README.md
In stefanrameseder/PartiallyFD: Partially Observed Functional Data: The Case of Systematically Missings
PartiallyFD
This repository contains an R package for the Paper "Partially Observed Functional Data: The Case of Systematically Missing" by Dominik Liebl and Stefan Rameseder. Below, we present a demo for the installation of the package and the application of the estimator.
The underlying statistical method is described in:
Partially Observed Functional Data: The Case of Systematically Missing
by Dominik Liebl and Stefan Rameseder (arXiv:1711.07715)
In addition, we provide .r files to reproduce the full simulation study.
- PartiallyFD_simulation.r: Given all meta parameters, simulate the DGPs and apply estimators
- PartiallyFD_simulation_analysis.r: Calculate the KPIs (and many more) used in the paper
Installation of required packages
install.packages("devtools")
install.packages("fda")
install.packages("mclust")
library("mclust")
library("devtools")
Installation of PartiallyFD-Package
install_github("stefanrameseder/PartiallyFD")
# install("PartiallyFD") # For installing locally
library("PartiallyFD")
Load data
data(log_partObsBidcurves)
attach(log_partObsBidcurves)
combinedNEG <- cbind(log_bc_fds[["NEG_HT"]], log_bc_fds[["NEG_NT"]])
matplot(x = md_dis, y=combinedNEG, ylim = c(0,10), # dim(combinedNEG)
col = PartiallyFD:::addAlpha("black", 0.2), type = "l", lwd = 1,
ylab = "", xaxt = "n", yaxt = "n", lty = "solid")
Exclude outliers
firstOutlier <- which.max(apply(combinedNEG, 2, max, na.rm = TRUE))
secondOutlier <- which.max(apply(combinedNEG[ ,-firstOutlier], 2, max, na.rm = TRUE))
combinedNEG_woOutlier <- combinedNEG[ ,-c(firstOutlier, secondOutlier+1)]
matplot(x = md_dis, y=combinedNEG_woOutlier, ylim = c(0,10), # dim(combinedNEG)
col = PartiallyFD:::addAlpha("black", 0.2), type = "l", lwd = 1,
ylab = "", xaxt = "n", yaxt = "n", lty = "solid")
Exploratory Data Analysis (Figure 2)
# Find components on "fully observed" domain [0 MW, 1830 MW]
d_max <- which.min(md_dis < 1830)
d_min <- which.max(md_dis >= 0)
# Calculate Eigenvectors and Scores
X_mat <- combinedNEG_woOutlier[d_min:d_max, ]
X_cent_mat <- X_mat - rowMeans(X_mat)
Cov_mat <- X_cent_mat %*% t(X_cent_mat) / ncol(X_cent_mat)
eigen_obj <- eigen(Cov_mat)
c(cumsum(eigen_obj$values)/sum(eigen_obj$values))[1:5]
# Standardizing to L2-norm == 1 (assuming [a,b]=[0,1])
eigenvec_1 <- eigen_obj$vectors[,1] * sqrt(length(d_min:d_max))
eigenvec_1 <- eigenvec_1 * sign(sum(eigenvec_1))
PC_scores <- c(eigenvec_1 %*% X_cent_mat)/length(d_min:d_max)
# Remove point-mass on minimal PC-scores (zero-functions)
quantile(PC_scores, seq(0, 0.1, 0.005))
thr <- -5.16
scoreIndicesProbMass <- PC_scores <= thr
PC_scores_red <- PC_scores[PC_scores > thr]
# Cluster PC_scores_red
mclust.obj <- densityMclust(data = PC_scores_red, G=2)
