[Returnanalytics-commits] r3618 - in pkg/FactorAnalytics: . R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri Mar 20 00:01:59 CET 2015
Author: chenyian
Date: 2015-03-20 00:01:59 +0100 (Fri, 20 Mar 2015)
New Revision: 3618
Modified:
pkg/FactorAnalytics/DESCRIPTION
pkg/FactorAnalytics/R/fitTsfmUpDn.r
pkg/FactorAnalytics/R/plot.tsfm.r
pkg/FactorAnalytics/R/plot.tsfmUpDn.r
pkg/FactorAnalytics/R/predict.tsfmUpDn.r
pkg/FactorAnalytics/man/fitTsfmUpDn.Rd
pkg/FactorAnalytics/man/plot.tsfmUpDn.Rd
Log:
version 2.0.14
1. Add single market factor line and Robust up/dn fitted line for plot.tsfmUpDn.r.
2. Change the corresponding plot.tsfmUpDn.Rd file.
3. Add a new legend.
Modified: pkg/FactorAnalytics/DESCRIPTION
===================================================================
--- pkg/FactorAnalytics/DESCRIPTION 2015-03-16 20:41:50 UTC (rev 3617)
+++ pkg/FactorAnalytics/DESCRIPTION 2015-03-19 23:01:59 UTC (rev 3618)
@@ -1,36 +1,36 @@
-Package: factorAnalytics
-Type: Package
-Title: Factor Analytics
-Version: 2.0.14
-Date: 2015-03-15
-Author: Eric Zivot, Sangeetha Srinivasan and Yi-An Chen
-Maintainer: Sangeetha Srinivasan <sangee at uw.edu>
-Description: An R package for the estimation and risk analysis of linear factor
- models for asset returns and portfolios. It contains model fitting methods
- for the three major types of factor models: time series (or, macroeconomic)
- factor model, fundamental factor model and statistical factor model. They
- allow for different types of distributions to be specified for modeling the
- fat-tailed behavior of financial returns, including Edgeworth expansions.
- Risk analysis measures such as VaR and ES, as well as performance
- attribution for factor models (factor-contributed vs idiosyncratic returns)
- are included.
-License: GPL-2
-Depends:
- R (>= 3.0.0),
- xts (>= 0.9)
-Imports:
- PerformanceAnalytics(>= 1.1.0),
- corrplot,
- robust,
- leaps,
- lars,
- strucchange,
- lmtest,
- sandwich,
- lattice,
- MASS
-Suggests:
- testthat, quantmod, knitr
-LazyLoad: yes
-LazyDataCompression: xz
-URL: http://r-forge.r-project.org/R/?group_id=579
+Package: factorAnalytics
+Type: Package
+Title: Factor Analytics
+Version:2.0.14
+Date:2015-03-19
+Author: Eric Zivot, Sangeetha Srinivasan and Yi-An Chen
+Maintainer: Sangeetha Srinivasan <sangee at uw.edu>
+Description: An R package for the estimation and risk analysis of linear factor
+ models for asset returns and portfolios. It contains model fitting methods
+ for the three major types of factor models: time series (or, macroeconomic)
+ factor model, fundamental factor model and statistical factor model. They
+ allow for different types of distributions to be specified for modeling the
+ fat-tailed behavior of financial returns, including Edgeworth expansions.
+ Risk analysis measures such as VaR and ES, as well as performance
+ attribution for factor models (factor-contributed vs idiosyncratic returns)
+ are included.
+License: GPL-2
+Depends:
+ R (>= 3.0.0),
+ xts (>= 0.9)
+Imports:
+ PerformanceAnalytics(>= 1.1.0),
+ corrplot,
+ robust,
+ leaps,
+ lars,
+ strucchange,
+ lmtest,
+ sandwich,
+ lattice,
+ MASS
+Suggests:
+ testthat, quantmod, knitr
+LazyLoad: yes
+LazyDataCompression: xz
+URL: http://r-forge.r-project.org/R/?group_id=579
Modified: pkg/FactorAnalytics/R/fitTsfmUpDn.r
===================================================================
--- pkg/FactorAnalytics/R/fitTsfmUpDn.r 2015-03-16 20:41:50 UTC (rev 3617)
+++ pkg/FactorAnalytics/R/fitTsfmUpDn.r 2015-03-19 23:01:59 UTC (rev 3618)
@@ -39,9 +39,13 @@
#' \code{fitted}, \code{residuals} and \code{fmCov} can be applied as well.
