/convolve.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{convolve}
\alias{convolve}
\title{Convolution of Sequences via FFT}
\description{
Use the Fast Fourier Transform to compute the several kinds of
convolutions of two sequences.
}
\usage{
convolve(x, y, conj = TRUE, type = c("circular", "open", "filter"))
}
\arguments{
\item{x, y}{numeric sequences \emph{of the same length} to be
convolved.}
\item{conj}{logical; if \code{TRUE}, take the complex \emph{conjugate}
before back-transforming (default, and used for usual convolution).}
\item{type}{character; one of \code{"circular"}, \code{"open"},
\code{"filter"} (beginning of word is ok). For \code{circular}, the
two sequences are treated as \emph{circular}, i.e., periodic.
For \code{open} and \code{filter}, the sequences are padded with
\code{0}s (from left and right) first; \code{"filter"} returns the
middle sub-vector of \code{"open"}, namely, the result of running a
weighted mean of \code{x} with weights \code{y}.}
}
\details{
The Fast Fourier Transform, \code{\link{fft}}, is used for efficiency.
The input sequences \code{x} and \code{y} must have the same length if
\code{circular} is true.
Note that the usual definition of convolution of two sequences
\code{x} and \code{y} is given by \code{convolve(x, rev(y), type = "o")}.
}
\value{
If \code{r <- convolve(x, y, type = "open")}
and \code{n <- length(x)}, \code{m <- length(y)}, then
\deqn{r_k = \sum_{i} x_{k-m+i} y_{i}}{r[k] = sum(i; x[k-m+i] * y[i])}
where the sum is over all valid indices \eqn{i}, for
\eqn{k = 1, \dots, n+m-1}.
If \code{type == "circular"}, \eqn{n = m} is required, and the above is
true for \eqn{i , k = 1,\dots,n} when
\eqn{x_{j} := x_{n+j}}{x[j] := x[n+j]} for \eqn{j < 1}.
}
\references{
Brillinger, D. R. (1981)
\emph{Time Series: Data Analysis and Theory}, Second Edition.
San Francisco: Holden-Day.
}
\seealso{\code{\link{fft}}, \code{\link{nextn}}, and particularly
\code{\link{filter}} (from the \pkg{stats} package) which may be
more appropriate.
}
\examples{
require(graphics)
x <- c(0,0,0,100,0,0,0)
y <- c(0,0,1, 2 ,1,0,0)/4
zapsmall(convolve(x, y)) # *NOT* what you first thought.
zapsmall(convolve(x, y[3:5], type = "f")) # rather
x <- rnorm(50)
y <- rnorm(50)
# Circular convolution *has* this symmetry:
all.equal(convolve(x, y, conj = FALSE), rev(convolve(rev(y),x)))
n <- length(x <- -20:24)
y <- (x-10)^2/1000 + rnorm(x)/8
Han <- function(y) # Hanning
convolve(y, c(1,2,1)/4, type = "filter")
plot(x, y, main = "Using convolve(.) for Hanning filters")
lines(x[-c(1 , n) ], Han(y), col = "red")
lines(x[-c(1:2, (n-1):n)], Han(Han(y)), lwd = 2, col = "dark blue")
}
\keyword{math}
\keyword{dplot}
| 2,883 | gpl-2.0 |
374949dc1d3e3e7944c56ae5491da5ce60d74ff6 | kalibera/rexp | src/library/stats/man/convolve.Rd | % File src/library/stats/man/convolve.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{convolve}
\alias{convolve}
\title{Convolution of Sequences via FFT}
\description{
Use the Fast Fourier Transform to compute the several kinds of
convolutions of two sequences.
}
\usage{
convolve(x, y, conj = TRUE, type = c("circular", "open", "filter"))
}
\arguments{
\item{x, y}{numeric sequences \emph{of the same length} to be
convolved.}
\item{conj}{logical; if \code{TRUE}, take the complex \emph{conjugate}
before back-transforming (default, and used for usual convolution).}
\item{type}{character; one of \code{"circular"}, \code{"open"},
\code{"filter"} (beginning of word is ok). For \code{circular}, the
two sequences are treated as \emph{circular}, i.e., periodic.
For \code{open} and \code{filter}, the sequences are padded with
\code{0}s (from left and right) first; \code{"filter"} returns the
middle sub-vector of \code{"open"}, namely, the result of running a
weighted mean of \code{x} with weights \code{y}.}
}
\details{
The Fast Fourier Transform, \code{\link{fft}}, is used for efficiency.
The input sequences \code{x} and \code{y} must have the same length if
\code{circular} is true.
Note that the usual definition of convolution of two sequences
\code{x} and \code{y} is given by \code{convolve(x, rev(y), type = "o")}.
}
\value{
If \code{r <- convolve(x, y, type = "open")}
and \code{n <- length(x)}, \code{m <- length(y)}, then
\deqn{r_k = \sum_{i} x_{k-m+i} y_{i}}{r[k] = sum(i; x[k-m+i] * y[i])}
where the sum is over all valid indices \eqn{i}, for
\eqn{k = 1, \dots, n+m-1}.
If \code{type == "circular"}, \eqn{n = m} is required, and the above is
true for \eqn{i , k = 1,\dots,n} when
\eqn{x_{j} := x_{n+j}}{x[j] := x[n+j]} for \eqn{j < 1}.
}
\references{
Brillinger, D. R. (1981)
\emph{Time Series: Data Analysis and Theory}, Second Edition.
San Francisco: Holden-Day.
}
\seealso{\code{\link{fft}}, \code{\link{nextn}}, and particularly
\code{\link{filter}} (from the \pkg{stats} package) which may be
more appropriate.
}
\examples{
require(graphics)
x <- c(0,0,0,100,0,0,0)
y <- c(0,0,1, 2 ,1,0,0)/4
zapsmall(convolve(x, y)) # *NOT* what you first thought.
zapsmall(convolve(x, y[3:5], type = "f")) # rather
x <- rnorm(50)
y <- rnorm(50)
# Circular convolution *has* this symmetry:
all.equal(convolve(x, y, conj = FALSE), rev(convolve(rev(y),x)))
n <- length(x <- -20:24)
y <- (x-10)^2/1000 + rnorm(x)/8
Han <- function(y) # Hanning
convolve(y, c(1,2,1)/4, type = "filter")
plot(x, y, main = "Using convolve(.) for Hanning filters")
lines(x[-c(1 , n) ], Han(y), col = "red")
lines(x[-c(1:2, (n-1):n)], Han(Han(y)), lwd = 2, col = "dark blue")
}
\keyword{math}
\keyword{dplot}
| 2,883 | gpl-2.0 |
0fd1d191ed6156670544d76c2ff972ac86a50c50 | cxxr-devel/cxxr-svn-mirror | src/library/grid/man/grid.layout.Rd | % File src/library/grid/man/grid.layout.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{grid.layout}
\alias{grid.layout}
\title{Create a Grid Layout}
\description{
This function returns a Grid layout, which describes a subdivision
of a rectangular region.
}
\usage{
grid.layout(nrow = 1, ncol = 1,
widths = unit(rep(1, ncol), "null"),
heights = unit(rep(1, nrow), "null"),
default.units = "null", respect = FALSE,
just="centre")
}
\arguments{
\item{nrow}{An integer describing the number of rows in the layout.}
\item{ncol}{An integer describing the number of columns in the layout.}
\item{widths}{A numeric vector or unit object
describing the widths of the columns
in the layout.}
\item{heights}{A numeric vector or unit object
describing the heights of the rows
in the layout.}
\item{default.units}{A string indicating the default units to use
if \code{widths} or \code{heights} are only given as numeric vectors.}
\item{respect}{A logical value or a numeric matrix.
If a logical, this indicates whether
row heights and column widths should respect each other.
If a matrix, non-zero values indicate that the corresponding
row and column should be respected (see examples below).
}
\item{just}{A string or numeric
vector specifying how the
layout should be
justified if it is not the same size as its parent viewport.