clust_vec <- mclust.obj$classification
# Figure 2
par(mfrow=c(1,2), mar=c(4.5,4,2.5,1)+0.1, family = "sans")
plotDensityMclust1(mclust.obj, xlab="First FPC Scores (98.7% Explained Variance)", main="")
mtext(text = "Normal Mixture Cluster Result", side = 3, line = 1.25, cex=1.2)
hist(PC_scores_red, add=TRUE, freq = FALSE, breaks = 12)
points(x = PC_scores_red[clust_vec==2],
y = rep(0,length(PC_scores_red[clust_vec==2])), col = "red", bg="red",
pch=21, cex=1)
points(x = PC_scores_red[clust_vec==1],
y = rep(0,length(PC_scores_red[clust_vec==1])), col = "blue", bg="blue",
pch=22, cex=1)
points(x = PC_scores[PC_scores < thr],
y = seq(0,0.1,len=length(PC_scores[PC_scores < thr])), col="darkorange", bg="darkorange",
pch=23, cex=1)
legend("topleft", legend = c("High-Price Cluster", "Low-Price Cluster", "Zero-Functions"),
pt.cex=1,
pch=c(21,22,23),
pt.bg = c("red", "blue", "darkorange"),
col=c("red", "blue", "darkorange"), bty="n")
# Histogram
g <- PC_scores_red[clust_vec==2]
m <- mean(g)
std <- sqrt(var(g))
hist(g, prob=TRUE, breaks = 8, ylim=c(-0, 0.8), xlim = c(-.5,3), main="", xlab="First FPC Scores (98.7% Explained Variance)")
mtext(text = "Histogram (High-Price Cluster)", side = 3, line = 1.25, cex=1.2)
curve(dnorm(x, mean=m, sd=std), lwd=2, add=TRUE, yaxt="n"); box()
dev.off()
Reduce Sample by excluding Low-Price and Zero-Function Clusters
scoreIndicesClust <- clust_vec==1
paste0("Point mass at Zero-Price: ",sum(scoreIndicesProbMass) ," and Low-Price Cluster:", sum(scoreIndicesClust))
reducedDomSample <- combinedNEG_woOutlier[ , !scoreIndicesProbMass]
combinedNEG_woOutlier <- reducedDomSample[ , !scoreIndicesClust]
matplot(combinedNEG_woOutlier, type = "l", col = PartiallyFD:::addAlpha("black", 0.3))
combinedNEG_der <- cbind(log_bc_fds_der[["NEG_HT"]], log_bc_fds_der[["NEG_NT"]])
combinedNEG_woOutlier_der <- combinedNEG_der[ , -c(firstOutlier, secondOutlier+1)]
reducedDomSample <- combinedNEG_woOutlier_der[ , !scoreIndicesProbMass]
combinedNEG_woOutlier_der <- reducedDomSample[ , !scoreIndicesClust]
Application
maxBasisLength <- 51 # The Basis Selection Criterion in BIC
basisSel <- "Med" # The Basis Selection Criterion in BIC
B <- 1000 # Number of Bootstrap Replications
basis_choice <- "fourier"# The basis where the functions are projected onto
res <- calcFTC(fds = combinedNEG_woOutlier, comp_dom = md_dis,
basis_seq = seq(3,maxBasisLength,2), base = basis_choice,
maxBasisLength = maxBasisLength, basisChoice = basisSel,
alpha = 0.05, B = B, derFds = combinedNEG_woOutlier_der)
Test-Procedure (See Section 3)
PartiallyFD:::checkFtcHypothesis(res$romWolf$ent)
Calculate Confidence Intervalls
alpha <- 0.05
nPerP <- apply(combinedNEG_woOutlier, 1, function(x) sum(!is.na(x)))
ftcSD <- sqrt(diag(res$ftcCov))/sqrt(nPerP)
critValue <- qnorm(1-(alpha/2))
