#' @title t-stats and plots for a fitted Fundamental Factor Model object
#'
#' @description
#' Calculate and plot the time series of t-statistic values and the
#' number of risk indices with significant t-stats for a fundamental factor
#' model of class \code{ffm} produced by \code{fitFfm} or \code{fitFfmDT}
#'
#' @importFrom data.table melt as.data.table
#' @importFrom zoo plot.zoo coredata as.yearmon
#' @importFrom lattice panel.abline xyplot panel.xyplot barchart
#' @importFrom grDevices dev.off
#' @importFrom stats vcov
#'
#' @param ffmObj an object of class \code{ffm} produced by \code{fitFfm}
#' @param isPlot logical. If \code{FALSE} no plots are displayed.
#' @param isPrint logical. if \code{TRUE}, the time series of the computed
#' factor model values is printed. Default is \code{FALSE}
#' @param whichPlot string indicating the plot(s) to be plotted. Choose from
#' ("all", "tStats", "significantTstatsV", "significantTstatsH", "significantTstatsLikert").
#' Three variants of significantTstats stand for vertical, horizontal and likert barplots.
#' Default is \code{tStats} plotting t-stats and significant t-stats with vertical bars.
#' @param color length 2 vector specifying the plotting color for t-stats plot
#' and for barplot respectively. default is \code{c("black", "cyan")}
#' @param lwd line width relative to the default. default is 2.
#' @param digits an integer indicating the number of decimal places to be used
#' for rounding. default is 2.
#' @param z.alpha critical value corresponding to the confidence interval.
#' Default is 1.96 i.e 95\% C.I
#' @param layout numeric vector of length 2 or 3 giving the number of columns,
#' rows, and pages (optional) in the xyplot of t-statistics. default is c(2,3).
#' @param type type character. Type of the xyplot of t-statistics; \code{"l"}
#' for lines, \code{"p"} for points, \code{"h"} for histogram like (or high-density)
#' vertical lines and \code{"b"} for both. Default is \code{"h"}.
#' @param scale character. It determines how axis limits are calculated for each
#' panel. Possible values are "same" , "free" (default) and "sliced".
#' @param stripText.cex a number indicating the amount by which strip text in
#' the plot(s) should be scaled relative to the default. 1=default, 1.5 is 50\%
#' larger, 0.5 is 50\% smaller, etc.
#' @param axis.cex a number indicating the amount by which axis in the plot(s)
#' should be scaled relative to the default. Default = 1. 1.5 is 50\% larger,
#' 0.5 is 50\% smaller, etc.
#' @param title logical. if \code{TRUE}, the plots will have the main title.
#' Default is \code{TRUE}.
#' @param ... potentially further arguments passed.
#'
#' @author Avinash Acharya and Doug Martin
#'
#' @return \code{fmTstats} plots the t-stats and significant t-stats values
#' if \code{isPlot} is \code{TRUE} and returns a list with following components:
#' \item{tstats}{ an xts object of t-stats values.}
#' \item{z.alpha}{ critical value corresponding to the confidence interval.}
#'
#' @examples
#'
#' data("factorDataSetDjia5Yrs")
#'
#' #Fit a Ffm with style factors only
#' fit <- fitFfm(data = factorDataSetDjia5Yrs,
#' exposure.vars = c("MKTCAP","ENTVAL","P2B","EV2S"),
#' date.var = "DATE",
#' ret.var = "RETURN",
#' asset.var = "TICKER",
#' fit.method = "WLS",
#' z.score = "crossSection")
#'
#' #Compute time series of t-stats and number of significant t-stats
#' stats = fmTstats(fit, isPlot = TRUE, lwd = 2, color = c("blue", "blue"),
#' z.alpha = 1.96)
#'
#' fit1 <- fitFfm(data = factorDataSetDjia5Yrs, asset.var = "TICKER",
#' exposure.vars = c("SECTOR","MKTCAP","ENTVAL","P2B"),
#' ret.var = "RETURN",
#' date.var = "DATE",
#' addIntercept = TRUE)
#'
#' #Compute time series of t-stats and number of significant t-stats
#' stats = fmTstats(fit1, isPlot = TRUE, z.alpha = 1.96)
#'
#' # Fit a SECTOR+COUNTRY+Style model with Intercept
#' # Create a COUNTRY column with just 3 countries
#' #
#' # factorDataSetDjia5Yrs$COUNTRY = rep(rep(c(rep("US", 1 ), rep("GERMANY", 1 )), 11), 60)
#' #
#' # fit.MICM <- fitFfm(data = factorDataSetDjia5Yrs,
#' # asset.var = "TICKER",
#' # exposure.vars = c("SECTOR", "COUNTRY","P2B", "MKTCAP"),
#' # ret.var = "RETURN",
#' # date.var = "DATE",
#' # addIntercept = FALSE)
#' #
#' # Load library 'HH' to access the Likert option
#' # library("HH")
#' # stats = fmTstats(fit.MICM, isPlot = TRUE, z.alpha =1.96,
#' # whichPlot = "significantTstatsLikert")
#' @rdname fmTstats
#' @export
fmTstats <- function(ffmObj, ...){
# check input object validity
if (!inherits(ffmObj, c("tsfm", "sfm", "ffm"))) {
stop("Invalid argument: Object should be of class 'tsfm', 'sfm' or 'ffm'.")