If there are two values, the first
value specifies horizontal justification and the second value specifies
vertical justification. Possible string values are: \code{"left"},
\code{"right"}, \code{"centre"}, \code{"center"}, \code{"bottom"},
and \code{"top"}. For numeric values, 0 means left alignment
and 1 means right alignment. NOTE that in this context,
\code{"left"}, for example, means align the left
edge of the left-most layout column with the left edge of the
parent viewport.}
}
\details{
The unit objects given for the \code{widths} and \code{heights}
of a layout may use a special \code{units} that only has
meaning for layouts. This is the \code{"null"} unit, which
indicates what relative fraction of the available width/height the
column/row occupies. See the reference for a better description
of relative widths and heights in layouts.
}
\section{WARNING}{
This function must NOT be confused with the base R graphics function
\code{layout}. In particular, do not use \code{layout} in
combination with Grid graphics. The documentation for
\code{layout} may provide some useful information and this
function should behave identically in comparable situations. The
\code{grid.layout}
function has \emph{added} the ability to specify a broader range
of units for row heights and column widths, and allows for nested
layouts (see \code{viewport}).
}
\value{
A Grid layout object.
}
\references{Murrell, P. R. (1999), Layouts: A Mechanism for Arranging
Plots on a Page, \emph{Journal of Computational and Graphical
Statistics}, \bold{8}, 121--134.}
\author{Paul Murrell}
\seealso{
\link{Grid},
\code{\link{grid.show.layout}},
\code{\link{viewport}},
\code{\link{layout}}}
\examples{
## A variety of layouts (some a bit mid-bending ...)
layout.torture()
## Demonstration of layout justification
grid.newpage()
testlay <- function(just="centre") {
pushViewport(viewport(layout=grid.layout(1, 1, widths=unit(1, "inches"),
heights=unit(0.25, "npc"),
just=just)))
pushViewport(viewport(layout.pos.col=1, layout.pos.row=1))
grid.rect()
grid.text(paste(just, collapse="-"))
popViewport(2)
}
testlay()
testlay(c("left", "top"))
testlay(c("right", "top"))
testlay(c("right", "bottom"))
testlay(c("left", "bottom"))
testlay(c("left"))
testlay(c("right"))
testlay(c("bottom"))
testlay(c("top"))
}
\keyword{dplot}
| 3,964 | gpl-2.0 |
6752987357c52e975d47ddbf33d37e3a6c6c2825 | datadotworld/dwapi-r | man/check_user_info_response.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/user_info_response.R
\name{check_user_info_response}
\alias{check_user_info_response}
\title{Validate \code{get_user} response object, returning the object if valid,
and stopping with an error message if invalid.}
\usage{
check_user_info_response(object)
}
\arguments{
\item{object}{Object of type \code{\link{user_info_response}}.}
}
\value{
the object
}
\description{
Validate \code{get_user} response object, returning the object if valid,
and stopping with an error message if invalid.
}
| 570 | apache-2.0 |
374949dc1d3e3e7944c56ae5491da5ce60d74ff6 | lajus/customr | src/library/stats/man/convolve.Rd | % File src/library/stats/man/convolve.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{convolve}
\alias{convolve}
\title{Convolution of Sequences via FFT}
\description{
Use the Fast Fourier Transform to compute the several kinds of
convolutions of two sequences.
}
\usage{
convolve(x, y, conj = TRUE, type = c("circular", "open", "filter"))
}
\arguments{
\item{x, y}{numeric sequences \emph{of the same length} to be
convolved.}
\item{conj}{logical; if \code{TRUE}, take the complex \emph{conjugate}
before back-transforming (default, and used for usual convolution).}
\item{type}{character; one of \code{"circular"}, \code{"open"},
\code{"filter"} (beginning of word is ok). For \code{circular}, the
two sequences are treated as \emph{circular}, i.e., periodic.
For \code{open} and \code{filter}, the sequences are padded with
\code{0}s (from left and right) first; \code{"filter"} returns the
middle sub-vector of \code{"open"}, namely, the result of running a
weighted mean of \code{x} with weights \code{y}.}
}
\details{
The Fast Fourier Transform, \code{\link{fft}}, is used for efficiency.
The input sequences \code{x} and \code{y} must have the same length if
\code{circular} is true.
Note that the usual definition of convolution of two sequences
\code{x} and \code{y} is given by \code{convolve(x, rev(y), type = "o")}.
}
\value{
If \code{r <- convolve(x, y, type = "open")}
and \code{n <- length(x)}, \code{m <- length(y)}, then
\deqn{r_k = \sum_{i} x_{k-m+i} y_{i}}{r[k] = sum(i; x[k-m+i] * y[i])}
where the sum is over all valid indices \eqn{i}, for
\eqn{k = 1, \dots, n+m-1}.
If \code{type == "circular"}, \eqn{n = m} is required, and the above is
true for \eqn{i , k = 1,\dots,n} when
\eqn{x_{j} := x_{n+j}}{x[j] := x[n+j]} for \eqn{j < 1}.
}
\references{
Brillinger, D. R. (1981)
\emph{Time Series: Data Analysis and Theory}, Second Edition.
San Francisco: Holden-Day.
}
\seealso{\code{\link{fft}}, \code{\link{nextn}}, and particularly
\code{\link{filter}} (from the \pkg{stats} package) which may be
more appropriate.
}
\examples{
require(graphics)
x <- c(0,0,0,100,0,0,0)
y <- c(0,0,1, 2 ,1,0,0)/4
zapsmall(convolve(x, y)) # *NOT* what you first thought.
zapsmall(convolve(x, y[3:5], type = "f")) # rather
x <- rnorm(50)
y <- rnorm(50)
# Circular convolution *has* this symmetry:
all.equal(convolve(x, y, conj = FALSE), rev(convolve(rev(y),x)))
n <- length(x <- -20:24)
y <- (x-10)^2/1000 + rnorm(x)/8
Han <- function(y) # Hanning
convolve(y, c(1,2,1)/4, type = "filter")
plot(x, y, main = "Using convolve(.) for Hanning filters")
lines(x[-c(1 , n) ], Han(y), col = "red")
lines(x[-c(1:2, (n-1):n)], Han(Han(y)), lwd = 2, col = "dark blue")
}
\keyword{math}
\keyword{dplot}
| 2,883 | gpl-2.0 |
dfbb26fcc029dfc48a60e85f60952e32a45ba283 | jeroenooms/r-source | src/library/utils/man/shortPathName.Rd | % File src/library/utils/man/shortPathName.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2018 R Core Team
% Distributed under GPL 2 or later
\name{shortPathName}
\alias{shortPathName}
\title{Express File Paths in Short Form on Windows}
\description{
Convert file paths to the short form. This is an interface to the
Windows API call \code{GetShortPathNameW}.
}
\usage{
shortPathName(path)
}
\arguments{
\item{path}{character vector of file paths.}
}
% http://msdn.microsoft.com/en-gb/library/windows/desktop/aa364989%28v=vs.85%29.aspx
\details{
For most file systems, the short form is the \sQuote{DOS} form with
8+3 path components and no spaces, and this used to be guaranteed.
But some file systems on recent versions of Windows do not have short
path names when the long-name path will be returned instead.
}
\value{
A character vector. The path separator will be \code{\\}. If a
file path does not exist, the supplied path will be returned with slashes
replaced by backslashes.
}
\note{
This is only available on Windows.
}
\seealso{
\code{\link{normalizePath}}.
}
\examples{% (spacing: for nice rendering of visual part of example)
if(.Platform$OS.type == "windows") withAutoprint({
\donttest{ cat(shortPathName(c(R.home(), tempdir())), sep = "\n")}
\dontshow{ cat(shortPathName(R.home()), sep = "\n")}
})
}
\keyword{ utilities }
| 1,392 | gpl-2.0 |
374949dc1d3e3e7944c56ae5491da5ce60d74ff6 | jeffreyhorner/R-Array-Hash | src/library/stats/man/convolve.Rd | % File src/library/stats/man/convolve.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{convolve}
\alias{convolve}
\title{Convolution of Sequences via FFT}
\description{
Use the Fast Fourier Transform to compute the several kinds of
convolutions of two sequences.
