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\name{BasicStatistics}
\alias{BasicStatistics}
\alias{basicStats}
\alias{skewness}
\alias{skewness.default}
\alias{skewness.data.frame}
\alias{skewness.POSIXct}
\alias{skewness.POSIXlt}
\alias{kurtosis}
\alias{kurtosis.default}
\alias{kurtosis.data.frame}
\alias{kurtosis.POSIXct}
\alias{kurtosis.POSIXlt}
\alias{rowStats}
\alias{rowAvgs}
\alias{rowVars}
\alias{rowStdevs}
\alias{rowSkewness}
\alias{rowKurtosis}
\alias{rowCumsums}
\alias{colStats}
\alias{colAvgs}
\alias{colVars}
\alias{colStdevs}
\alias{colSkewness}
\alias{colKurtosis}
\alias{colCumsums}
\alias{stdev}
\title{Basic Statistics Summary}
\description{
A collection and description of functions to compute
basic statistical properties of financial and
economic time series data. Missing functions in
R to calculate skewness and kurtosis are added, a
function which creates a summary statistics, and
functions to calculate column and row statistics.
\cr
The functions are:
\tabular{ll}{
\code{skewness} \tab returns value of skewness, \cr
\code{kurtosis} \tab returns value of kurtosis, \cr
\code{basicStats} \tab computes an overview of basic statistical values, \cr
\code{rowStats}\tab calculates row statistics, \cr
\code{colStats} \tab calculates column statistics, \cr
\code{rowAvgs} \tab calculates row means, \cr
\code{colAvgs} \tab calculates column means, \cr
\code{rowVars} \tab calculates row variances, \cr
\code{colVars} \tab calculates column variances, \cr
\code{rowStdevs} \tab calculates row standard deviations, \cr
\code{colStdevs} \tab calculates column standard deviations, \cr
\code{rowSkewness} \tab calculates row skewness, \cr
\code{colSkewness} \tab calculates column skewness, \cr
\code{rowKurtosis} \tab calculates row kurtosis, \cr
\code{colKurtosis} \tab calculates column kurtosis, \cr
\code{rowCumsums} \tab calculates row cumulated Sums, \cr
\code{colCumsums} \tab calculates column cumulated Sums. }
For SPLUS Compatibility:
\tabular{ll}{
\code{stdev} \tab Returns the standard deviation of a vector or matrix. }
}
\usage{
stdev(x, na.rm = FALSE)
skewness(x, \dots)
\method{skewness}{default}(x, na.rm = FALSE, method = c("moment", "fisher"), \dots)
\method{skewness}{data.frame}(x, \dots)
\method{skewness}{POSIXct}(x, \dots)
\method{skewness}{POSIXlt}(x, \dots)
kurtosis(x, \dots)
\method{kurtosis}{default}(x, na.rm = FALSE, method = c("excess", "moment", "fisher"), \dots)
\method{kurtosis}{data.frame}(x, \dots)
\method{kurtosis}{POSIXct}(x, \dots)
\method{kurtosis}{POSIXlt}(x, \dots)
basicStats(x, ci = 0.95)
rowStats(x, FUN, na.rm = FALSE, \dots)
rowAvgs(x, na.rm = FALSE, \dots)
rowVars(x, na.rm = FALSE, \dots)
rowStdevs(x, na.rm = FALSE, \dots)
rowSkewness(x, na.rm = FALSE, \dots)
rowKurtosis(x, na.rm = FALSE, \dots)
rowCumsums(x, na.rm = FALSE, \dots)
colStats(x, FUN, na.rm = FALSE, \dots)
colAvgs(x, na.rm = FALSE, \dots)
colVars(x, na.rm = FALSE, \dots)
colStdevs(x, na.rm = FALSE, \dots)
colSkewness(x, na.rm = FALSE, \dots)
colKurtosis(x, na.rm = FALSE, \dots)
colCumsums(x, na.rm = FALSE, \dots)
}
\arguments{
\item{ci}{
confidence interval, a numeric value, by default 0.95,
i.e. 95 percent.
}
\item{FUN}{
[colStats][rowStats - \cr
the statistical function to be applied.
}
\item{na.rm}{
a logical. Should missing values be removed?
}
\item{method}{
[kurtosis][skewness] - \cr
a character string which specifies the method of computation.
These are either \code{"moment"} or \code{"fisher"}, kurtosis
allows in addition for \code{"excess"}. If \code{"excess"} is
selected, then the value of the kurtosis is computed by the
\code{"moment"} method and a value of 3 will be subtracted.
The \code{"moment"} method is based on the definitions of
skewness and kurtosis for distributions; these forms should
be used when resampling (bootstrap or jackknife). The
\code{"fisher"} method correspond to the usual "unbiased"
definition of sample variance, although in the case of skewness
and kurtosis exact unbiasedness is not possible.
}
\item{x}{
a numeric vector, or a matrix for column statistics.
\cr
[basicStats] - \cr
allows also a matrix, data.frame or timeSeries as input.
In this case only the first column of data will be considered
and a a warning will be printed.
}
\item{\dots}{
arguments to be passed.
}
}
\value{
\code{skewness}\cr
\code{kurtosis}
\cr
return the value of the statistics, a numeric value. An
attribute which reports the used method is added.
\cr
\code{basicsStats}
\cr
returns data frame with the following entries and row names:
nobs, NAs, Minimum, Maximum , 1. Quartile, 3. Quartile,
Mean, Median, Sum, SE Mean, LCL Mean, UCL Mean, Variance,
Stdev, Skewness, Kurtosis.
\cr
\code{rowStats}\cr
\code{rowAvgs}\cr
\code{rowVars}\cr
\code{rowStdevs}\cr
\code{rowSkewness}\cr
\code{rowKurtosis}\cr
\code{rowCumsum}
\cr
compute sample statistics by column. Missing values can be
handled.
\cr
\code{colStats}\cr
\code{colAvgs}\cr
\code{colVars}\cr
\code{colStdevs},\cr
\code{colSkewness}\cr
\code{colKurtosis}\cr
\code{colCumsum}
\cr
compute sample statistics by column. Missing values can be
handled.
}
\note{
R's-base package contains a function \code{colMeans} with an
additional argument \code{dim=1}. Therefore, the function used
here to compute column means (averages) is named \code{colAvgs}.
\cr
The function \code{stdev} computes the standard deviation for a
vector or matrix and was introduced for SPlus compatibility.
Under R use the function \code{sd}.
}
\author{
Diethelm Wuertz for the Rmetrics \R-port.
}
\examples{
## SOURCE("fBasics.3A-BasicStatistics")
## basicStats -
# Simulated Monthly Return Data:
tS = timeSeries(matrix(rnorm(12)), timeCalendar())
# ... must be univariate:
basicStats(tS)
## mean -
## var -
## skewness -
## kurtosis -
# Mean, Variance:
mean(tS)
var(tS)
# Skewness, Kurtosis:
class(tS)
skewness(tS)
kurtosis(tS)
}
\keyword{univar}
|
