Description Usage Arguments Value Author(s) Examples
Calculate and plot the time series of the t-statistic values and the number of risk indices with significant t-stats for a fundamentally fit object.
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ffmObj |
an object of class |
... |
potentially further arguments passed. |
isPlot |
logical. If |
isPrint |
logical. if |
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 |
color |
length 2 vector specifying the plotting color for t-stats plot and for barplot
respectively. default is |
lwd |
line width relative to the default. default is 2. |
digits |
an integer indicating the number of decimal places to be used for rounding. default is 2. |
z.alpha |
critical value corresponding to the confidence interval. default is 1.96 i.e 95% C.I |
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). |
type |
character. Type of the xyplot of t-statistics; |
scale |
character. It determines how axis limits are calculated for each panel. Possible values are "same" , "free" (default) and "sliced". |
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. |
axis.cex |
a number indicating the amount by which axis in the plot(s) should be scaled relative to the default. 1=default, 1.5 is 50% larger, 0.5 is 50% smaller, etc. |
title |
logical. if |
fmTstats
plots the t-stats and significant t-stats values if isPlot
is TRUE
and returns a list with following components:
tstats |
an xts object of t-stats values. |
z.alpha |
critical value corresponding to the confidence interval. |
Avinash Acharya and Doug Martin
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data("factorDataSetDjia5Yrs")
#Fit a Ffm with style factors only
require(factorAnalytics)
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", ret.var="RETURN",
date.var="DATE", exposure.vars=c("SECTOR","MKTCAP","ENTVAL","P2B"), 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)
exposure.vars= c("SECTOR", "COUNTRY","P2B", "MKTCAP")
fit.MICM <- fitFfm(data=factorDataSetDjia5Yrs, asset.var="TICKER", ret.var="RETURN",
date.var="DATE", exposure.vars=exposure.vars, addIntercept=TRUE)
stats = fmTstats(fit.MICM, isPlot = TRUE, z.alpha =1.96)
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