}
\usage{
convolve(x, y, conj = TRUE, type = c("circular", "open", "filter"))
}
\arguments{
\item{x, y}{numeric sequences \emph{of the same length} to be
convolved.}
\item{conj}{logical; if \code{TRUE}, take the complex \emph{conjugate}
before back-transforming (default, and used for usual convolution).}
\item{type}{character; one of \code{"circular"}, \code{"open"},
\code{"filter"} (beginning of word is ok). For \code{circular}, the
two sequences are treated as \emph{circular}, i.e., periodic.
For \code{open} and \code{filter}, the sequences are padded with
\code{0}s (from left and right) first; \code{"filter"} returns the
middle sub-vector of \code{"open"}, namely, the result of running a
weighted mean of \code{x} with weights \code{y}.}
}
\details{
The Fast Fourier Transform, \code{\link{fft}}, is used for efficiency.
The input sequences \code{x} and \code{y} must have the same length if
\code{circular} is true.
Note that the usual definition of convolution of two sequences
\code{x} and \code{y} is given by \code{convolve(x, rev(y), type = "o")}.
}
\value{
If \code{r <- convolve(x, y, type = "open")}
and \code{n <- length(x)}, \code{m <- length(y)}, then
\deqn{r_k = \sum_{i} x_{k-m+i} y_{i}}{r[k] = sum(i; x[k-m+i] * y[i])}
where the sum is over all valid indices \eqn{i}, for
\eqn{k = 1, \dots, n+m-1}.
If \code{type == "circular"}, \eqn{n = m} is required, and the above is
true for \eqn{i , k = 1,\dots,n} when
\eqn{x_{j} := x_{n+j}}{x[j] := x[n+j]} for \eqn{j < 1}.
}
\references{
Brillinger, D. R. (1981)
\emph{Time Series: Data Analysis and Theory}, Second Edition.
San Francisco: Holden-Day.
}
\seealso{\code{\link{fft}}, \code{\link{nextn}}, and particularly
\code{\link{filter}} (from the \pkg{stats} package) which may be
more appropriate.
}
\examples{
require(graphics)
x <- c(0,0,0,100,0,0,0)
y <- c(0,0,1, 2 ,1,0,0)/4
zapsmall(convolve(x, y)) # *NOT* what you first thought.
zapsmall(convolve(x, y[3:5], type = "f")) # rather
x <- rnorm(50)
y <- rnorm(50)
# Circular convolution *has* this symmetry:
all.equal(convolve(x, y, conj = FALSE), rev(convolve(rev(y),x)))
n <- length(x <- -20:24)
y <- (x-10)^2/1000 + rnorm(x)/8
Han <- function(y) # Hanning
convolve(y, c(1,2,1)/4, type = "filter")
plot(x, y, main = "Using convolve(.) for Hanning filters")
lines(x[-c(1 , n) ], Han(y), col = "red")
lines(x[-c(1:2, (n-1):n)], Han(Han(y)), lwd = 2, col = "dark blue")
}
\keyword{math}
\keyword{dplot}
| 2,883 | gpl-2.0 |
dfbb26fcc029dfc48a60e85f60952e32a45ba283 | reactorlabs/gnur | src/library/utils/man/shortPathName.Rd | % File src/library/utils/man/shortPathName.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2018 R Core Team
% Distributed under GPL 2 or later
\name{shortPathName}
\alias{shortPathName}
\title{Express File Paths in Short Form on Windows}
\description{
Convert file paths to the short form. This is an interface to the
Windows API call \code{GetShortPathNameW}.
}
\usage{
shortPathName(path)
}
\arguments{
\item{path}{character vector of file paths.}
}
% http://msdn.microsoft.com/en-gb/library/windows/desktop/aa364989%28v=vs.85%29.aspx
\details{
For most file systems, the short form is the \sQuote{DOS} form with
8+3 path components and no spaces, and this used to be guaranteed.
But some file systems on recent versions of Windows do not have short
path names when the long-name path will be returned instead.
}
\value{
A character vector. The path separator will be \code{\\}. If a
file path does not exist, the supplied path will be returned with slashes
replaced by backslashes.
}
\note{
This is only available on Windows.
}
\seealso{
\code{\link{normalizePath}}.
}
\examples{% (spacing: for nice rendering of visual part of example)
if(.Platform$OS.type == "windows") withAutoprint({
\donttest{ cat(shortPathName(c(R.home(), tempdir())), sep = "\n")}
\dontshow{ cat(shortPathName(R.home()), sep = "\n")}
})
}
\keyword{ utilities }
| 1,392 | gpl-2.0 |
374949dc1d3e3e7944c56ae5491da5ce60d74ff6 | jeffreyhorner/R-Judy-Arrays | src/library/stats/man/convolve.Rd | % File src/library/stats/man/convolve.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{convolve}
\alias{convolve}
\title{Convolution of Sequences via FFT}
\description{
Use the Fast Fourier Transform to compute the several kinds of
convolutions of two sequences.
}
\usage{
convolve(x, y, conj = TRUE, type = c("circular", "open", "filter"))
}
\arguments{
\item{x, y}{numeric sequences \emph{of the same length} to be
convolved.}
\item{conj}{logical; if \code{TRUE}, take the complex \emph{conjugate}
before back-transforming (default, and used for usual convolution).}
\item{type}{character; one of \code{"circular"}, \code{"open"},
\code{"filter"} (beginning of word is ok). For \code{circular}, the
two sequences are treated as \emph{circular}, i.e., periodic.
For \code{open} and \code{filter}, the sequences are padded with
\code{0}s (from left and right) first; \code{"filter"} returns the
middle sub-vector of \code{"open"}, namely, the result of running a
weighted mean of \code{x} with weights \code{y}.}
}
\details{
The Fast Fourier Transform, \code{\link{fft}}, is used for efficiency.
The input sequences \code{x} and \code{y} must have the same length if
\code{circular} is true.
Note that the usual definition of convolution of two sequences
\code{x} and \code{y} is given by \code{convolve(x, rev(y), type = "o")}.
}
\value{
If \code{r <- convolve(x, y, type = "open")}
and \code{n <- length(x)}, \code{m <- length(y)}, then
\deqn{r_k = \sum_{i} x_{k-m+i} y_{i}}{r[k] = sum(i; x[k-m+i] * y[i])}
where the sum is over all valid indices \eqn{i}, for
\eqn{k = 1, \dots, n+m-1}.
If \code{type == "circular"}, \eqn{n = m} is required, and the above is
true for \eqn{i , k = 1,\dots,n} when
\eqn{x_{j} := x_{n+j}}{x[j] := x[n+j]} for \eqn{j < 1}.
}
\references{
Brillinger, D. R. (1981)
\emph{Time Series: Data Analysis and Theory}, Second Edition.
San Francisco: Holden-Day.
}
\seealso{\code{\link{fft}}, \code{\link{nextn}}, and particularly
\code{\link{filter}} (from the \pkg{stats} package) which may be
more appropriate.
}
\examples{
require(graphics)
x <- c(0,0,0,100,0,0,0)
y <- c(0,0,1, 2 ,1,0,0)/4
zapsmall(convolve(x, y)) # *NOT* what you first thought.
zapsmall(convolve(x, y[3:5], type = "f")) # rather
x <- rnorm(50)
y <- rnorm(50)
# Circular convolution *has* this symmetry:
all.equal(convolve(x, y, conj = FALSE), rev(convolve(rev(y),x)))
n <- length(x <- -20:24)
y <- (x-10)^2/1000 + rnorm(x)/8
Han <- function(y) # Hanning
convolve(y, c(1,2,1)/4, type = "filter")
plot(x, y, main = "Using convolve(.) for Hanning filters")
lines(x[-c(1 , n) ], Han(y), col = "red")
lines(x[-c(1:2, (n-1):n)], Han(Han(y)), lwd = 2, col = "dark blue")
}
\keyword{math}
\keyword{dplot}
| 2,883 | gpl-2.0 |
374949dc1d3e3e7944c56ae5491da5ce60d74ff6 | cxxr-devel/cxxr-svn-mirror | src/library/stats/man/convolve.Rd | % File src/library/stats/man/convolve.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{convolve}
\alias{convolve}
\title{Convolution of Sequences via FFT}
\description{
Use the Fast Fourier Transform to compute the several kinds of
convolutions of two sequences.