#'
#' An object of class \code{"tsfmUpDn"} is a list containing \code{Up} and \code{Dn}:
-#' \item{Up}{An object of \code{tsfm} fitted by \code{fitTsfm} for the up market.}
-#' \item{Dn}{An object of \code{tsfm} fitted by \code{fitTsfm} for the down market.}
+#' \item{Up}{An object of \code{tsfm} fitted by \code{fitTsfm} for the up market;}
+#' \item{Dn}{An object of \code{tsfm} fitted by \code{fitTsfm} for the down market;}
#'
+#' and others useful items:
+#' \item{call}{Function call.}
+#' \item{data}{Original data used but converted to \code{xts} class.}
+#'
#' Each object of \code{tsfm} contains :
#' \item{asset.fit}{list of fitted objects for each asset. Each object is of
#' class \code{lm} if \code{fit.method="OLS" or "DLS"}, class \code{lmRob} if
@@ -100,10 +104,11 @@
data=data, fit.method=c("OLS","DLS","Robust"),
control=fitTsfm.control(...),...) {
+ call <- match.call()
+
if (is.null(mkt.name)){
stop("Missing argument: mkt.name has to be specified for up and down market model.")
}
-
# convert data into an xts object and hereafter work with xts objects
data.xts <- checkData(data)
@@ -114,7 +119,7 @@
dat.xts <- merge(data.xts[,asset.names], data.xts[,mkt.name])
### After merging xts objects, the spaces in names get converted to periods
- # convert all asset and factor returns to excess return form if specified
+ # convert all asset and factor returns to excess returns if specified
if (!is.null(rf.name)) {
dat.xts <- "[<-"(dat.xts,,vapply(dat.xts, function(x) x-data.xts[,rf.name],
FUN.VALUE = numeric(nrow(dat.xts))))
@@ -124,18 +129,16 @@
# up market
dataUp.xts <- dat.xts[mkt >= 0]
- fitUp <- fitTsfm(asset.names=asset.names,factor.names=mkt.name,mkt.name=mkt.name,rf.name=rf.name,
- data=dataUp.xts,fit.method=fit.method,variable.selection="none",
- control=control)
+ fitUp <- fitTsfm(asset.names=asset.names,factor.names=mkt.name,mkt.name=mkt.name,rf.name=NULL,
+ data=dataUp.xts,fit.method=fit.method,control=control)
# down market
dataDn.xts <- dat.xts[mkt < 0]
- fitDn <- fitTsfm(asset.names=asset.names,factor.names=mkt.name,mkt.name=mkt.name,rf.name=rf.name,
- data=dataDn.xts,fit.method=fit.method,variable.selection="none",
- control=control)
+ fitDn <- fitTsfm(asset.names=asset.names,factor.names=mkt.name,mkt.name=mkt.name,rf.name=NULL,
+ data=dataDn.xts,fit.method=fit.method,control=control)
- result <- list(Up = fitUp, Dn = fitDn)
+ result <- list(Up = fitUp, Dn = fitDn, call= call, data=dat.xts)
class(result) <- "tsfmUpDn"
return(result)
}
Modified: pkg/FactorAnalytics/R/plot.tsfm.r
===================================================================
--- pkg/FactorAnalytics/R/plot.tsfm.r 2015-03-16 20:41:50 UTC (rev 3617)
+++ pkg/FactorAnalytics/R/plot.tsfm.r 2015-03-19 23:01:59 UTC (rev 3618)
@@ -1,462 +1,464 @@
-#' @title Plots from a fitted time series factor model
-#'
-#' @description Generic \code{plot} method for object of class \code{tsfm}.
-#' Plots chosen characteristic(s) for one or more assets.
-#'
-#' @details
-#'
-#' The function can be used for group plots and individual plots. User can
-#' select the type of plot either from the menu prompt (default) or directly
-#' via argument \code{which}.
-#'
-#' In case multiple plots are needed, the menu is repeated after each plot
-#' (enter 0 to exit). User can also input a numeric vector of plot options via
-#' \code{which}.
-#'
-#' For group plots (the default), the first \code{max.show} assets are plotted.
