[Roxygen-devel] file which doesn't seem to process \dontrun properly in examples section
Brian G. Peterson
brian at braverock.com
Wed May 23 20:41:25 CEST 2012
I've finished converting the 140+ page manual of package
PerformanceAnalytics to roxygen2 documention mostly using Rd2roxygen.
I had 14 R files that generated Rd errors after conversion, all of which
I have now resolved except one.
The attached .R file generates the attached .Rd file with these
warnings:
Writing chart.QQPlot.Rd
Warning messages:
1: fit$estimate[[1]], is an unknown key in block chart.QQPlot.R:2
2: fit$estimate[[2]], is an unknown key in block chart.QQPlot.R:2
3: fit$estimate[[3]], is an unknown key in block chart.QQPlot.R:2
4: fit$estimate[[4]], is an unknown key in block chart.QQPlot.R:2
and then, the generated .Rd file is missing a closing brace } on the
example section:
R CMD Rd2pdf PerformanceAnalytics/man/chart.QQPlot.Rd
Converting Rd files to LaTeX ...
chart.QQPlot.Rd
Warning in parse_Rd("PerformanceAnalytics/man/chart.QQPlot.Rd", encoding
= "unknown", :
PerformanceAnalytics/man/chart.QQPlot.Rd:118: unexpected section
header '\author'
Warning in parse_Rd("PerformanceAnalytics/man/chart.QQPlot.Rd", encoding
= "unknown", :
PerformanceAnalytics/man/chart.QQPlot.Rd:121: unexpected section
header '\references'
Warning in parse_Rd("PerformanceAnalytics/man/chart.QQPlot.Rd", encoding
= "unknown", :
PerformanceAnalytics/man/chart.QQPlot.Rd:127: unexpected section
header '\seealso'
Warning in parse_Rd("PerformanceAnalytics/man/chart.QQPlot.Rd", encoding
= "unknown", :
PerformanceAnalytics/man/chart.QQPlot.Rd:131: unexpected section
header '\keyword'
<...>
I can manually add the last brace to the examples section of the .Rd
file, and I've done this for now, but this will break any time that
roxygenize is run.
Any idea what I've done wrong? Or is this an error in the roclet
parser?
Regards,
- Brian
--
Brian G. Peterson
http://braverock.com/brian/
Ph: 773-459-4973
IM: bgpbraverock
-------------- next part --------------
#' Plot a QQ chart
#'
#' Plot the return data against any theoretical distribution.
#'
#' A Quantile-Quantile (QQ) plot is a scatter plot designed to compare the data
#' to the theoretical distributions to visually determine if the observations
#' are likely to have come from a known population. The empirical quantiles are
#' plotted to the y-axis, and the x-axis contains the values of the theorical
#' model. A 45-degree reference line is also plotted. If the empirical data
#' come from the population with the choosen distribution, the points should
#' fall approximately along this reference line. The larger the departure from
#' the reference line, the greater the evidence that the data set have come
#' from a population with a different distribution.
#'
#' @param R an xts, vector, matrix, data frame, timeSeries or zoo object of
#' asset returns
#' @param distribution root name of comparison distribution - e.g., 'norm' for
#' the normal distribution; 't' for the t-distribution. See examples for other
#' ideas.
#' @param xlab set the x-axis label, as in \code{\link{plot}}
#' @param ylab set the y-axis label, as in \code{\link{plot}}
#' @param xaxis if true, draws the x axis
#' @param yaxis if true, draws the y axis
#' @param ylim set the y-axis limits, same as in \code{\link{plot}}
#' @param main set the chart title, same as in \code{plot}
#' @param las set the direction of axis labels, same as in \code{plot}
#' @param envelope confidence level for point-wise confidence envelope, or
#' FALSE for no envelope.
#' @param labels vector of point labels for interactive point identification,
#' or FALSE for no labels.
#' @param col color for points and lines; the default is the \emph{second}
#' entry in the current color palette (see 'palette' and 'par').
#' @param lwd set the line width, as in \code{\link{plot}}
#' @param pch symbols to use, see also \code{\link{plot}}
#' @param cex symbols to use, see also \code{\link{plot}}
#' @param line 'quartiles' to pass a line through the quartile-pairs, or
#' 'robust' for a robust-regression line; the latter uses the 'rlm' function
#' in the 'MASS' package. Specifying 'line = "none"' suppresses the line.