}
\usage{
convolve(x, y, conj = TRUE, type = c("circular", "open", "filter"))
}
\arguments{
\item{x, y}{numeric sequences \emph{of the same length} to be
convolved.}
\item{conj}{logical; if \code{TRUE}, take the complex \emph{conjugate}
before back-transforming (default, and used for usual convolution).}
\item{type}{character; one of \code{"circular"}, \code{"open"},
\code{"filter"} (beginning of word is ok). For \code{circular}, the
two sequences are treated as \emph{circular}, i.e., periodic.
For \code{open} and \code{filter}, the sequences are padded with
\code{0}s (from left and right) first; \code{"filter"} returns the
middle sub-vector of \code{"open"}, namely, the result of running a
weighted mean of \code{x} with weights \code{y}.}
}
\details{
The Fast Fourier Transform, \code{\link{fft}}, is used for efficiency.
The input sequences \code{x} and \code{y} must have the same length if
\code{circular} is true.
Note that the usual definition of convolution of two sequences
\code{x} and \code{y} is given by \code{convolve(x, rev(y), type = "o")}.
}
\value{
If \code{r <- convolve(x, y, type = "open")}
and \code{n <- length(x)}, \code{m <- length(y)}, then
\deqn{r_k = \sum_{i} x_{k-m+i} y_{i}}{r[k] = sum(i; x[k-m+i] * y[i])}
where the sum is over all valid indices \eqn{i}, for
\eqn{k = 1, \dots, n+m-1}.
If \code{type == "circular"}, \eqn{n = m} is required, and the above is
true for \eqn{i , k = 1,\dots,n} when
\eqn{x_{j} := x_{n+j}}{x[j] := x[n+j]} for \eqn{j < 1}.
}
\references{
Brillinger, D. R. (1981)
\emph{Time Series: Data Analysis and Theory}, Second Edition.
San Francisco: Holden-Day.
}
\seealso{\code{\link{fft}}, \code{\link{nextn}}, and particularly
\code{\link{filter}} (from the \pkg{stats} package) which may be
more appropriate.
}
\examples{
require(graphics)
x <- c(0,0,0,100,0,0,0)
y <- c(0,0,1, 2 ,1,0,0)/4
zapsmall(convolve(x, y)) # *NOT* what you first thought.
zapsmall(convolve(x, y[3:5], type = "f")) # rather
x <- rnorm(50)
y <- rnorm(50)
# Circular convolution *has* this symmetry:
all.equal(convolve(x, y, conj = FALSE), rev(convolve(rev(y),x)))
n <- length(x <- -20:24)
y <- (x-10)^2/1000 + rnorm(x)/8
Han <- function(y) # Hanning
convolve(y, c(1,2,1)/4, type = "filter")
plot(x, y, main = "Using convolve(.) for Hanning filters")
lines(x[-c(1 , n) ], Han(y), col = "red")
lines(x[-c(1:2, (n-1):n)], Han(Han(y)), lwd = 2, col = "dark blue")
}
\keyword{math}
\keyword{dplot}
| 2,883 | gpl-2.0 |
374949dc1d3e3e7944c56ae5491da5ce60d74ff6 | skyguy94/R | src/library/stats/man/convolve.Rd | % File src/library/stats/man/convolve.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{convolve}
\alias{convolve}
\title{Convolution of Sequences via FFT}
\description{
Use the Fast Fourier Transform to compute the several kinds of
convolutions of two sequences.
}
\usage{
convolve(x, y, conj = TRUE, type = c("circular", "open", "filter"))
}
\arguments{
\item{x, y}{numeric sequences \emph{of the same length} to be
convolved.}
\item{conj}{logical; if \code{TRUE}, take the complex \emph{conjugate}
before back-transforming (default, and used for usual convolution).}
\item{type}{character; one of \code{"circular"}, \code{"open"},
\code{"filter"} (beginning of word is ok). For \code{circular}, the
two sequences are treated as \emph{circular}, i.e., periodic.
For \code{open} and \code{filter}, the sequences are padded with
\code{0}s (from left and right) first; \code{"filter"} returns the
middle sub-vector of \code{"open"}, namely, the result of running a
weighted mean of \code{x} with weights \code{y}.}
}
\details{
The Fast Fourier Transform, \code{\link{fft}}, is used for efficiency.
The input sequences \code{x} and \code{y} must have the same length if
\code{circular} is true.
Note that the usual definition of convolution of two sequences
\code{x} and \code{y} is given by \code{convolve(x, rev(y), type = "o")}.
}
\value{
If \code{r <- convolve(x, y, type = "open")}
and \code{n <- length(x)}, \code{m <- length(y)}, then
\deqn{r_k = \sum_{i} x_{k-m+i} y_{i}}{r[k] = sum(i; x[k-m+i] * y[i])}
where the sum is over all valid indices \eqn{i}, for
\eqn{k = 1, \dots, n+m-1}.
If \code{type == "circular"}, \eqn{n = m} is required, and the above is
true for \eqn{i , k = 1,\dots,n} when
\eqn{x_{j} := x_{n+j}}{x[j] := x[n+j]} for \eqn{j < 1}.
}
\references{
Brillinger, D. R. (1981)
\emph{Time Series: Data Analysis and Theory}, Second Edition.
San Francisco: Holden-Day.
}
\seealso{\code{\link{fft}}, \code{\link{nextn}}, and particularly
\code{\link{filter}} (from the \pkg{stats} package) which may be
more appropriate.
}
\examples{
require(graphics)
x <- c(0,0,0,100,0,0,0)
y <- c(0,0,1, 2 ,1,0,0)/4
zapsmall(convolve(x, y)) # *NOT* what you first thought.
zapsmall(convolve(x, y[3:5], type = "f")) # rather
x <- rnorm(50)
y <- rnorm(50)
# Circular convolution *has* this symmetry:
all.equal(convolve(x, y, conj = FALSE), rev(convolve(rev(y),x)))
n <- length(x <- -20:24)
y <- (x-10)^2/1000 + rnorm(x)/8
Han <- function(y) # Hanning
convolve(y, c(1,2,1)/4, type = "filter")
plot(x, y, main = "Using convolve(.) for Hanning filters")
lines(x[-c(1 , n) ], Han(y), col = "red")
lines(x[-c(1:2, (n-1):n)], Han(Han(y)), lwd = 2, col = "dark blue")
}
\keyword{math}
\keyword{dplot}
| 2,883 | gpl-2.0 |
374949dc1d3e3e7944c56ae5491da5ce60d74ff6 | glycerine/bigbird | r-3.0.2/src/library/stats/man/convolve.Rd | % File src/library/stats/man/convolve.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{convolve}
\alias{convolve}
\title{Convolution of Sequences via FFT}
\description{
Use the Fast Fourier Transform to compute the several kinds of
convolutions of two sequences.
}
\usage{
convolve(x, y, conj = TRUE, type = c("circular", "open", "filter"))
}
\arguments{
\item{x, y}{numeric sequences \emph{of the same length} to be
convolved.}
\item{conj}{logical; if \code{TRUE}, take the complex \emph{conjugate}
before back-transforming (default, and used for usual convolution).}
\item{type}{character; one of \code{"circular"}, \code{"open"},
\code{"filter"} (beginning of word is ok). For \code{circular}, the
two sequences are treated as \emph{circular}, i.e., periodic.
For \code{open} and \code{filter}, the sequences are padded with
\code{0}s (from left and right) first; \code{"filter"} returns the
middle sub-vector of \code{"open"}, namely, the result of running a
weighted mean of \code{x} with weights \code{y}.}
}
\details{
The Fast Fourier Transform, \code{\link{fft}}, is used for efficiency.
The input sequences \code{x} and \code{y} must have the same length if
\code{circular} is true.
