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#' Returns Summary: Statistics and Stylized Facts
#'
#' Returns a basic set of statistics that match the period of the data passed
#' in (e.g., monthly returns will get monthly statistics, daily will be daily
#' stats, and so on)
#'
#' This was created as a way to display a set of related statistics together
#' for comparison across a set of instruments or funds. Careful consideration
#' to missing data or unequal time series should be given when intepreting the
#' results.
#'
#' @aliases table.Stats table.MonthlyReturns
#' @param R an xts, vector, matrix, data frame, timeSeries or zoo object of
#' asset returns
#' @param ci confidence interval, defaults to 95\%
#' @param digits number of digits to round results to
#' @author Peter Carl
###keywords ts multivariate distribution models
#' @examples
#'
#' data(edhec)
#' table.Stats(edhec[,1:3])
#' t(table.Stats(edhec))
#'
#' result=t(table.Stats(edhec))
#' require("Hmisc")
#' textplot(format.df(result, na.blank=TRUE, numeric.dollar=FALSE, cdec=c(rep(1,2),rep(3,14))),
#' rmar = 0.8, cmar = 1.5, max.cex=.9, halign = "center", valign = "top",
#' row.valign="center", wrap.rownames=10, wrap.colnames=10, mar = c(0,0,3,0)+0.1)
#' title(main="Statistics for EDHEC Indexes")
#'
#' @rdname table.MonthlyReturns
#' @export
table.Stats <-
function (R, ci = 0.95, digits = 4)
{# @author Peter Carl
# DESCRIPTION
# Returns Summary: Statistics and Stylized Facts
# Inputs:
# R: Assumes returns rather than prices
# Output:
# Returns a basic set of statistics that match the period of the data passed
# in (e.g., monthly returns will get monthly statistics)
# FUNCTION:
y = checkData(R, method = "zoo")
# Set up dimensions and labels
columns = ncol(y)
rows = nrow(y)
columnnames = colnames(y)
rownames = rownames(y)
cl.vals = function(x, ci) {
x = x[!is.na(x)]
n = length(x)
if (n <= 1)
return(c(NA, NA))
se.mean = sqrt(var(x)/n)
t.val = qt((1 - ci)/2, n - 1)
mn = mean(x)
lcl = mn + se.mean * t.val
ucl = mn - se.mean * t.val
c(lcl, ucl)
}
# for each column, do the following:
for(column in 1:columns) {
x = as.vector(y[,column])
x.length = length(x)
x = x[!is.na(x)]
x.na = x.length - length(x)
z = c(
length(x),
x.na, min(x),
as.numeric(quantile(x, prob = 0.25, na.rm = TRUE)),
median(x),
mean(x),
exp(mean(log(1+x)))-1,
as.numeric(quantile(x, prob = 0.75, na.rm = TRUE)),
max(x),
sqrt(var(x)/length(x)),
cl.vals(x, ci)[1],
cl.vals(x, ci)[2],
var(x),
sqrt(var(x)),
skewness(x),
kurtosis(x)
)
z = base::round(as.numeric(z),digits)
znames = c(
"Observations",
"NAs",
"Minimum",
"Quartile 1",
"Median",
"Arithmetic Mean",
"Geometric Mean",
"Quartile 3",
"Maximum",
"SE Mean",
paste("LCL Mean (",ci,")",sep=""),
paste("UCL Mean (",ci,")",sep=""),
"Variance",
"Stdev",
"Skewness",
"Kurtosis"
)
if(column == 1) {
resultingtable = data.frame(Value = z, row.names = znames)
}
else {
nextcolumn = data.frame(Value = z, row.names = znames)
resultingtable = cbind(resultingtable, nextcolumn)
}
}
colnames(resultingtable) = columnnames
ans = resultingtable
ans
}
###############################################################################
# R (http://r-project.org/) Econometrics for Performance and Risk Analysis
#
# Copyright (c) 2004-2020 Peter Carl and Brian G. Peterson
#
# This R package is distributed under the terms of the GNU Public License (GPL)
# for full details see the file COPYING
#
# $Id$
#
###############################################################################
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