-#'
-#' Setting \code{plot.single=TRUE} enables individual plots. If there is more
-#' than one asset fit by \code{x}, \code{asset.name} should be specified.
-#' However, if the \code{tsfm} object \code{x} only contains one asset’s factor
-#' model fit, plot.tsfm can infer \code{asset.name} without user input.
-#'
-#' CUSUM plots (individual asset plot options 10, 11 and 12) are applicable
-#' only for \code{fit.method="OLS"}.
-#'
-#' Rolling estimates (individual asset plot option 13) is not applicable for
-#' \code{variable.slection="lars"}.
-#'
-#' @param x an object of class \code{tsfm} produced by \code{fitTsfm}.
-#' @param which a number to indicate the type of plot. If a subset of the plots
-#' is required, specify a subset of the numbers 1:10 for group plots and 1:16
-#' for individual plots. If \code{which=NULL} (default), the following menu
-#' appears: \cr \cr
-#' For plots of a group of assets: \cr
-#' 1 = Factor model coefficients: Alpha, \cr
-#' 2 = Factor model coefficients: Betas, \cr
-#' 3 = Actual and Fitted asset returns, \cr
-#' 4 = R-squared, \cr
-#' 5 = Residual Volatility,\cr
-#' 6 = Factor Model Residual Correlation \cr
-#' 7 = Factor Model Correlation,\cr
-#' 8 = Factor Contribution to SD,\cr
-#' 9 = Factor Contribution to ES,\cr
-#' 10 = Factor Contribution to VaR \cr \cr
-#' For individual asset plots:\cr
-#' 1 = Actual and fitted returns,\cr
-#' 2 = Residuals and fitted returns, \cr
-#' 3 = Scale-Location plot, \cr
-#' 4 = Residuals with standard error bands, \cr
-#' 5 = Time series of squared residuals, \cr
-#' 6 = Time series of absolute residuals,\cr
-#' 7 = SACF and PACF of residuals,\cr
-#' 8 = SACF and PACF of squared residuals,\cr
-#' 9 = SACF and PACF of absolute residuals,\cr
-#' 10 = Density estimate of residuals, \cr
-#' 11 = Histogram of residuals with normal curve overlayed,\cr
-#' 12 = Normal QQ-plot of residuals,\cr
-#' 13 = CUSUM test-Recursive residuals,\cr
-#' 14 = CUSUM test-OLS residuals,\cr
-#' 15 = Recursive estimates (RE) test of OLS regression coefficients,\cr
-#' 16 = Rolling estimates over a 24-period observation window
-#' @param max.show maximum number of assets in a given plot. Default is 6.
-#' @param plot.single logical; If \code{TRUE} plots the characteristics of an
-#' individual asset's factor model. The type of plot is given by
-#' \code{which}. Default is \code{FALSE}.
-#' @param asset.name name of the individual asset to be plotted. Is necessary
-#' if \code{x} contains multiple asset fits and \code{plot.single=TRUE}.
-#' @param colorset color palette to use for all the plots. Default is
-#' \code{c(1:12)}. The 1st element will be used for individual time series
-#' plots or the 1st series plotted, the 2nd element for the 2nd object in the
-#' plot and so on.
-#' @param legend.loc places a legend into one of nine locations on the chart:
-#' "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright",
-#' "right", or "center". Default is "bottomright". Use \code{legend.loc=NULL}
-#' to suppress the legend.
-#' @param las one of {0, 1, 2, 3} to set the direction of axis labels, same as
-#' in \code{plot}. Default here is 1.
-#' @param VaR.method a method for computing VaR; one of "modified", "gaussian",
-#' "historical" or "kernel". VaR is computed using
-#' \code{\link[PerformanceAnalytics]{VaR}}. Default is "historical".
-#' @param ... further arguments to be passed to other plotting functions.
-#'
-#' @author Eric Zivot, Sangeetha Srinivasan and Yi-An Chen
-#'
-#' @seealso \code{\link{fitTsfm}} and \code{\link{summary.tsfm}} for details
-#' about the time series factor model fit, extractor functions and summary
-#' statistics.
-#'
-#' \code{\link[strucchange]{efp}} for CUSUM tests.
