#'
#' It is a function which plots relevant parameters
#'
-#'
+#' @param model the model constructed by valse procedure
+#' @param n sample size
#' @return several plots
#'
#' @examples TODO
#'
#' @export
#'
-plot_valse = function(){
+plot_valse = function(model,n){
require("gridExtra")
require("ggplot2")
require("reshape2")
+ require("cowplot")
+ K = length(model$pi)
## regression matrices
gReg = list()
for (r in 1:K){
print(gCov )
### proportions
- Gam = matrix(0, ncol = K, nrow = n)
- gam = Gam
- for (i in 1:n){
- for (r in 1:K){
- sqNorm2 = sum( (Y[i,]%*%model$rho[,,r]-X[i,]%*%model$phi[,,r])^2 )
- Gam[i,r] = model$pi[r] * exp(-0.5*sqNorm2)* det(model$rho[,,r])
- }
- gam[i,] = Gam[i,] / sum(Gam[i,])
- }
- affec = apply(gam, 1,which.max)
gam2 = matrix(NA, ncol = K, nrow = n)
for (i in 1:n){
- gam2[i, ] = c(gam[i, affec[i]], affec[i])
+ gam2[i, ] = c(model$Gam[i, model$affec[i]], model$affec[i])
}
+
bp <- ggplot(data.frame(gam2), aes(x=X2, y=X1, color=X2, group = X2)) +
- geom_boxplot() + theme(legend.position = "none")
- print(bp + background_grid(major = "xy", minor = "none"))
+ geom_boxplot() + theme(legend.position = "none")+ background_grid(major = "xy", minor = "none")
+ print(bp )
### Mean in each cluster
XY = cbind(X,Y)