}
UseMethod("fmTstats")
}
#' @rdname fmTstats
#' @method fmTstats ffm
#' @export
#'
fmTstats.ffm<- function(ffmObj, isPlot = TRUE, isPrint = FALSE,
whichPlot = "tStats", color = c("black", "cyan"),
lwd = 2, digits =2, z.alpha = 1.96, layout = c(2, 3),
type = "h", scale = "free",
stripText.cex = 1, axis.cex = 1,
title = TRUE, ... ) {
# CREATE TIME SERIES OF T-STATS
time.periods = length(ffmObj$time.periods)
exposure.vars = ffmObj$exposure.vars
n.exposures = length(exposure.vars)
which.numeric <- sapply(ffmObj$data[ ,exposure.vars,drop = FALSE], is.numeric)
exposures.num <- exposure.vars[which.numeric]
exposures.char <- exposure.vars[!which.numeric]
n.expo.num <- length(exposures.num)
n.expo.char <- length(exposures.char)
if ( n.expo.char > 0 && grepl("Market|Alpha", ffmObj$factor.names[1]))
{ #Covaraince matrix for g coefficients.
cov.g = lapply(seq(time.periods), function(a) vcov((ffmObj$factor.fit)[[a]]))
restriction.mat = ffmObj$restriction.mat
#Number of factors in g except for the style factors
fac.num = ncol(restriction.mat)
# covarinace of f coefficeints: cov(f) = R*cov.g*t(R)
cov.factors = lapply(seq(time.periods), function(x) restriction.mat %*% cov.g[[x]][1:fac.num, 1:fac.num]
%*% t(restriction.mat))
std.errors = lapply(seq(time.periods), function(x) sqrt(diag(cov.factors[[x]])))
std.errors = matrix(unlist(std.errors), byrow = TRUE, nrow = time.periods)
fac.names.indcty = lapply(seq(n.expo.char), function(x)
paste0( levels(ffmObj$data[ ,exposures.char[x]])))
colnames(std.errors) <- c("Market", unlist(fac.names.indcty))
if(n.expo.num > 0)
{
#std.errs of stly factors
stdErr.sty = lapply(seq(time.periods), function(a)
summary(ffmObj)$sum.list[[a]]$coefficients[((fac.num+1):(fac.num+n.expo.num)),2])
stdErr.sty = matrix(unlist(stdErr.sty), byrow = TRUE, nrow = time.periods)
colnames(stdErr.sty) = exposures.num
#Should be in same order as that of factor.returns
std.errors = cbind(std.errors,stdErr.sty)
std.errors = std.errors[, colnames(ffmObj$factor.returns)]
}
#tstatsTs = ffmObj$factor.returns/std.errors
tstats = coredata(ffmObj$factor.returns/std.errors)
}else
{ tstats = lapply(seq(time.periods),
function(a) summary(ffmObj)$sum.list[[a]]$coefficients[,3])
secNames = names(tstats[[1]])
tstats = matrix(unlist(tstats), byrow = TRUE, nrow = time.periods)
colnames(tstats) = secNames
#tstatsTs = xts(tstats,order.by=as.yearmon(names(ffmObj$r2)))
}
tstatsTs = xts(tstats, order.by = as.yearmon(names(ffmObj$r2)))
# COUNT NUMBER OF RISK INDICES WITH SIGNIFICANT T-STATS EACH MONTH
#(Modified code using xts obj in ifelse to bypass bug in xts package v0.10)
sigTstats = as.matrix(rowSums(ifelse(abs(tstats) > z.alpha, 1, 0)))
sigTstatsTs = xts(sigTstats, order.by = as.yearmon(names(ffmObj$r2)))
pos.sigTstatsTs = as.matrix(colSums(ifelse((tstats) > z.alpha, 1, 0)))
#pos.sigTstatsTs = xts(pos.sigTstats,order.by=as.yearmon(names(ffmObj$r2)))
neg.sigTstatsTs = as.matrix(colSums(ifelse((tstats) < -z.alpha, 1, 0)))
Toal.sigTstats = as.matrix(colSums(ifelse(abs(tstats) > z.alpha, 1, 0)))
combined.sigTstats = cbind(neg.sigTstatsTs,pos.sigTstatsTs, Toal.sigTstats)
colnames(combined.sigTstats) = c("Negative", "Positive", "Total")
sum.significant = apply(combined.sigTstats, 2, FUN = sum)[[3]]
percent.sigTstats = as.data.frame((100/sum.significant)*combined.sigTstats[,-3])
#percent.sigTstats = rbind(percent.sigTstats,"TOTAL" = colSums(percent.sigTstats))
percent.sigTstats$var = rownames(combined.sigTstats)
if(isPlot)
{
panel = function(...){
panel.abline(h=z.alpha,lty = 3, col = "red")
panel.abline(h=-z.alpha,lty = 3, col = "red")
panel.xyplot(...)