-#'
-#' \code{\link[xts]{plot.xts}},
-#' \code{\link[PerformanceAnalytics]{chart.TimeSeries}},
-#' \code{\link[PerformanceAnalytics]{chart.ACFplus}},
-#' \code{\link[PerformanceAnalytics]{chart.Histogram}},
-#' \code{\link[PerformanceAnalytics]{chart.QQPlot}},
-#' \code{\link[graphics]{barplot}}, \code{\link[lattice]{barchart}} and
-#' \code{\link[corrplot]{corrplot}} for plotting methods used.
-#'
-#' \code{\link{fmSdDecomp}}, \code{\link{fmEsDecomp}},
-#' \code{\link{fmVaRDecomp}} for factor model risk measures.
-#'
-#' @examples
-#'
-#' # load data from the database
-#' data(managers)
-#' fit.macro <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
-#' factor.names=colnames(managers[,(7:8)]),
-#' rf.name="US.3m.TR", data=managers)
-#'
-#' # for group plots (default), user can select type from menu prompt
-#' # menu is repeated to get multiple types of plots based on the same fit
-#' # plot(fit.macro)
-#'
-#' # plot specific option(s) though which
-#' # plot the factor betas of 1st 4 assets fitted above
-#' plot(fit.macro, max.show=4, which=2)
-#'
-#' # plot factor model return correlation; angular order of the eigenvectors
-#' plot(fit.macro, which=7, order="AOE", method="ellipse", tl.pos = "d")
-#'
-#' # for individual plots: set plot.single=TRUE; specify asset.name
-#' # histogram of residuals from an individual asset's factor model fit
-#' plot(fit.macro, plot.single=TRUE, asset.name="HAM1", which=8)
-#'
-#' @importFrom PerformanceAnalytics chart.TimeSeries chart.ACFplus
-#' chart.Histogram chart.QQPlot
-#' @importFrom lattice barchart panel.barchart panel.grid
-#' @importFrom corrplot corrplot
-#' @importFrom strucchange efp
-#' @importFrom robust lmRob
-#'
-#' @method plot tsfm
-#' @export
-
-plot.tsfm <- function(x, which=NULL, max.show=6, plot.single=FALSE,
- asset.name, colorset=(1:12), legend.loc="topleft", las=1,
- VaR.method="historical", ...) {
-
- which.vec <- which
- which <- which[1]
-
- if (plot.single==TRUE) {
-
- if (missing(asset.name) && length(x$asset.names)>1) {
- stop("Missing input: 'asset.name' is required if plot.single is TRUE and
- the factor model fits multiple assets.")
- } else if (length(x$asset.names)==1) {
- i <- x$asset.names[1]
- } else {
- i <- asset.name
- }
- # extract info from the fitTsfm object
- plotData <- merge.xts(x$data[,i], fitted(x)[,i])
- colnames(plotData) <- c("Actual","Fitted")
- Residuals <- residuals(x)[,i]
- plotData2 <- merge.xts(Residuals, fitted(x)[,i])
- colnames(plotData2) <- c("Residuals","Fitted")
- fit <- x$asset.fit[[i]]
-
- # plot selection
- repeat {
- if (is.null(which)) {
- which <-
- menu(c("Actual vs fitted asset returns",
- "Residuals vs fitted asset returns",
- "Scale-Location plot",
- "Residuals with standard error bands",
- "Time series of squared residuals",
- "Time series of absolute residuals",
- "SACF and PACF of residuals",
- "SACF and PACF of squared residuals",
- "SACF and PACF of absolute residuals",
- "Density Estimate of Residuals",
- "Histogram of residuals with normal curve overlayed",
- "Normal qq-plot of residuals",
- "CUSUM test-Recursive residuals",
- "CUSUM test-OLS residuals",
- "Recursive estimates (RE) test of OLS regression coefficients",
- "Rolling estimates over a 24-period observation window"),
- title="\nMake a plot selection (or 0 to exit):")
- }
-
- par(las=las) # default horizontal axis labels
-
- switch(which,
- "1L" = {
- ## time series plot of actual and fitted asset returns
- chart.TimeSeries(plotData, main=paste("Actual and Fitted:",i),
- colorset=colorset, xlab="", ylab="Asset returns",
- legend.loc=legend.loc, pch=NULL, las=las, ...)
- }, "2L" = {
- ## residuals vs fitted asset returns
- plot(fit, which=1, main=paste("Residuals vs Fitted Returns:",i),
- caption=NA, sub.caption="", las=las, ...)