CISummand <- critValue * ftcSD
ftcCI_plus <- res$ftcMean + CISummand
ftcCI_minus <- res$ftcMean - CISummand
krausSD <- sqrt(diag(res$krausCov))/sqrt(nPerP)
CISummand <- critValue * krausSD
krausCI_plus <- res$krausMean + CISummand
krausCI_minus <- res$krausMean - CISummand
Final Plot with Estimates (Figure 3)
scl.axs <- 1.9
p <- length(res$krausMean)
p_seq <- seq(1,p,8)
p.cex <- 1.2
maxVals <- apply(combinedNEG_woOutlier, 2, max, na.rm = TRUE)
layout(matrix(c(1,1,2), nrow = 1, ncol = 3, byrow = TRUE))
par(mar=c(5.1, 5.1, 2.1, 1.1))
matplot(x = 0, y = 0, type = "l", col = PartiallyFD:::addAlpha("black", 0.2), ylim = c(0, 10), xlim = c(0, 2500), lty = 1, xaxt = "n", yaxt = "n",
ylab = "Log Price [Log(Euro/MW)/week]", xlab = "Electricity Demand [MW]", cex.lab=scl.axs+0.45)
for(i in 1:length(maxVals)){ # i = 283
ind <- which.max(combinedNEG_woOutlier[!is.na(combinedNEG_woOutlier[ ,i]),i])
if(ind !=1){
abline(v = md_dis[ind],lwd = 2 , col = "lightblue")
}
}
matplot(x = md_dis, y = combinedNEG_woOutlier, type = "l", col = PartiallyFD:::addAlpha("black", 0.2), ylim = c(0, 10), lty = 1, add = TRUE)
points(x = md_dis[p_seq], y = res$ftcMean[p_seq], col = "blue", pch = 16, cex = p.cex)
lines(x = md_dis, y = res$ftcMean, col = "blue", lwd = 2)
points(x = md_dis[p_seq], y = res$krausMean[p_seq], col = "darkred", pch = 18, cex = p.cex)
lines(x = md_dis, y = res$krausMean, col = "darkred", lwd = 2)
polygon(x = c(md_dis, rev(md_dis)),
y = c(ftcCI_plus, rev(ftcCI_minus)),
col = PartiallyFD:::addAlpha("blue", 0.2), border = "blue", lwd = 1)
polygon(x = c(md_dis, rev(md_dis)),
y = c(krausCI_plus, rev(krausCI_minus)),
col = PartiallyFD:::addAlpha("darkred", 0.2), border = "darkred", lwd = 1)
yLim <- c(4,10)
xLim <- c(1750, 2500)
rect(xLim[1],yLim[1],xLim[2],yLim[2],col = rgb(0.5,0.5,0.5,1/4))
axis(side = 1, at = seq(0, 2500, 500), labels = paste0(seq(0, 2500, 500), " MW"), cex.axis = scl.axs)
axis(side = 2, at = seq(0, 10, 2), labels = paste0(seq(0, 10, 2)), cex.axis = scl.axs)
legend( "topleft", col = c("black", "darkred", "blue", "lightblue"), inset = 0.01, cex=scl.axs+ 0.4, pt.cex = scl.axs,
lwd=c(1,2, 2, 3), lty = c("solid", "solid", "solid","solid"),
pch=c(NA,18,16, NA),
legend = c(expression(paste(X[t])), expression(paste(hat(mu))), expression(paste(hat(mu)[FTC])), expression(paste(d[i]))))
matplot(x = 0, y = 0, type = "l", col = PartiallyFD:::addAlpha("black", 0.2), ylim = c(0, 10), xlim = c(0, 2500), lty = 1, xaxt = "n", yaxt = "n",
ylab = "Log Price in (Log Euro/MW)/week", xlab = "Electricity Demand in MW", cex.lab=scl.axs+0.45, add = TRUE)
par(mar=c(5.1, 3.1, 2.1, 2.1))
matplot(x = md_dis, y = combinedNEG, type = "l", col = PartiallyFD:::addAlpha("black", 0.2), lty = 1,
xaxt = "n", yaxt = "n", ylim = yLim, xlim = xLim,
ylab = "", xlab = "", cex.lab=scl.axs+0.45)