Note that the usual definition of convolution of two sequences
\code{x} and \code{y} is given by \code{convolve(x, rev(y), type = "o")}.
}
\value{
If \code{r <- convolve(x, y, type = "open")}
and \code{n <- length(x)}, \code{m <- length(y)}, then
\deqn{r_k = \sum_{i} x_{k-m+i} y_{i}}{r[k] = sum(i; x[k-m+i] * y[i])}
where the sum is over all valid indices \eqn{i}, for
\eqn{k = 1, \dots, n+m-1}.
If \code{type == "circular"}, \eqn{n = m} is required, and the above is
true for \eqn{i , k = 1,\dots,n} when
\eqn{x_{j} := x_{n+j}}{x[j] := x[n+j]} for \eqn{j < 1}.
}
\references{
Brillinger, D. R. (1981)
\emph{Time Series: Data Analysis and Theory}, Second Edition.
San Francisco: Holden-Day.
}
\seealso{\code{\link{fft}}, \code{\link{nextn}}, and particularly
\code{\link{filter}} (from the \pkg{stats} package) which may be
more appropriate.
}
\examples{
require(graphics)
x <- c(0,0,0,100,0,0,0)
y <- c(0,0,1, 2 ,1,0,0)/4
zapsmall(convolve(x, y)) # *NOT* what you first thought.
zapsmall(convolve(x, y[3:5], type = "f")) # rather
x <- rnorm(50)
y <- rnorm(50)
# Circular convolution *has* this symmetry:
all.equal(convolve(x, y, conj = FALSE), rev(convolve(rev(y),x)))
n <- length(x <- -20:24)
y <- (x-10)^2/1000 + rnorm(x)/8
Han <- function(y) # Hanning
convolve(y, c(1,2,1)/4, type = "filter")
plot(x, y, main = "Using convolve(.) for Hanning filters")
lines(x[-c(1 , n) ], Han(y), col = "red")
lines(x[-c(1:2, (n-1):n)], Han(Han(y)), lwd = 2, col = "dark blue")
}
\keyword{math}
\keyword{dplot}
| 2,883 | bsd-2-clause |
374949dc1d3e3e7944c56ae5491da5ce60d74ff6 | mirror/r | src/library/stats/man/convolve.Rd | % File src/library/stats/man/convolve.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{convolve}
\alias{convolve}
\title{Convolution of Sequences via FFT}
\description{
Use the Fast Fourier Transform to compute the several kinds of
convolutions of two sequences.
}
\usage{
convolve(x, y, conj = TRUE, type = c("circular", "open", "filter"))
}
\arguments{
\item{x, y}{numeric sequences \emph{of the same length} to be
convolved.}
\item{conj}{logical; if \code{TRUE}, take the complex \emph{conjugate}
before back-transforming (default, and used for usual convolution).}
\item{type}{character; one of \code{"circular"}, \code{"open"},
\code{"filter"} (beginning of word is ok). For \code{circular}, the
two sequences are treated as \emph{circular}, i.e., periodic.
For \code{open} and \code{filter}, the sequences are padded with
\code{0}s (from left and right) first; \code{"filter"} returns the
middle sub-vector of \code{"open"}, namely, the result of running a
weighted mean of \code{x} with weights \code{y}.}
}
\details{
The Fast Fourier Transform, \code{\link{fft}}, is used for efficiency.
The input sequences \code{x} and \code{y} must have the same length if
\code{circular} is true.
Note that the usual definition of convolution of two sequences
\code{x} and \code{y} is given by \code{convolve(x, rev(y), type = "o")}.
}
\value{
If \code{r <- convolve(x, y, type = "open")}
and \code{n <- length(x)}, \code{m <- length(y)}, then
\deqn{r_k = \sum_{i} x_{k-m+i} y_{i}}{r[k] = sum(i; x[k-m+i] * y[i])}
where the sum is over all valid indices \eqn{i}, for
\eqn{k = 1, \dots, n+m-1}.
If \code{type == "circular"}, \eqn{n = m} is required, and the above is
true for \eqn{i , k = 1,\dots,n} when
\eqn{x_{j} := x_{n+j}}{x[j] := x[n+j]} for \eqn{j < 1}.
}
\references{
Brillinger, D. R. (1981)
\emph{Time Series: Data Analysis and Theory}, Second Edition.
San Francisco: Holden-Day.
}
\seealso{\code{\link{fft}}, \code{\link{nextn}}, and particularly
\code{\link{filter}} (from the \pkg{stats} package) which may be
more appropriate.
}
\examples{
require(graphics)
x <- c(0,0,0,100,0,0,0)
y <- c(0,0,1, 2 ,1,0,0)/4
zapsmall(convolve(x, y)) # *NOT* what you first thought.
zapsmall(convolve(x, y[3:5], type = "f")) # rather
x <- rnorm(50)
y <- rnorm(50)
# Circular convolution *has* this symmetry:
all.equal(convolve(x, y, conj = FALSE), rev(convolve(rev(y),x)))
n <- length(x <- -20:24)
y <- (x-10)^2/1000 + rnorm(x)/8
Han <- function(y) # Hanning
convolve(y, c(1,2,1)/4, type = "filter")
plot(x, y, main = "Using convolve(.) for Hanning filters")
lines(x[-c(1 , n) ], Han(y), col = "red")
lines(x[-c(1:2, (n-1):n)], Han(Han(y)), lwd = 2, col = "dark blue")
}
\keyword{math}
\keyword{dplot}
| 2,883 | gpl-2.0 |
dfbb26fcc029dfc48a60e85f60952e32a45ba283 | wch/r-source | src/library/utils/man/shortPathName.Rd | % File src/library/utils/man/shortPathName.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2018 R Core Team
% Distributed under GPL 2 or later
\name{shortPathName}
\alias{shortPathName}
\title{Express File Paths in Short Form on Windows}
\description{
Convert file paths to the short form. This is an interface to the
Windows API call \code{GetShortPathNameW}.
}
\usage{
shortPathName(path)
}
\arguments{
\item{path}{character vector of file paths.}
}
% http://msdn.microsoft.com/en-gb/library/windows/desktop/aa364989%28v=vs.85%29.aspx
\details{
For most file systems, the short form is the \sQuote{DOS} form with
8+3 path components and no spaces, and this used to be guaranteed.
But some file systems on recent versions of Windows do not have short
path names when the long-name path will be returned instead.
}
\value{
A character vector. The path separator will be \code{\\}. If a
file path does not exist, the supplied path will be returned with slashes
replaced by backslashes.
}
\note{
This is only available on Windows.
}
\seealso{
\code{\link{normalizePath}}.
}
\examples{% (spacing: for nice rendering of visual part of example)
if(.Platform$OS.type == "windows") withAutoprint({
\donttest{ cat(shortPathName(c(R.home(), tempdir())), sep = "\n")}
\dontshow{ cat(shortPathName(R.home()), sep = "\n")}
})
}
\keyword{ utilities }
| 1,392 | gpl-2.0 |
374949dc1d3e3e7944c56ae5491da5ce60d74ff6 | hxfeng/R-3.1.2 | src/library/stats/man/convolve.Rd | % File src/library/stats/man/convolve.Rd
% Part of the R package, http://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{convolve}
\alias{convolve}
\title{Convolution of Sequences via FFT}
\description{
Use the Fast Fourier Transform to compute the several kinds of
convolutions of two sequences.
}
\usage{
convolve(x, y, conj = TRUE, type = c("circular", "open", "filter"))
}
\arguments{
\item{x, y}{numeric sequences \emph{of the same length} to be
convolved.}
\item{conj}{logical; if \code{TRUE}, take the complex \emph{conjugate}
before back-transforming (default, and used for usual convolution).}
\item{type}{character; one of \code{"circular"}, \code{"open"},
\code{"filter"} (beginning of word is ok). For \code{circular}, the
two sequences are treated as \emph{circular}, i.e., periodic.
For \code{open} and \code{filter}, the sequences are padded with
\code{0}s (from left and right) first; \code{"filter"} returns the
middle sub-vector of \code{"open"}, namely, the result of running a
weighted mean of \code{x} with weights \code{y}.}
}
\details{
The Fast Fourier Transform, \code{\link{fft}}, is used for efficiency.