- }, "3L" = {
- ## square root of absolute residuals vs fitted asset returns
- plot(fit, which=3, main=paste("Scale-Location plot:",i),
- caption=NA, sub.caption="", las=las, ...)
- }, "4L" = {
- ## time series plot of residuals with standard error bands
- chart.TimeSeries(Residuals, main=paste("Residuals:",i),
- colorset=colorset, xlab="", ylab="Residuals",
- lwd=2, lty="solid", las=las, ...)
- abline(h=1.96*x$resid.sd[i], lwd=2, lty="dotted", col="red")
- abline(h=-1.96*x$resid.sd[i], lwd=2, lty="dotted", col="red")
- legend(x=legend.loc, lty=c("solid","dotted"),
- col=c(colorset[1],"red"), lwd=2,
- legend=c("Residuals",expression("\u00b1 1.96"*sigma)))
- }, "5L" = {
- ## time series plot of squared residuals
- chart.TimeSeries(Residuals^2, colorset=colorset, xlab="",
- ylab=" Squared Residuals",
- main=paste("Squared Residuals:",i),
- legend.loc=legend.loc, pch=NULL, las=las, ...)
- }, "6L" = {
- ## time series plot of absolute residuals
- chart.TimeSeries(abs(Residuals), colorset=colorset, xlab="",
- ylab="Absolute Residuals",
- main=paste("Absolute Residuals:",i),
- legend.loc=legend.loc, pch=NULL, las=las, ...)
- }, "7L" = {
- ## SACF and PACF of residuals
- chart.ACFplus(Residuals, col=colorset[1],
- main=paste("SACF & PACF - Residuals:",i), ...)
- }, "8L" = {
- ## SACF and PACF of squared residuals
- chart.ACFplus(Residuals^2, col=colorset[1], ...,
- main=paste("SACF & PACF - Squared residuals:",i))
- }, "9L" = {
- ## SACF and PACF of absolute residuals
- chart.ACFplus(abs(Residuals), col=colorset[1], ...,
- main=paste("SACF & PACF - Absolute Residuals:",i))
- }, "10L" = {
- ## density estimate of residuals
- plot(density(Residuals), xlab="Return residuals",
- colorset=colorset, main=paste("Density estimate of Residuals:",i), ...)
- rug(Residuals)
- }, "11L" = {
- ## histogram of residuals with normal curve overlayed
- methods <- c("add.density","add.normal","add.rug")
- chart.Histogram(Residuals, xlab="Return residuals",
- methods=methods, colorset=colorset,
- main=paste("Histogram of Residuals:",i), ...)
- }, "12L" = {
- ## normal qq-plot of residuals
- chart.QQPlot(Residuals, envelope=0.95, col=colorset,
- main=paste("QQ-plot of Residuals:",i), ...)
- legend(x=legend.loc, col="red", lty="dotted", lwd=1,
- legend=c("0.95 confidence envelope"))
- }, "13L" = {
- ## Recursive CUSUM test
- if (!x$fit.method=="OLS") {
- stop("CUSUM analysis applicable only for 'OLS' fit.method.")
- }
- cusum.rec <- efp(formula(fit), type="Rec-CUSUM", data=fit$model)
- plot(cusum.rec, main=paste("Recursive CUSUM test:",i), las=las,
- col=colorset, ...)
- }, "14L" = {
- ## OLS-based CUSUM test
- if (!x$fit.method=="OLS") {
- stop("CUSUM analysis applicable only for 'OLS' fit.method.")
- }
- cusum.ols <- efp(formula(fit), type="OLS-CUSUM", data=fit$model)
- plot(cusum.ols, main=paste("OLS-based CUSUM test:",i), las=las,
- col=colorset, ...)
- }, "15L" = {
- ## Recursive estimates (RE) test of OLS regression coefficients
- if (!x$fit.method=="OLS") {
- stop("CUSUM analysis applicable only for 'OLS' fit.method.")
- }
- cusum.est <- efp(formula(fit), type="RE", data=fit$model)
- plot(cusum.est, functional=NULL, col=colorset, las=0,
- main=paste("RE test (Recursive estimates test):",i), ...)