#' @param element.color provides the color for drawing chart elements, such as
#' the box lines, axis lines, etc. Default is "darkgray"
#' @param cex.legend The magnification to be used for sizing the legend
#' relative to the current setting of 'cex'
#' @param cex.axis The magnification to be used for axis annotation relative to
#' the current setting of 'cex'
#' @param cex.lab The magnification to be used for x- and y-axis labels
#' relative to the current setting of 'cex'
#' @param cex.main The magnification to be used for the main title relative to
#' the current setting of 'cex'.
#' @param \dots any other passthru parameters to the distribution function
#'
#' @author John Fox, ported by Peter Carl
#' @seealso
#' \code{\link[stats]{qqplot}} \cr
#' \code{\link[car]{qq.plot}} \cr
#' \code{\link{plot}}
#' @references main code forked/borrowed/ported from the excellent: \cr Fox,
#' John (2007) \emph{car: Companion to Applied Regression} \cr
#' \url{http://www.r-project.org},
#' \url{http://socserv.socsci.mcmaster.ca/jfox/}
#' @keywords ts multivariate distribution models hplot
#' @examples
#'
#' library(MASS)
#' data(managers)
#' x = checkData(managers[,2, drop = FALSE], na.rm = TRUE, method = "vector")
#' #layout(rbind(c(1,2),c(3,4)))
#' # Panel 1, Normal distribution
#' chart.QQPlot(x, main = "Normal Distribution", distribution = 'norm', envelope=0.95)
#' # Panel 2, Log-Normal distribution
#' fit = fitdistr(1+x, 'lognormal')
#' chart.QQPlot(1+x, main = "Log-Normal Distribution", envelope=0.95, distribution='lnorm')#, meanlog = fit$estimate[[1]], sdlog = fit$estimate[[2]])
#' \dontrun{
#' # Panel 3, Skew-T distribution
#' library(sn)
#' fit = st.mle(y=x)
#' chart.QQPlot(x, main = "Skew T Distribution", envelope=0.95, distribution = 'st', location = fit$dp[[1]], scale = fit$dp[[2]], shape = fit$dp[[3]], df=fit$dp[[4]])
#' #Panel 4: Stable Parietian
#' library(fBasics)
#' fit.stable = stableFit(x,doplot=FALSE)
#' chart.QQPlot(x, main = "Stable Paretian Distribution", envelope=0.95, distribution = 'stable', alpha = fit.stable at fit$estimate[[1]], beta = fit.stable at fit$estimate[[2]], gamma = fit.stable at fit$estimate[[3]], delta = fit.stable at fit$estimate[[4]], pm = 0)
#' }
#' #end examples
#'
chart.QQPlot <-
function(R, distribution="norm", ylab=NULL,
xlab=paste(distribution, "Quantiles"), main=NULL, las=par("las"),
envelope=FALSE, labels=FALSE, col=c(1,4), lwd=2, pch=1, cex=1,
line=c("quartiles", "robust", "none"), element.color = "darkgray",
cex.axis = 0.8, cex.legend = 0.8, cex.lab = 1, cex.main = 1, xaxis=TRUE, yaxis=TRUE, ylim=NULL, ...)
{ # @author Peter Carl
# DESCRIPTION:
# A wrapper to create a chart of relative returns through time
# Inputs:
# R: a matrix, data frame, or timeSeries of returns
# Outputs:
# A Normal Q-Q Plot
# FUNCTION:
x = checkData(R, method = "vector", na.rm = TRUE)
# n = length(x)
if(is.null(main)){
if(!is.null(colnames(R)[1]))
main=colnames(R)[1]
else
main = "QQ Plot"
}
if(is.null(ylab)) ylab = "Empirical Quantiles"
# the core of this function is taken from John Fox's qq.plot, which is part of the car package
result <- NULL
line <- match.arg(line)
good <- !is.na(x)
ord <- order(x[good])
ord.x <- x[good][ord]
q.function <- eval(parse(text=paste("q",distribution, sep="")))
d.function <- eval(parse(text=paste("d",distribution, sep="")))
n <- length(ord.x)
P <- ppoints(n)
z <- q.function(P, ...)
plot(z, ord.x, xlab=xlab, ylab=ylab, main=main, las=las, col=col[1], pch=pch,
cex=cex, cex.main = cex.main, cex.lab = cex.lab, axes=FALSE, ylim=ylim, ...)
if (line=="quartiles"){
Q.x<-quantile(ord.x, c(.25,.75))
Q.z<-q.function(c(.25,.75), ...)