The input sequences \code{x} and \code{y} must have the same length if
\code{circular} is true.
Note that the usual definition of convolution of two sequences
\code{x} and \code{y} is given by \code{convolve(x, rev(y), type = "o")}.
}
\value{
If \code{r <- convolve(x, y, type = "open")}
and \code{n <- length(x)}, \code{m <- length(y)}, then
\deqn{r_k = \sum_{i} x_{k-m+i} y_{i}}{r[k] = sum(i; x[k-m+i] * y[i])}
where the sum is over all valid indices \eqn{i}, for
\eqn{k = 1, \dots, n+m-1}.
If \code{type == "circular"}, \eqn{n = m} is required, and the above is
true for \eqn{i , k = 1,\dots,n} when
\eqn{x_{j} := x_{n+j}}{x[j] := x[n+j]} for \eqn{j < 1}.
}
\references{
Brillinger, D. R. (1981)
\emph{Time Series: Data Analysis and Theory}, Second Edition.
San Francisco: Holden-Day.
}
\seealso{\code{\link{fft}}, \code{\link{nextn}}, and particularly
\code{\link{filter}} (from the \pkg{stats} package) which may be
more appropriate.
}
\examples{
require(graphics)
x <- c(0,0,0,100,0,0,0)
y <- c(0,0,1, 2 ,1,0,0)/4
zapsmall(convolve(x, y)) # *NOT* what you first thought.
zapsmall(convolve(x, y[3:5], type = "f")) # rather
x <- rnorm(50)
y <- rnorm(50)
# Circular convolution *has* this symmetry:
all.equal(convolve(x, y, conj = FALSE), rev(convolve(rev(y),x)))
n <- length(x <- -20:24)
y <- (x-10)^2/1000 + rnorm(x)/8
Han <- function(y) # Hanning
convolve(y, c(1,2,1)/4, type = "filter")
plot(x, y, main = "Using convolve(.) for Hanning filters")
lines(x[-c(1 , n) ], Han(y), col = "red")
lines(x[-c(1:2, (n-1):n)], Han(Han(y)), lwd = 2, col = "dark blue")
}
\keyword{math}
\keyword{dplot}
| 2,883 | gpl-2.0 |
ad8ab78d93d61590ab2f6cc787a5158b748a6129 | BlackEdder/mcmcsample | man/inside.ci.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/credible.R
\name{inside.ci}
\alias{inside.ci}
\title{Calculate samples within credibility region}
\usage{
inside.ci(samples, ci = 0.9, method = "bin", ...)
}
\arguments{
\item{samples}{Data frame holding the posterior samples. Each row is a sample, each column a parameter in the sample}
\item{ci}{Minimum fraction the credibility region should cover}
\item{method}{Method to use. Currently bin, chull and minmax are supported}
\item{...}{Parameters forwarded to the method used for calculating the regions}
}
\value{
A boolean vector, with true for samples inside the credibility region
}
\description{
Calculate which samples will fall inside a credibility region and which outside.
}
| 769 | gpl-3.0 |
963cbad9e380b0c7de6cb51b09a3005270d1f117 | jmp75/metaheuristics | R/pkgs/mh/man/loadMhLog.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/visualisation.r
\name{loadMhLog}
\alias{loadMhLog}
\title{Load a CSV log file of an optimisation}
\usage{
loadMhLog(fn)
}
\arguments{
\item{fn}{the file name of the CSV}
}
\value{
a data frame, as loaded with read.csv, and an added column 'PointNumber'
}
\description{
Load a CSV log file of an optimisation
}
| 397 | lgpl-2.1 |
fdede4de9f6d0874bf3331f7412aae6868350b08 | jread-usgs/repgen | man/json.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/utils-json.R
\name{json}
\alias{json}
\title{Import a JSON file to use for report}
\usage{
json(file)
}
\arguments{
\item{file}{incoming json file}
}
\description{
Import a JSON file to use for report
}
| 290 | cc0-1.0 |
0b7a1f0ae7fd583d4663875a680a2d18d394024c | prafols/rMSI | man/insertRasterImageAtCols.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/libimgramdisk.R
\name{insertRasterImageAtCols}
\alias{insertRasterImageAtCols}
\title{Inserts a image at specified Cols of a rMSI object.}
\usage{
insertRasterImageAtCols(Img, Cols, raster_matrix)
}
\arguments{
\item{Img}{the rMSI object where the data is stored (ramdisk).}
\item{Cols}{the columns indexes from which data will be inserted}
\item{raster_matrix}{a raster image represented as a matrix with pixel values.}
}
\description{
A raster image provided as a matrix is inserted at given Cols with a gaussian shape.
The raster_matrix has nrows as X direction.
}
| 648 | gpl-3.0 |
5f98c3b614f97398d9bd99b2dfd4409c3ba8900d | swarm-lab/Rvision | man/grabCut.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/transform.R
\name{grabCut}
\alias{grabCut}
\title{Segmentation with GrabCut Algorithm}
\usage{
grabCut(
image,
mask,
rect = rep(1, 4),
bgdModel,
fgdModel,
iter = 1,
mode = "EVAL"
)
}
\arguments{
\item{image}{An 8-bit (8U), 3-channel \code{\link{Image}} object to segment.}
\item{mask}{An 8-bit (8U), single-channel \code{\link{Image}} object. Each
pixel can take any of the following 4 values:
\itemize{
\item{0: }{an obvious background pixels.}
\item{1: }{an obvious foreground (object) pixel.}
\item{2: }{a possible background pixel.}
\item{3: }{a possible foreground pixel.}
}}
\item{rect}{A vector defining the region of interest containing a segmented
object. The pixels outside of the region of interest are marked as "obvious
background". \code{rect} must be a 4-element numeric vector which elements
correspond to - in this order - the x and y coordinates of the bottom left
corner of the region of interest, and to its width and height. The parameter
is only used when \code{mode="RECT"} (default: rep(1, 4)).}
\item{bgdModel}{A 1x65, single-channel, 64-bit (64F) \code{\link{Image}}
object to set and store the parameters of the background model.}
\item{fgdModel}{A 1x65, single-channel, 64-bit (64F) \code{\link{Image}}
object to set and store the parameters of the foreground model.}
\item{iter}{Number of iterations (default: 1) the algorithm should make
before returning the result. Note that the result can be refined with
further calls with \code{mode="MASK"} or \code{mode="MASK"}.}
\item{mode}{A character string indicating the operation mode of the function.
It can be any of the following:
\itemize{
\item{"RECT": }{The function initializes the state and the mask using the
provided \code{rect}. After that it runs \code{iter} iterations of the
algorithm.}
\item{"MASK":}{The function initializes the state using the provided
\code{mask}.}
\item{"EVAL":}{The value means that the function should just resume.}
\item{"FREEZE":}{The value means that the function should just run the
grabCut algorithm (a single iteration) with the fixed model.}
}}
}
\value{
This function returns nothing. It modifies in place \code{mask},
\code{bgdModel}, and \code{fgdModel}.
}
\description{
\code{grabCut} performs image segmentation (i.e., partition of
the image into coherent regions) using the GrabCut method.
}
\examples{
balloon <- image(system.file("sample_img/balloon1.png", package = "Rvision"))
mask <- zeros(nrow(balloon), ncol(balloon), 1)
bgdModel <- zeros(1, 65, 1, "64F")
fgdModel <- zeros(1, 65, 1, "64F")
grabCut(balloon, mask, c(290, 170, 160, 160), bgdModel, fgdModel, iter = 5, mode = "RECT")
}
\seealso{
\code{\link{Image}}
}
\author{
Simon Garnier, \email{[email protected]}
}
| 2,842 | gpl-3.0 |
b69f7417d50aee177fa5c43a407d8c652dc2a710 | pietrofranceschi/LCMSdemo | man/ExWarp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ptw.R
\name{ExWarp}
\alias{ExWarp}
\title{ExWarp}
\usage{
ExWarp()
}
\value{
an interactive demo on runnin gon the console. A web based shiny version of the demo is also available
}
\description{
Demo showing how parametric time warping (ptw) can be used to perform a simple form of
retention time alignment
}
\examples{
}
| 403 | gpl-3.0 |
e58b6ac8df6fff012865346e9e3e42b71dab55b4 | basilrabi/mansched | man/validEmpStatus.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{validEmpStatus}
\alias{validEmpStatus}
\title{Valid employment \code{status}}
\format{
character vector
}
\usage{
validEmpStatus
}
\description{
A character vector containing the valid employment status in Taganito Mine.