- }, "16L" = {
- ## Rolling estimates over 24-period observation window
- if (x$fit.method=="OLS") {
- rollReg <- function(data.z, formula) {
- coef(lm(formula, data=as.data.frame(data.z)))
- }
- reg.z <- zoo(fit$model, as.Date(rownames(fit$model)))
- rollReg.z <- rollapply(reg.z, FUN=rollReg, formula(fit),
- width=24, by.column=FALSE, align="right")
- } else if (x$fit.method=="DLS") {
- # get decay factor
- if (as.character(x$call["decay"])=="NULL") {
- decay <- 0.95 # default value for the decay factor
- } else {
- decay <- as.numeric(as.character(x$call["decay"]))
- }
- # calculate exp. decaying weights for 24-period window
- w <- decay^seq(23,0,-1)
- w <- w/sum(w) # weights sum to unity
- rollReg.w <- function(data.z, formula, w) {
- coef(lm(formula, weights=w, data=as.data.frame(data.z)))
- }
- reg.z <- zoo(fit$model[-length(fit$model)],
- as.Date(rownames(fit$model)))
- rollReg.z <- rollapply(reg.z, FUN=rollReg.w, formula(fit), w,
- width=24, by.column=FALSE, align="right")
- } else if (x$fit.method=="Robust") {
- rollReg.Rob <- function(data.z, formula) {
- coef(lmRob(formula=formula, data=as.data.frame(data.z)))
- }
- reg.z <- zoo(fit$model, as.Date(rownames(fit$model)))
- rollReg.z <- rollapply(reg.z, width=24, FUN=rollReg.Rob,
- formula(fit), by.column=FALSE,
- align="right")
- } else if (is.null(x$fit.method)) {
- stop("Rolling estimates is not available for 'lars' fits.")
- }
- par(las=0)
- plot(rollReg.z, ..., las=las,
- main=paste("Rolling estimates (24-period obs window):",i))
- par(las=las)
- },
- invisible()
- )
- # repeat menu if user didn't choose to exit from the plot options
- if (which==0 || length(which.vec)==1) {break}
- if (length(which.vec)>1) {
- which.vec <- which.vec[-1]
- which <- which.vec[1]
- par(ask=TRUE)
- } else {which=NULL}
- }
- } else { # start of group asset plots
-
- # plot selection
- repeat {
- if (is.null(which)) {
- which <-
- menu(c("Factor model coefficients: Alpha",
- "Factor model coefficients: Betas",
- "Actual and Fitted asset returns",
- "R-squared",
- "Residual Volatility",
- "Factor Model Residual Correlation",
- "Factor Model Return Correlation",
- "Factor Contribution to SD",
- "Factor Contribution to ES",
- "Factor Contribution to VaR"),
- title="\nMake a plot selection (or 0 to exit):")
- }
-
- par(las=las) # default horizontal axis labels
-
- switch(which,
- "1L" = {
- ## Factor model coefficients: Alpha
- barplot(coef(x)[,1], main="Factor model Alpha (Intercept)",
- names.arg=rownames(coef(x)), col="darkblue", las=las,
- horiz=TRUE, ...)
- abline(v=0, lwd=1, lty=1, col=1)
- },
- "2L" = {
- ## Factor model coefficients: Betas
- k <- ncol(coef(x))-1
- if (k > max.show) {
- cat(paste("Displaying only the first", max.show,"factor betas, as the number of factors > 'max.show' =", max.show))
- k <- max.show
- }
- par(mfrow=c(ceiling(k/2),2))
- for (i in 2:(k+1)) {
- main=paste(colnames(coef(x))[i], "factor Betas")
- barplot(coef(x)[,i], main=main, names.arg=rownames(coef(x)),
- col="darkblue", las=las, horiz=TRUE, ...)
- abline(v=0, lwd=1, lty=1, col=1)
- }
- par(mfrow=c(1,1))
- },
- "3L" = {
- ## Actual and Fitted asset returns
- n <- length(x$asset.names)
- if (n > max.show) {
- cat(paste("Displaying only the first", max.show, "assets, since the number of assets > 'max.show'"))
- n <- max.show
- }
- par(mfrow=c(ceiling(n/2),2))
- for (i in 1:n) {
- plotData <- merge.xts(x$data[,i], fitted(x)[,i])
- colnames(plotData) <- c("Actual","Fitted")
- main <- paste("Actual vs Fitted:", x$asset.names[i])
- chart.TimeSeries(plotData, colorset=colorset, main=main,
- xlab="", ylab="Asset returns",
- legend.loc=legend.loc, pch=NULL, las=las,...)
