### Regression matrices
-model = res_valse
+model = Res
K = dim(model$phi)[3]
valMax = max(abs(model$phi))
require(fields)
+
if (K<4){
par(mfrow = c(1,K))
-} else par(mfrow = c(2, (K+1)/2))
+} else op = par(mfrow = c(2, (K+1)/2))
+
+## Phi
for (r in 1:K){
- image.plot(t(abs(model$phi[,,r])),
+ image.plot(t(abs(model$phi[,,r])),
col=gray(rev(seq(0,64,length.out=65))/65),breaks=seq(0,valMax,length.out=66))
}
+par(mfrow = c(1,K),oma = c(0,0,3,0))
+mtext("Regression matrices in each cluster", side=3, line=4, font=2, cex=2, col='red')
+
+par(mfrow = c(1,2), oma=c(0,0,3,0))
+for (i in 1:4)
+ plot(runif(20), runif(20),
+ main=paste("random plot (",i,")",sep=''))
+par(op)
+mtext("Four plots",
+ side=3, line=4, font=2, cex=2, col='red')
### Zoom onto two classes we want to compare
kSel = c(1,2)
Gam = matrix(0, ncol = K, nrow = n)
gam = Gam
for (i in 1:n){
- for (r in 1:k){
+ for (r in 1:K){
sqNorm2 = sum( (Y[i,]%*%model$rho[,,r]-X[i,]%*%model$phi[,,r])^2 )
Gam[i,r] = model$pi[r] * exp(-0.5*sqNorm2)* det(model$rho[,,r])
}