}
if(whichPlot == "significantTstatsLikert")
{
plt = HH::likert(var ~ .,
percent.sigTstats,
scales = list(y = list(cex = stripText.cex),
x = list(cex = axis.cex)),
positive.order = TRUE,
between = list(y = 0),
strip = FALSE,
strip.left = FALSE,
#par.strip.text=list(cex=stripText.cex, lines=3),
main = "significant t-stats",
rightAxis = FALSE,
ylab = NULL,
xlab = 'Total significance %')
print(plt)
}
if(whichPlot == "significantTstatsH")
{
combined.sigTstatsH = combined.sigTstats[ ,c(3, 1, 2)]
mydata = data.table::as.data.table( t(combined.sigTstatsH))
id <- NULL # due to NSE notes in R CMD check
mydata$id <- c("Total", "Negative","Positive")
mydata$id = factor(mydata$id , levels = c("Total", "Negative","Positive"))
dat <- data.table::melt(mydata, id.vars = "id")
my.settings <- list(
superpose.polygon = list(col = c("grey", "red","black"),
border = "transparent"),
strip.border = list(col = "black")
)
plt = barchart(~value|variable, group = (id), data = dat,
par.settings = my.settings, layout = layout,
main = "Significant t-stats", ylab = "Type",
xlab="Total significance %",
auto.key = list(space = "right",
points = FALSE,
rectangles = TRUE,
title = "Significant type",
cex.title =1 ),
scales = list(y = list(cex = axis.cex),
x = list(cex = axis.cex)),
par.strip.text = list(col = "black", font = 2,
cex = stripText.cex))
print(plt)
}
if(whichPlot == "all" | whichPlot == "significantTstatsV")
{
combined.sigTstatsV = combined.sigTstats[ ,c(3,1,2)]
mydata = data.table::as.data.table( t(combined.sigTstatsV))
mydata$id <- c("Total", "Negative", "Positive")
mydata$id = factor(mydata$id, levels = c("Positive", "Negative", "Total"))
dat <- data.table::melt(mydata, id.vars = "id")
my.settings <- list(superpose.polygon = list(col = c("black", "red", "grey"),
border = "transparent"),
strip.border = list(col = "black")
)
plt = barchart(value~(id)|variable, group = (id), data = dat, origin = 0,
stack = TRUE, main = "Significant t-stats", xlab = "Type",
ylab = "Total significance %",
par.settings = my.settings, layout = layout,
auto.key = list(space = "right",
points = FALSE,
rectangles = TRUE,
title = "Significant type",
cex.title = 1),
scales = list(y = list(cex = axis.cex),
x = list(cex = axis.cex)),
par.strip.text = list(col = "black",
font = 2,
cex = stripText.cex))
print(plt)
}
if(whichPlot == "all" | whichPlot == "tStats")
{
#par(mfrow= c(3,1))
# PLOT T-STATS WITH XYPLOT
if(title) title.tstats = "t statistic values " else title.tstats = " "
plt <- xyplot(tstatsTs, panel = panel, type = type,
scales = list(y = list(cex = axis.cex, relation = scale),
x = list(cex = axis.cex)),
layout = layout, main = title.tstats,
col = color[1], lwd = lwd,
strip.left = T, strip = F,
par.strip.text = list(col = "black", cex = stripText.cex))
print(plt)
#par(mfrow= c(1,1))
}
}
out = list("tstats" =round(tstatsTs, digits), "z.alpha" = z.alpha)
if(isPrint){print(out)} else invisible(out)
}
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