- }
- par(mfrow=c(1,1))
- },
- "4L" ={
- ## R-squared
- plot(
- barchart(x$r2, main="R-squared values", xlab="",
- col="darkblue", ...)
- )
- },
- "5L" = {
- ## Residual Volatility
- plot(
- barchart(x$resid.sd, main="Residual volatility", xlab="",
- col="darkblue", ...)
- )
- },
- "6L" = {
- ## Factor Model Residual Correlation
- cor.resid <- cor(residuals(x), use="pairwise.complete.obs")
- corrplot(cor.resid, ...)
- # mtext("pairwise complete obs", line=0.5)
- },
- "7L" = {
- ## Factor Model Return Correlation
- cor.fm <- cov2cor(fmCov(x))
- corrplot(cor.fm, ...)
- # mtext("pairwise complete obs", line=0.5)
- },
- "8L" = {
- ## Factor Percentage Contribution to SD
- pcSd.fm <- fmSdDecomp(x)$pcSd
- plot(
- barchart(pcSd.fm, main="Factor % Contribution to SD", xlab="",
- auto.key=list(space="bottom",columns=3,
- points=FALSE,rectangles=TRUE),
- par.settings=list(superpose.polygon=list(col=colorset)),
- panel=function(...){panel.grid(h=0, v=-1);
- panel.barchart(...)}, ...)
- )
- },
- "9L"={
- ## Factor Percentage Contribution to ES
- pcES.fm <- fmEsDecomp(x, method=VaR.method)$pcES
- plot(
- barchart(pcES.fm, main="Factor % Contribution to ES", xlab="",
- auto.key=list(space="bottom",columns=3,
- points=FALSE,rectangles=TRUE),
- par.settings=list(superpose.polygon=list(col=colorset)),
- panel=function(...){panel.grid(h=0, v=-1);
- panel.barchart(...)}, ...)
- )
- },
- "10L" ={
- ## Factor Percentage Contribution to VaR
- pcVaR.fm <- fmVaRDecomp(x, method=VaR.method)$pcVaR
- plot(
- barchart(pcVaR.fm, main="Factor % Contribution to VaR",
- xlab="", auto.key=list(space="bottom",columns=3,
- points=FALSE,rectangles=TRUE),
- par.settings=list(superpose.polygon=list(col=colorset)),
- panel=function(...){panel.grid(h=0, v=-1);
- panel.barchart(...)}, ...)
- )
- },
- invisible()
- )
- # repeat menu if user didn't choose to exit from the plot options
- if (which==0 || length(which.vec)==1) {break}
- if (length(which.vec)>1) {
- which.vec <- which.vec[-1]
- which <- which.vec[1]
- par(ask=TRUE)
- } else {which=NULL}
- }
- } # end of group plots
-}
+#' @title Plots from a fitted time series factor model
+#'
+#' @description Generic \code{plot} method for object of class \code{tsfm}.
+#' Plots chosen characteristic(s) for one or more assets.
+#'
+#' @details
+#'
+#' The function can be used for group plots and individual plots. User can
+#' select the type of plot either from the menu prompt (default) or directly
+#' via argument \code{which}.
+#'
+#' In case multiple plots are needed, the menu is repeated after each plot
+#' (enter 0 to exit). User can also input a numeric vector of plot options via
+#' \code{which}.
+#'
+#' For group plots (the default), the first \code{max.show} assets are plotted.
+#'
+#' Setting \code{plot.single=TRUE} enables individual plots. If there is more
+#' than one asset fit by \code{x}, \code{asset.name} should be specified.
+#' However, if the \code{tsfm} object \code{x} only contains one asset’s factor
+#' model fit, plot.tsfm can infer \code{asset.name} without user input.
+#'
+#' CUSUM plots (individual asset plot options 10, 11 and 12) are applicable
+#' only for \code{fit.method="OLS"}.
+#'
+#' Rolling estimates (individual asset plot option 13) is not applicable for
+#' \code{variable.slection="lars"}.
+#'
+#' @param x an object of class \code{tsfm} produced by \code{fitTsfm}.