b<-(Q.x[2]-Q.x[1])/(Q.z[2]-Q.z[1])
a<-Q.x[1]-b*Q.z[1]
abline(a, b, col=col[2], lwd=lwd)
}
if (line=="robust"){
stopifnot("package:MASS" %in% search() || require("MASS",quietly=TRUE))
coef<-coefficients(rlm(ord.x~z))
a<-coef[1]
b<-coef[2]
abline(a,b, col=col[2])
}
if (line != 'none' & envelope != FALSE) {
zz<-qnorm(1-(1-envelope)/2)
SE<-(b/d.function(z, ...))*sqrt(P*(1-P)/n)
fit.value<-a+b*z
upper<-fit.value+zz*SE
lower<-fit.value-zz*SE
lines(z, upper, lty=2, lwd=lwd/2, col=col[2])
lines(z, lower, lty=2, lwd=lwd/2, col=col[2])
}
if (labels[1]==TRUE & length(labels)==1) labels<-seq(along=z)
if (labels[1] != FALSE) {
selected<-identify(z, ord.x, labels[good][ord])
result <- seq(along=x)[good][ord][selected]
}
if (is.null(result)) invisible(result) else sort(result)
# if(distribution == "normal") {
# if(is.null(xlab)) xlab = "Normal Quantiles"
# if(is.null(ylab)) ylab = "Empirical Quantiles"
# if(is.null(main)) main = "Normal QQ-Plot"
#
# # Normal Quantile-Quantile Plot:
# qqnorm(x, xlab = xlab, ylab = ylab, main = main, pch = symbolset, axes = FALSE, ...)
# # qqline(x, col = colorset[2], lwd = 2)
# q.theo = qnorm(c(0.25,0.75))
# }
# if(distribution == "sst") {
# library("sn")
# if(is.null(xlab)) xlab = "Skew-T Quantiles"
# if(is.null(ylab)) ylab = "Empirical Quantiles"
# if(is.null(main)) main = "Skew-T QQ-Plot"
#
# # Skew Student-T Quantile-Quantile Plot:
# y = qst(c(1:n)/(n+1))
# qqplot(y, x, xlab = xlab, ylab = ylab, axes=FALSE, main=main, ...)
# q.theo = qst(c(0.25,0.75))
# }
# if(distribution == "cauchy") {
# if(is.null(xlab)) xlab = "Cauchy Quantiles"
# if(is.null(ylab)) ylab = "Empirical Quantiles"
# if(is.null(main)) main = "Cauchy QQ-Plot"
#
# # Skew Student-T Quantile-Quantile Plot:
# y = qcauchy(c(1:n)/(n+1))
# qqplot(y, x, xlab = xlab, ylab = ylab, axes=FALSE, main=main, ...)
# q.theo = qcauchy(c(0.25,0.75))
# }
# if(distribution == "lnorm") {
# if(is.null(xlab)) xlab = "Log Normal Quantiles"
# if(is.null(ylab)) ylab = "Empirical Quantiles"
# if(is.null(main)) main = "Log Normal QQ-Plot"
#
# # Skew Student-T Quantile-Quantile Plot:
# y = qlnorm(c(1:n)/(n+1))
# qqplot(y, x, xlab = xlab, ylab = ylab, axes=FALSE, main=main, ...)
# q.theo = qlnorm(c(0.25,0.75))
# }
#
# q.data=quantile(x,c(0.25,0.75))
# slope = diff(q.data)/diff(q.theo)
# int = q.data[1] - slope* q.theo[1]
#
# if(line) abline(int, slope, col = colorset[2], lwd = 2)
if(xaxis)
axis(1, cex.axis = cex.axis, col = element.color)
if(yaxis)
axis(2, cex.axis = cex.axis, col = element.color)
box(col=element.color)
}
###############################################################################
# R (http://r-project.org/) Econometrics for Performance and Risk Analysis
#
# Copyright (c) 2004-2012 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: chart.QQPlot.R 1957 2012-05-23 18:12:45Z braverock $
#
###############################################################################
-------------- next part --------------
\name{chart.QQPlot}
\alias{chart.QQPlot}
\title{Plot a QQ chart}
\usage{
chart.QQPlot(R, distribution = "norm", ylab = NULL,
xlab = paste(distribution, "Quantiles"), main = NULL,
las = par("las"), envelope = FALSE, labels = FALSE,
col = c(1, 4), lwd = 2, pch = 1, cex = 1,
line = c("quartiles", "robust", "none"),
element.color = "darkgray", cex.axis = 0.8,
cex.legend = 0.8, cex.lab = 1, cex.main = 1,
xaxis = TRUE, yaxis = TRUE, ylim = NULL, ...)