}
\keyword{datasets}
| 348 | gpl-3.0 |
d92529fb60863392760ce51e2b9728e360dd0f99 | ecjbosu/fSEAL | PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/chart.AcarSim.Rd | \name{chart.AcarSim}
\alias{chart.AcarSim}
\title{Acar-Shane Maximum Loss Plot}
\usage{
chart.AcarSim(R)
}
\arguments{
\item{R}{an xts, vector, matrix, data frame, timeSeries
or zoo object of asset returns}
}
\description{
To get some insight on the relationships between maximum
drawdown per unit of volatility and mean return divided
by volatility, we have proceeded to Monte-Carlo
simulations. We have simulated cash flows over a period
of 36 monthly returns and measured maximum drawdown for
varied levels of annualised return divided by volatility
varying from minus \emph{two to two} by step of
\emph{0.1} . The process has been repeated \bold{six
thousand times}.
}
\details{
Unfortunately, there is no \bold{analytical formulae} to
establish the maximum drawdown properties under the
random walk assumption. We should note first that due to
its definition, the maximum drawdown divided by
volatility is an only function of the ratio mean divided
by volatility. \deqn{MD/[\sigma]= Min (\sum[X(j)])/\sigma
= F(\mu/\sigma)} Where j varies from 1 to n ,which is the
number of drawdown's in simulation
}
\examples{
require(PerformanceAnalytics)
library(PerformanceAnalytics)
data(edhec)
chart.AcarSim(edhec)
}
\author{
Shubhankit Mohan
}
\references{
Maximum Loss and Maximum Drawdown in Financial
Markets,\emph{International Conference Sponsored by BNP
and Imperial College on: Forecasting Financial Markets,
London, United Kingdom, May 1997}
\url{http://www.intelligenthedgefundinvesting.com/pubs/easj.pdf}
}
\keyword{Drawdown}
\keyword{Loss}
\keyword{Maximum}
\keyword{Simulated}
| 1,693 | gpl-2.0 |
8fc8325bfc84a3276085a91ca2676ac48d7bab57 | cxxr-devel/cxxr-svn-mirror | src/library/Recommended/rpart/man/rpart.Rd | \name{rpart}
\alias{rpart}
%\alias{rpartcallback}
\title{
Recursive Partitioning and Regression Trees
}
\description{
Fit a \code{rpart} model
}
\usage{
rpart(formula, data, weights, subset, na.action = na.rpart, method,
model = FALSE, x = FALSE, y = TRUE, parms, control, cost, \dots)
}
\arguments{
\item{formula}{a \link{formula}, with a response but no interaction
terms. If this a a data frome, that is taken as the model frame
(see \code{\link{model.frame}).}
}
\item{data}{an optional data frame in which to interpret the variables
named in the formula.}
\item{weights}{optional case weights.}
\item{subset}{optional expression saying that only a subset of the
rows of the data should be used in the fit.}
\item{na.action}{the default action deletes all observations for which
\code{y} is missing, but keeps those in which one or more predictors
are missing.}
\item{method}{one of \code{"anova"}, \code{"poisson"}, \code{"class"}
or \code{"exp"}. If \code{method} is missing then the routine tries
to make an intelligent guess.
If \code{y} is a survival object, then \code{method = "exp"} is assumed,
if \code{y} has 2 columns then \code{method = "poisson"} is assumed,
if \code{y} is a factor then \code{method = "class"} is assumed,
otherwise \code{method = "anova"} is assumed.
It is wisest to specify the method directly, especially as more
criteria may added to the function in future.
Alternatively, \code{method} can be a list of functions named
\code{init}, \code{split} and \code{eval}. Examples are given in
the file \file{tests/usersplits.R} in the sources, and in the
vignettes \sQuote{User Written Split Functions}.}
\item{model}{if logical: keep a copy of the model frame in the result?
If the input value for \code{model} is a model frame (likely from an
earlier call to the \code{rpart} function), then this frame is used
rather than constructing new data.}
\item{x}{keep a copy of the \code{x} matrix in the result.}
\item{y}{keep a copy of the dependent variable in the result. If
missing and \code{model} is supplied this defaults to \code{FALSE}.}
\item{parms}{optional parameters for the splitting function.\cr
Anova splitting has no parameters.\cr
Poisson splitting has a single parameter, the coefficient of variation of
the prior distribution on the rates. The default value is 1.\cr
Exponential splitting has the same parameter as Poisson.\cr
For classification splitting, the list can contain any of:
the vector of prior probabilities (component \code{prior}), the loss matrix
(component \code{loss}) or the splitting index (component
\code{split}). The priors must be positive and sum to 1. The loss
matrix must have zeros on the diagonal and positive off-diagonal
elements. The splitting index can be \code{gini} or
\code{information}. The default priors are proportional to the data
counts, the losses default to 1, and the split defaults to
\code{gini}.}
\item{control}{a list of options that control details of the
\code{rpart} algorithm. See \code{\link{rpart.control}}.}
\item{cost}{a vector of non-negative costs, one for each variable in
the model. Defaults to one for all variables. These are scalings to
be applied when considering splits, so the improvement on splitting
on a variable is divided by its cost in deciding which split to
choose.}
\item{\dots}{arguments to \code{\link{rpart.control}} may also be
specified in the call to \code{rpart}. They are checked against the
list of valid arguments.}
}
\details{
This differs from the \code{tree} function in S mainly in its handling
of surrogate variables. In most details it follows Breiman
\emph{et. al} (1984) quite closely. \R package \pkg{tree} provides a
re-implementation of \code{tree}.
}
\value{
An object of class \code{rpart}. See \code{\link{rpart.object}}.
}
\references{
Breiman L., Friedman J. H., Olshen R. A., and Stone, C. J. (1984)
\emph{Classification and Regression Trees.}
Wadsworth.
}
\seealso{
\code{\link{rpart.control}}, \code{\link{rpart.object}},
\code{\link{summary.rpart}}, \code{\link{print.rpart}}
}
\examples{
fit <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis)
fit2 <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis,
parms = list(prior = c(.65,.35), split = "information"))
fit3 <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis,
control = rpart.control(cp = 0.05))
par(mfrow = c(1,2), xpd = NA) # otherwise on some devices the text is clipped
plot(fit)
text(fit, use.n = TRUE)
plot(fit2)
text(fit2, use.n = TRUE)
}
\keyword{tree}
| 4,799 | gpl-2.0 |
8fc8325bfc84a3276085a91ca2676ac48d7bab57 | andeek/rpart | man/rpart.Rd | \name{rpart}
\alias{rpart}
%\alias{rpartcallback}
\title{
Recursive Partitioning and Regression Trees
}
\description{
Fit a \code{rpart} model
}
\usage{
rpart(formula, data, weights, subset, na.action = na.rpart, method,
model = FALSE, x = FALSE, y = TRUE, parms, control, cost, \dots)
}
\arguments{
\item{formula}{a \link{formula}, with a response but no interaction
terms. If this a a data frome, that is taken as the model frame
(see \code{\link{model.frame}).}
}
\item{data}{an optional data frame in which to interpret the variables
named in the formula.}
\item{weights}{optional case weights.}
\item{subset}{optional expression saying that only a subset of the
rows of the data should be used in the fit.}
\item{na.action}{the default action deletes all observations for which
\code{y} is missing, but keeps those in which one or more predictors
are missing.}
\item{method}{one of \code{"anova"}, \code{"poisson"}, \code{"class"}
or \code{"exp"}. If \code{method} is missing then the routine tries
to make an intelligent guess.