+#' @param which a number to indicate the type of plot. If a subset of the plots
+#' is required, specify a subset of the numbers 1:10 for group plots and 1:16
+#' for individual plots. If \code{which=NULL} (default), the following menu
+#' appears: \cr \cr
+#' For plots of a group of assets: \cr
+#' 1 = Factor model coefficients: Alpha, \cr
+#' 2 = Factor model coefficients: Betas, \cr
+#' 3 = Actual and Fitted asset returns, \cr
+#' 4 = R-squared, \cr
+#' 5 = Residual Volatility,\cr
+#' 6 = Factor Model Residual Correlation \cr
+#' 7 = Factor Model Correlation,\cr
+#' 8 = Factor Contribution to SD,\cr
+#' 9 = Factor Contribution to ES,\cr
+#' 10 = Factor Contribution to VaR \cr \cr
+#' For individual asset plots:\cr
+#' 1 = Actual and fitted returns,\cr
+#' 2 = Residuals and fitted returns, \cr
+#' 3 = Scale-Location plot, \cr
+#' 4 = Residuals with standard error bands, \cr
+#' 5 = Time series of squared residuals, \cr
+#' 6 = Time series of absolute residuals,\cr
+#' 7 = SACF and PACF of residuals,\cr
+#' 8 = SACF and PACF of squared residuals,\cr
+#' 9 = SACF and PACF of absolute residuals,\cr
+#' 10 = Density estimate of residuals, \cr
+#' 11 = Histogram of residuals with normal curve overlayed,\cr
+#' 12 = Normal QQ-plot of residuals,\cr
+#' 13 = CUSUM test-Recursive residuals,\cr
+#' 14 = CUSUM test-OLS residuals,\cr
+#' 15 = Recursive estimates (RE) test of OLS regression coefficients,\cr
+#' 16 = Rolling estimates over a 24-period observation window
+#' @param max.show maximum number of assets in a given plot. Default is 6.
+#' @param plot.single logical; If \code{TRUE} plots the characteristics of an
+#' individual asset's factor model. The type of plot is given by
+#' \code{which}. Default is \code{FALSE}.
+#' @param asset.name name of the individual asset to be plotted. Is necessary
+#' if \code{x} contains multiple asset fits and \code{plot.single=TRUE}.
+#' @param colorset color palette to use for all the plots. Default is
+#' \code{c(1:12)}. The 1st element will be used for individual time series
+#' plots or the 1st series plotted, the 2nd element for the 2nd object in the
+#' plot and so on.
+#' @param legend.loc places a legend into one of nine locations on the chart:
+#' "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright",
+#' "right", or "center". Default is "bottomright". Use \code{legend.loc=NULL}
+#' to suppress the legend.
+#' @param las one of {0, 1, 2, 3} to set the direction of axis labels, same as
+#' in \code{plot}. Default here is 1.
+#' @param VaR.method a method for computing VaR; one of "modified", "gaussian",
+#' "historical" or "kernel". VaR is computed using
+#' \code{\link[PerformanceAnalytics]{VaR}}. Default is "historical".
+#' @param ... further arguments to be passed to other plotting functions.
+#'
+#' @author Eric Zivot, Sangeetha Srinivasan and Yi-An Chen
+#'
+#' @seealso \code{\link{fitTsfm}} and \code{\link{summary.tsfm}} for details
+#' about the time series factor model fit, extractor functions and summary
+#' statistics.
+#'
+#' \code{\link[strucchange]{efp}} for CUSUM tests.
+#'
+#' \code{\link[xts]{plot.xts}},
+#' \code{\link[PerformanceAnalytics]{chart.TimeSeries}},
+#' \code{\link[PerformanceAnalytics]{chart.ACFplus}},
+#' \code{\link[PerformanceAnalytics]{chart.Histogram}},
+#' \code{\link[PerformanceAnalytics]{chart.QQPlot}},
+#' \code{\link[graphics]{barplot}}, \code{\link[lattice]{barchart}} and
+#' \code{\link[corrplot]{corrplot}} for plotting methods used.
+#'
+#' \code{\link{fmSdDecomp}}, \code{\link{fmEsDecomp}},
+#' \code{\link{fmVaRDecomp}} for factor model risk measures.
+#'
+#' @examples
+#'
+#' # load data from the database
+#' data(managers)
[TRUNCATED]
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svnlook diff /svnroot/returnanalytics -r 3618
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