}
\arguments{
\item{R}{an xts, vector, matrix, data frame, timeSeries
or zoo object of asset returns}
\item{distribution}{root name of comparison distribution
- e.g., 'norm' for the normal distribution; 't' for the
t-distribution. See examples for other ideas.}
\item{xlab}{set the x-axis label, as in
\code{\link{plot}}}
\item{ylab}{set the y-axis label, as in
\code{\link{plot}}}
\item{xaxis}{if true, draws the x axis}
\item{yaxis}{if true, draws the y axis}
\item{ylim}{set the y-axis limits, same as in
\code{\link{plot}}}
\item{main}{set the chart title, same as in \code{plot}}
\item{las}{set the direction of axis labels, same as in
\code{plot}}
\item{envelope}{confidence level for point-wise
confidence envelope, or FALSE for no envelope.}
\item{labels}{vector of point labels for interactive
point identification, or FALSE for no labels.}
\item{col}{color for points and lines; the default is the
\emph{second} entry in the current color palette (see
'palette' and 'par').}
\item{lwd}{set the line width, as in \code{\link{plot}}}
\item{pch}{symbols to use, see also \code{\link{plot}}}
\item{cex}{symbols to use, see also \code{\link{plot}}}
\item{line}{'quartiles' to pass a line through the
quartile-pairs, or 'robust' for a robust-regression line;
the latter uses the 'rlm' function in the 'MASS' package.
Specifying 'line = "none"' suppresses the line.}
\item{element.color}{provides the color for drawing chart
elements, such as the box lines, axis lines, etc. Default
is "darkgray"}
\item{cex.legend}{The magnification to be used for sizing
the legend relative to the current setting of 'cex'}
\item{cex.axis}{The magnification to be used for axis
annotation relative to the current setting of 'cex'}
\item{cex.lab}{The magnification to be used for x- and
y-axis labels relative to the current setting of 'cex'}
\item{cex.main}{The magnification to be used for the main
title relative to the current setting of 'cex'.}
\item{\dots}{any other passthru parameters to the
distribution function}
}
\description{
Plot the return data against any theoretical
distribution.
}
\details{
A Quantile-Quantile (QQ) plot is a scatter plot designed
to compare the data to the theoretical distributions to
visually determine if the observations are likely to have
come from a known population. The empirical quantiles are
plotted to the y-axis, and the x-axis contains the values
of the theorical model. A 45-degree reference line is
also plotted. If the empirical data come from the
population with the choosen distribution, the points
should fall approximately along this reference line. The
larger the departure from the reference line, the greater
the evidence that the data set have come from a
population with a different distribution.
}
\examples{
library(MASS)
data(managers)
x = checkData(managers[,2, drop = FALSE], na.rm = TRUE, method = "vector")
#layout(rbind(c(1,2),c(3,4)))
# Panel 1, Normal distribution
chart.QQPlot(x, main = "Normal Distribution", distribution = 'norm', envelope=0.95)
# Panel 2, Log-Normal distribution
fit = fitdistr(1+x, 'lognormal')
chart.QQPlot(1+x, main = "Log-Normal Distribution", envelope=0.95, distribution='lnorm')#, meanlog = fit$estimate[[1]], sdlog = fit$estimate[[2]])
\dontrun{
# Panel 3, Skew-T distribution
library(sn)
fit = st.mle(y=x)
chart.QQPlot(x, main = "Skew T Distribution", envelope=0.95, distribution = 'st', location = fit$dp[[1]], scale = fit$dp[[2]], shape = fit$dp[[3]], df=fit$dp[[4]])
#Panel 4: Stable Parietian
library(fBasics)
fit.stable = stableFit(x,doplot=FALSE)
chart.QQPlot(x, main = "Stable Paretian Distribution", envelope=0.95, distribution = 'stable', alpha = fit.stable
}
\author{
John Fox, ported by Peter Carl
}
\references{
main code forked/borrowed/ported from the excellent: \cr
Fox, John (2007) \emph{car: Companion to Applied
Regression} \cr \url{http://www.r-project.org},
\url{http://socserv.socsci.mcmaster.ca/jfox/}
}
\seealso{
\code{\link[stats]{qqplot}} \cr
\code{\link[car]{qq.plot}} \cr \code{\link{plot}}
}
\keyword{distribution}
\keyword{hplot}
\keyword{models}
\keyword{multivariate}
\keyword{ts}
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