for(i in 1:length(maxVals)){ # i = 283
ind <- which.max(combinedNEG_woOutlier[!is.na(combinedNEG_woOutlier[ ,i]),i])
if(ind !=1){
abline(v = md_dis[ind],lwd = 2 , col = "lightblue")
}
}
points(x = md_dis[p_seq], y = res$ftcMean[p_seq], col = "blue", pch = 16, cex = p.cex)
lines(x = md_dis, y = res$ftcMean, col = "blue", lwd = 2)
points(x = md_dis[p_seq], y = res$krausMean[p_seq], col = "darkred", pch = 18, cex = p.cex)
lines(x = md_dis, y = res$krausMean, col = "darkred", lwd = 2)
polygon(x = c(md_dis, rev(md_dis)),
y = c(ftcCI_plus, rev(ftcCI_minus)),
col = PartiallyFD:::addAlpha("blue", 0.2), border = "blue", lwd = 1)
polygon(x = c(md_dis, rev(md_dis)),
y = c(krausCI_plus, rev(krausCI_minus)),
col = PartiallyFD:::addAlpha("darkred", 0.2), border = "darkred", lwd = 1)
axis(side = 1, at = seq(xLim[1], xLim[2], 250), labels = paste0(seq(xLim[1], xLim[2], 250), " MW"), cex.axis = scl.axs)
axis(side = 2, at = seq(yLim[1], yLim[2], 2), labels = paste0(seq(yLim[1], yLim[2], 2)), cex.axis = scl.axs)
matplot(x = md_dis, y = combinedNEG, type = "l", col = PartiallyFD:::addAlpha("black", 0.3), ylim = yLim, xlim = xLim, lty = 1, add = TRUE)
dev.off()
## Plot Covariance Estimates
# install.packages("rgl")
library(rgl)
persp3d(x = md_dis, y =md_dis, z = res$ftcCov, col="skyblue", zlim = c(-1,5))
persp3d(x = md_dis, y =md_dis, z = res$krausCov, col="skyblue", zlim = c(-1,5))
stefanrameseder/PartiallyFD documentation built on May 29, 2019, 9:50 a.m.
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PartiallyFD
This repository contains an R package for the Paper "Partially Observed Functional Data: The Case of Systematically Missing" by Dominik Liebl and Stefan Rameseder. Below, we present a demo for the installation of the package and the application of the estimator.
The underlying statistical method is described in: Partially Observed Functional Data: The Case of Systematically Missing by Dominik Liebl and Stefan Rameseder (arXiv:1711.07715)
In addition, we provide .r files to reproduce the full simulation study. - PartiallyFD_simulation.r: Given all meta parameters, simulate the DGPs and apply estimators - PartiallyFD_simulation_analysis.r: Calculate the KPIs (and many more) used in the paper
Installation of required packages
install.packages("devtools")
install.packages("fda")
install.packages("mclust")
library("mclust")
library("devtools")
Installation of PartiallyFD-Package
install_github("stefanrameseder/PartiallyFD")
# install("PartiallyFD") # For installing locally
library("PartiallyFD")
Load data
data(log_partObsBidcurves)
attach(log_partObsBidcurves)
combinedNEG <- cbind(log_bc_fds[["NEG_HT"]], log_bc_fds[["NEG_NT"]])
matplot(x = md_dis, y=combinedNEG, ylim = c(0,10), # dim(combinedNEG)
col = PartiallyFD:::addAlpha("black", 0.2), type = "l", lwd = 1,
