Nothing
#
# Copyright (C) 2011-2015 Christina Yassouridis
#
#
plotFPCs <- function(plotParams, legendPlace){
t_unique <- plotParams$t_unique
mean.timeIn <- plotParams$mean.timeIn
mu.smoothed <- plotParams$mu.smoothed
mean <- plotParams$mean.raw
cov.timeIn <- plotParams$cov.timeIn
cov.raw <- plotParams$cov.raw
cov.timeOut <- plotParams$cov.timeOut
cov.smoothed <- plotParams$cov.smoothed
tildeG <- plotParams$tildeG
hatV <- plotParams$hatV
base <- plotParams$base
nt <- plotParams$nt
par(mfrow=c(2,3),oma=c(0,0,2,0))
##Plot mu and smoothed curve
minY <- min(mu.smoothed, mean)
maxY <- max(mu.smoothed, mean)
plot(t_unique, mu.smoothed, col="red", type='l' ,xlab="time", ylab="",
main="Smoothed mean function", ylim=c(minY, maxY))
points(mean.timeIn, as.numeric(mean))
legend("topright",legend=c("smoothed mu","original mu"),col=c("red","black"), box.lty=1, lwd=3)
##Plot Cov
if(!requireNamespace("scatterplot3d"))
stop("Please install scatterplot3d to use this plot.")
scatterplot3d::scatterplot3d(cov.timeIn[,1], cov.timeIn[,2], cov.raw, xlab="x",
ylab="y", zlab="cov(x,y)", main="Raw covariance")
##Plot Cov smoothed
scatterplot3d::scatterplot3d(cov.timeOut[,1],cov.timeOut[,2], cov.smoothed, xlab="x",
ylab="y", zlab="cov(x,y)", main="Smoothed reduced covariance")
##Plot Sigma
plot(t_unique, tildeG, main="Sigma", xlab="time",
xlim=range(t_unique), ylim=c(min(tildeG,hatV),max(tildeG,hatV)), type="b")
lines(t_unique, hatV, col=2, lwd=3)
suppressWarnings(arrows(t_unique, tildeG,t_unique,
tildeG+(hatV-tildeG), length = 0.05, col=3))
legend("topleft", legend=c("smoothed cov on diagonal","smoothed var on diagonal"),col=c("red","black"), box.lty=1, lwd=3)
matplot(base[,1:min(dim(base)[2],3)],type='l',
main="First 3 eigenfunctions", xlab="time")
title("Generating functional principal components", outer=TRUE)
par(mfrow=c(1,1))
}
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