If \code{y} is a survival object, then \code{method = "exp"} is assumed,
if \code{y} has 2 columns then \code{method = "poisson"} is assumed,
if \code{y} is a factor then \code{method = "class"} is assumed,
otherwise \code{method = "anova"} is assumed.
It is wisest to specify the method directly, especially as more
criteria may added to the function in future.
Alternatively, \code{method} can be a list of functions named
\code{init}, \code{split} and \code{eval}. Examples are given in
the file \file{tests/usersplits.R} in the sources, and in the
vignettes \sQuote{User Written Split Functions}.}
\item{model}{if logical: keep a copy of the model frame in the result?
If the input value for \code{model} is a model frame (likely from an
earlier call to the \code{rpart} function), then this frame is used
rather than constructing new data.}
\item{x}{keep a copy of the \code{x} matrix in the result.}
\item{y}{keep a copy of the dependent variable in the result. If
missing and \code{model} is supplied this defaults to \code{FALSE}.}
\item{parms}{optional parameters for the splitting function.\cr
Anova splitting has no parameters.\cr
Poisson splitting has a single parameter, the coefficient of variation of
the prior distribution on the rates. The default value is 1.\cr
Exponential splitting has the same parameter as Poisson.\cr
For classification splitting, the list can contain any of:
the vector of prior probabilities (component \code{prior}), the loss matrix
(component \code{loss}) or the splitting index (component
\code{split}). The priors must be positive and sum to 1. The loss
matrix must have zeros on the diagonal and positive off-diagonal
elements. The splitting index can be \code{gini} or
\code{information}. The default priors are proportional to the data
counts, the losses default to 1, and the split defaults to
\code{gini}.}
\item{control}{a list of options that control details of the
\code{rpart} algorithm. See \code{\link{rpart.control}}.}
\item{cost}{a vector of non-negative costs, one for each variable in
the model. Defaults to one for all variables. These are scalings to
be applied when considering splits, so the improvement on splitting
on a variable is divided by its cost in deciding which split to
choose.}
\item{\dots}{arguments to \code{\link{rpart.control}} may also be
specified in the call to \code{rpart}. They are checked against the
list of valid arguments.}
}
\details{
This differs from the \code{tree} function in S mainly in its handling
of surrogate variables. In most details it follows Breiman
\emph{et. al} (1984) quite closely. \R package \pkg{tree} provides a
re-implementation of \code{tree}.
}
\value{
An object of class \code{rpart}. See \code{\link{rpart.object}}.
}
\references{
Breiman L., Friedman J. H., Olshen R. A., and Stone, C. J. (1984)
\emph{Classification and Regression Trees.}
Wadsworth.
}
\seealso{
\code{\link{rpart.control}}, \code{\link{rpart.object}},
\code{\link{summary.rpart}}, \code{\link{print.rpart}}
}
\examples{
fit <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis)
fit2 <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis,
parms = list(prior = c(.65,.35), split = "information"))
fit3 <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis,
control = rpart.control(cp = 0.05))
par(mfrow = c(1,2), xpd = NA) # otherwise on some devices the text is clipped
plot(fit)
text(fit, use.n = TRUE)
plot(fit2)
text(fit2, use.n = TRUE)
}
\keyword{tree}
| 4,799 | gpl-3.0 |
5c7a722c94c89cbdf1a654b846c02ca04fed9f1c | aviralg/R-dyntrace | src/library/stats/man/Hypergeometric.Rd | % File src/library/stats/man/Hypergeometric.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2016 R Core Team
% Distributed under GPL 2 or later
\name{Hypergeometric}
\alias{Hypergeometric}
\alias{dhyper}
\alias{phyper}
\alias{qhyper}
\alias{rhyper}
\title{The Hypergeometric Distribution}
\description{
Density, distribution function, quantile function and random
generation for the hypergeometric distribution.
}
\usage{
dhyper(x, m, n, k, log = FALSE)
phyper(q, m, n, k, lower.tail = TRUE, log.p = FALSE)
qhyper(p, m, n, k, lower.tail = TRUE, log.p = FALSE)
rhyper(nn, m, n, k)
}
\arguments{
\item{x, q}{vector of quantiles representing the number of white balls
drawn without replacement from an urn which contains both black and
white balls.}
\item{m}{the number of white balls in the urn.}
\item{n}{the number of black balls in the urn.}
\item{k}{the number of balls drawn from the urn.}
\item{p}{probability, it must be between 0 and 1.}
\item{nn}{number of observations. If \code{length(nn) > 1}, the length
is taken to be the number required.}
\item{log, log.p}{logical; if TRUE, probabilities p are given as log(p).}
\item{lower.tail}{logical; if TRUE (default), probabilities are
\eqn{P[X \le x]}, otherwise, \eqn{P[X > x]}.}
}
\value{
\code{dhyper} gives the density,
\code{phyper} gives the distribution function,
\code{qhyper} gives the quantile function, and
\code{rhyper} generates random deviates.
Invalid arguments will result in return value \code{NaN}, with a warning.
The length of the result is determined by \code{n} for
\code{rhyper}, and is the maximum of the lengths of the
numerical arguments for the other functions.
The numerical arguments other than \code{n} are recycled to the
length of the result. Only the first elements of the logical
arguments are used.
}
\details{
The hypergeometric distribution is used for sampling \emph{without}
replacement. The density of this distribution with parameters
\code{m}, \code{n} and \code{k} (named \eqn{Np}, \eqn{N-Np}, and
\eqn{n}, respectively in the reference below) is given by
\deqn{
p(x) = \left. {m \choose x}{n \choose k-x} \right/ {m+n \choose k}%
}{p(x) = choose(m, x) choose(n, k-x) / choose(m+n, k)}
for \eqn{x = 0, \ldots, k}.
Note that \eqn{p(x)} is non-zero only for
\eqn{\max(0, k-n) \le x \le \min(k, m)}{max(0, k-n) <= x <= min(k, m)}.
With \eqn{p := m/(m+n)} (hence \eqn{Np = N \times p} in the
reference's notation), the first two moments are mean
\deqn{E[X] = \mu = k p} and variance
\deqn{\mbox{Var}(X) = k p (1 - p) \frac{m+n-k}{m+n-1},}{%
Var(X) = k p (1 - p) * (m+n-k)/(m+n-1),}
which shows the closeness to the Binomial\eqn{(k,p)} (where the
hypergeometric has smaller variance unless \eqn{k = 1}).
The quantile is defined as the smallest value \eqn{x} such that
\eqn{F(x) \ge p}, where \eqn{F} is the distribution function.
If one of \eqn{m, n, k}, exceeds \code{\link{.Machine}$integer.max},
currently the equivalent of \code{qhyper(runif(nn), m,n,k)} is used,
when a binomial approximation may be considerably more efficient.
}
\source{
\code{dhyper} computes via binomial probabilities, using code
contributed by Catherine Loader (see \code{\link{dbinom}}).
\code{phyper} is based on calculating \code{dhyper} and
\code{phyper(...)/dhyper(...)} (as a summation), based on ideas of Ian
Smith and Morten Welinder.
\code{qhyper} is based on inversion.
\code{rhyper} is based on a corrected version of
Kachitvichyanukul, V. and Schmeiser, B. (1985).
Computer generation of hypergeometric random variates.
\emph{Journal of Statistical Computation and Simulation},
\bold{22}, 127--145.
}
\references{
Johnson, N. L., Kotz, S., and Kemp, A. W. (1992)
\emph{Univariate Discrete Distributions},
Second Edition. New York: Wiley.
}
\seealso{
\link{Distributions} for other standard distributions.
}
\examples{
m <- 10; n <- 7; k <- 8
x <- 0:(k+1)
rbind(phyper(x, m, n, k), dhyper(x, m, n, k))
all(phyper(x, m, n, k) == cumsum(dhyper(x, m, n, k))) # FALSE
\donttest{## but error is very small:
signif(phyper(x, m, n, k) - cumsum(dhyper(x, m, n, k)), digits = 3)
}}
\keyword{distribution}
| 4,269 | gpl-2.0 |