ylab = "", xaxt = "n", yaxt = "n", lty = "solid")
Exclude outliers
firstOutlier <- which.max(apply(combinedNEG, 2, max, na.rm = TRUE))
secondOutlier <- which.max(apply(combinedNEG[ ,-firstOutlier], 2, max, na.rm = TRUE))
combinedNEG_woOutlier <- combinedNEG[ ,-c(firstOutlier, secondOutlier+1)]
matplot(x = md_dis, y=combinedNEG_woOutlier, ylim = c(0,10), # dim(combinedNEG)
col = PartiallyFD:::addAlpha("black", 0.2), type = "l", lwd = 1,
ylab = "", xaxt = "n", yaxt = "n", lty = "solid")
Exploratory Data Analysis (Figure 2)
# Find components on "fully observed" domain [0 MW, 1830 MW]
d_max <- which.min(md_dis < 1830)
d_min <- which.max(md_dis >= 0)
# Calculate Eigenvectors and Scores
X_mat <- combinedNEG_woOutlier[d_min:d_max, ]
X_cent_mat <- X_mat - rowMeans(X_mat)
Cov_mat <- X_cent_mat %*% t(X_cent_mat) / ncol(X_cent_mat)
eigen_obj <- eigen(Cov_mat)
c(cumsum(eigen_obj$values)/sum(eigen_obj$values))[1:5]
# Standardizing to L2-norm == 1 (assuming [a,b]=[0,1])
eigenvec_1 <- eigen_obj$vectors[,1] * sqrt(length(d_min:d_max))
eigenvec_1 <- eigenvec_1 * sign(sum(eigenvec_1))
PC_scores <- c(eigenvec_1 %*% X_cent_mat)/length(d_min:d_max)
# Remove point-mass on minimal PC-scores (zero-functions)
quantile(PC_scores, seq(0, 0.1, 0.005))
thr <- -5.16
scoreIndicesProbMass <- PC_scores <= thr
PC_scores_red <- PC_scores[PC_scores > thr]
# Cluster PC_scores_red
mclust.obj <- densityMclust(data = PC_scores_red, G=2)
clust_vec <- mclust.obj$classification
# Figure 2
par(mfrow=c(1,2), mar=c(4.5,4,2.5,1)+0.1, family = "sans")
plotDensityMclust1(mclust.obj, xlab="First FPC Scores (98.7% Explained Variance)", main="")
mtext(text = "Normal Mixture Cluster Result", side = 3, line = 1.25, cex=1.2)
hist(PC_scores_red, add=TRUE, freq = FALSE, breaks = 12)
points(x = PC_scores_red[clust_vec==2],
y = rep(0,length(PC_scores_red[clust_vec==2])), col = "red", bg="red",
pch=21, cex=1)
points(x = PC_scores_red[clust_vec==1],
y = rep(0,length(PC_scores_red[clust_vec==1])), col = "blue", bg="blue",
pch=22, cex=1)
points(x = PC_scores[PC_scores < thr],
y = seq(0,0.1,len=length(PC_scores[PC_scores < thr])), col="darkorange", bg="darkorange",
pch=23, cex=1)
legend("topleft", legend = c("High-Price Cluster", "Low-Price Cluster", "Zero-Functions"),
pt.cex=1,
pch=c(21,22,23),
pt.bg = c("red", "blue", "darkorange"),
col=c("red", "blue", "darkorange"), bty="n")
# Histogram
g <- PC_scores_red[clust_vec==2]
m <- mean(g)
std <- sqrt(var(g))
hist(g, prob=TRUE, breaks = 8, ylim=c(-0, 0.8), xlim = c(-.5,3), main="", xlab="First FPC Scores (98.7% Explained Variance)")
mtext(text = "Histogram (High-Price Cluster)", side = 3, line = 1.25, cex=1.2)
curve(dnorm(x, mean=m, sd=std), lwd=2, add=TRUE, yaxt="n"); box()
dev.off()
Reduce Sample by excluding Low-Price and Zero-Function Clusters
scoreIndicesClust <- clust_vec==1
