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### plot the rsm results
plot.rsmMCL <- function(x, family, trace.all = TRUE, plain=TRUE, ...){
plot.exact <- family == "gauss"
## extract all the elemnts in the list object of the rsm results
##for(i in 1:length(x)){
## tempobj=x[[i]]
## eval(parse(text=paste(names(x)[[i]],"= tempobj")))
###}
RSM.1 <- x$RSM.1
Psi <- x$Psi
psi0 <- x$psi0
FVbox <- x$FVbox
DAbox <- x$DAbox
ESAs <- x$ESAs
Exacts <- x$Exacts
N.iter <- x$N.iter
RSM.2 <- x$RSM.2
N.SA <- length(ESAs)
if(trace.all){
Psis <- do.call(rbind, Psi)
Psis <- rbind(psi0, Psis)
for (i in 1:N.iter) {
if (i <= N.SA){
SAn <- !is.null(ESAs[[i]])
}else{
SAn <- FALSE
}
if (SAn){
par(mfrow = c(1,3))
if (is.null(RSM.2[[i]])){
RSM.x <- RSM.1[[i]]
}else{
RSM.x <-RSM.2[[i]]
}
plot.RSM(RSM.x, psi = Psis[i, ], bounds = list(x1=c(-1.1,2), x2 = c(-1.5,1.5)),
fvbox = FVbox[[i]], DAbox = DAbox[[i]], A.path = "steepest",
distance = ESAs[[i]]$ds, exact = Exacts[[i]], exact.vals = plot.exact)
title(main = list(paste("Iteration ", i)))
SA <- ESAs[[i]][[1]]
plot(mc.lr ~ dist, data = SA$SA.fit, xlab = "distance", ylab = "Monte Carlo likelihood")
lines(SA$pred.val ~ SA$pred.dist)
plot(log(mc.var) ~ dist, data = SA$SA.fit, xlab = "distance",
ylab = "Monte Carlo variance")
lines(SA$pred.var ~ SA$pred.dist)
}else{
par(mfrow = c(1,1))
plot.RSM(RSM.2[[i]], psi = Psis[i, ], bounds = list(x1=c(-1.1,2), x2 = c(-1.5,1.5)),
fvbox = FVbox[[i]], DAbox = DAbox[[i]], A.path = "canonical",
exact = Exacts[[i]], exact.vals = plot.exact)
title(main = list(paste("Iteration ", i), cex = 1.1))
}
}
}else{
## plot the RSM of the final iteration
if(N.iter <=N.SA){
SAn <- is.null(ESAs[[N.iter]])
}else{
SAn <- FALSE
}
if (SAn){
par(mfrow = c(1,3))
if (is.null(RSM.2[[N.iter]])){
RSM.x <- RSM.1[[N.iter]]
}else{
RSM.x <-RSM.2[[N.iter]]
}
plot.RSM(RSM.x, psi = Psi[[N.iter-1] ], bounds = list(x1=c(-1.1,2), x2 = c(-1.5,1.5)),
fvbox = FVbox[[N.iter]], DAbox = DAbox[[N.iter]], A.path = "steepest",
distance = ESAs[[N.iter]]$ds, exact = Exacts[[N.iter]], exact.vals = plot.exact)
title(main = "Final Iteration")
SA <- ESAs[[N.iter]][[1]]
plot(mc.lr ~ dist, data = SA$SA.fit, xlab = "distance", ylab = "Monte Carlo likelihood")
lines(SA$pred.val ~ SA$pred.dist)
plot(log(mc.var) ~ dist, data = SA$SA.fit, xlab = "distance",
ylab = "Monte Carlo variance")
lines(SA$pred.var ~ SA$pred.dist)
}
else{
par(mfrow = c(1,1))
plot.RSM(RSM.2[[N.iter]], psi = Psi[[N.iter-1]], bounds = list(x1=c(-1.1,2), x2 = c(-1.5,1.5)),
fvbox = FVbox[[N.iter]], DAbox = DAbox[[N.iter]], A.path = "canonical",
exact = Exacts[[N.iter]], exact.vals = plot.exact)
title(main = "Final Iteration")
}
}
}
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