paste0("Point mass at Zero-Price: ",sum(scoreIndicesProbMass) ," and Low-Price Cluster:", sum(scoreIndicesClust))
reducedDomSample <- combinedNEG_woOutlier[ , !scoreIndicesProbMass]
combinedNEG_woOutlier <- reducedDomSample[ , !scoreIndicesClust]
matplot(combinedNEG_woOutlier, type = "l", col = PartiallyFD:::addAlpha("black", 0.3))
combinedNEG_der <- cbind(log_bc_fds_der[["NEG_HT"]], log_bc_fds_der[["NEG_NT"]])
combinedNEG_woOutlier_der <- combinedNEG_der[ , -c(firstOutlier, secondOutlier+1)]
reducedDomSample <- combinedNEG_woOutlier_der[ , !scoreIndicesProbMass]
combinedNEG_woOutlier_der <- reducedDomSample[ , !scoreIndicesClust]
Application
maxBasisLength <- 51 # The Basis Selection Criterion in BIC
basisSel <- "Med" # The Basis Selection Criterion in BIC
B <- 1000 # Number of Bootstrap Replications
basis_choice <- "fourier"# The basis where the functions are projected onto
res <- calcFTC(fds = combinedNEG_woOutlier, comp_dom = md_dis,
basis_seq = seq(3,maxBasisLength,2), base = basis_choice,
maxBasisLength = maxBasisLength, basisChoice = basisSel,
alpha = 0.05, B = B, derFds = combinedNEG_woOutlier_der)
Test-Procedure (See Section 3)
PartiallyFD:::checkFtcHypothesis(res$romWolf$ent)
Calculate Confidence Intervalls
alpha <- 0.05
nPerP <- apply(combinedNEG_woOutlier, 1, function(x) sum(!is.na(x)))
ftcSD <- sqrt(diag(res$ftcCov))/sqrt(nPerP)
critValue <- qnorm(1-(alpha/2))
CISummand <- critValue * ftcSD
ftcCI_plus <- res$ftcMean + CISummand
ftcCI_minus <- res$ftcMean - CISummand
krausSD <- sqrt(diag(res$krausCov))/sqrt(nPerP)
CISummand <- critValue * krausSD
krausCI_plus <- res$krausMean + CISummand
krausCI_minus <- res$krausMean - CISummand
Final Plot with Estimates (Figure 3)
scl.axs <- 1.9
p <- length(res$krausMean)
p_seq <- seq(1,p,8)
p.cex <- 1.2
maxVals <- apply(combinedNEG_woOutlier, 2, max, na.rm = TRUE)
layout(matrix(c(1,1,2), nrow = 1, ncol = 3, byrow = TRUE))
par(mar=c(5.1, 5.1, 2.1, 1.1))
matplot(x = 0, y = 0, type = "l", col = PartiallyFD:::addAlpha("black", 0.2), ylim = c(0, 10), xlim = c(0, 2500), lty = 1, xaxt = "n", yaxt = "n",
ylab = "Log Price [Log(Euro/MW)/week]", xlab = "Electricity Demand [MW]", cex.lab=scl.axs+0.45)
for(i in 1:length(maxVals)){ # i = 283
ind <- which.max(combinedNEG_woOutlier[!is.na(combinedNEG_woOutlier[ ,i]),i])
if(ind !=1){
abline(v = md_dis[ind],lwd = 2 , col = "lightblue")
}
}
matplot(x = md_dis, y = combinedNEG_woOutlier, type = "l", col = PartiallyFD:::addAlpha("black", 0.2), ylim = c(0, 10), lty = 1, add = TRUE)
points(x = md_dis[p_seq], y = res$ftcMean[p_seq], col = "blue", pch = 16, cex = p.cex)
lines(x = md_dis, y = res$ftcMean, col = "blue", lwd = 2)
points(x = md_dis[p_seq], y = res$krausMean[p_seq], col = "darkred", pch = 18, cex = p.cex)
lines(x = md_dis, y = res$krausMean, col = "darkred", lwd = 2)
polygon(x = c(md_dis, rev(md_dis)),
y = c(ftcCI_plus, rev(ftcCI_minus)),
col = PartiallyFD:::addAlpha("blue", 0.2), border = "blue", lwd = 1)
polygon(x = c(md_dis, rev(md_dis)),
y = c(krausCI_plus, rev(krausCI_minus)),
col = PartiallyFD:::addAlpha("darkred", 0.2), border = "darkred", lwd = 1)
yLim <- c(4,10)
xLim <- c(1750, 2500)
rect(xLim[1],yLim[1],xLim[2],yLim[2],col = rgb(0.5,0.5,0.5,1/4))
axis(side = 1, at = seq(0, 2500, 500), labels = paste0(seq(0, 2500, 500), " MW"), cex.axis = scl.axs)
axis(side = 2, at = seq(0, 10, 2), labels = paste0(seq(0, 10, 2)), cex.axis = scl.axs)
legend( "topleft", col = c("black", "darkred", "blue", "lightblue"), inset = 0.01, cex=scl.axs+ 0.4, pt.cex = scl.axs,
lwd=c(1,2, 2, 3), lty = c("solid", "solid", "solid","solid"),
pch=c(NA,18,16, NA),
legend = c(expression(paste(X[t])), expression(paste(hat(mu))), expression(paste(hat(mu)[FTC])), expression(paste(d[i]))))
matplot(x = 0, y = 0, type = "l", col = PartiallyFD:::addAlpha("black", 0.2), ylim = c(0, 10), xlim = c(0, 2500), lty = 1, xaxt = "n", yaxt = "n",
ylab = "Log Price in (Log Euro/MW)/week", xlab = "Electricity Demand in MW", cex.lab=scl.axs+0.45, add = TRUE)
par(mar=c(5.1, 3.1, 2.1, 2.1))
matplot(x = md_dis, y = combinedNEG, type = "l", col = PartiallyFD:::addAlpha("black", 0.2), lty = 1,
xaxt = "n", yaxt = "n", ylim = yLim, xlim = xLim,
ylab = "", xlab = "", cex.lab=scl.axs+0.45)
for(i in 1:length(maxVals)){ # i = 283
ind <- which.max(combinedNEG_woOutlier[!is.na(combinedNEG_woOutlier[ ,i]),i])
if(ind !=1){
abline(v = md_dis[ind],lwd = 2 , col = "lightblue")
}
}
points(x = md_dis[p_seq], y = res$ftcMean[p_seq], col = "blue", pch = 16, cex = p.cex)
lines(x = md_dis, y = res$ftcMean, col = "blue", lwd = 2)
points(x = md_dis[p_seq], y = res$krausMean[p_seq], col = "darkred", pch = 18, cex = p.cex)
lines(x = md_dis, y = res$krausMean, col = "darkred", lwd = 2)
polygon(x = c(md_dis, rev(md_dis)),
y = c(ftcCI_plus, rev(ftcCI_minus)),
col = PartiallyFD:::addAlpha("blue", 0.2), border = "blue", lwd = 1)
polygon(x = c(md_dis, rev(md_dis)),
y = c(krausCI_plus, rev(krausCI_minus)),
col = PartiallyFD:::addAlpha("darkred", 0.2), border = "darkred", lwd = 1)
axis(side = 1, at = seq(xLim[1], xLim[2], 250), labels = paste0(seq(xLim[1], xLim[2], 250), " MW"), cex.axis = scl.axs)
axis(side = 2, at = seq(yLim[1], yLim[2], 2), labels = paste0(seq(yLim[1], yLim[2], 2)), cex.axis = scl.axs)
matplot(x = md_dis, y = combinedNEG, type = "l", col = PartiallyFD:::addAlpha("black", 0.3), ylim = yLim, xlim = xLim, lty = 1, add = TRUE)
dev.off()
## Plot Covariance Estimates
# install.packages("rgl")
library(rgl)
persp3d(x = md_dis, y =md_dis, z = res$ftcCov, col="skyblue", zlim = c(-1,5))
persp3d(x = md_dis, y =md_dis, z = res$krausCov, col="skyblue", zlim = c(-1,5))
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