diff --git a/pls_playground.Rmd b/pls_playground.Rmd
index 83c79a7..c35feac 100644
--- a/pls_playground.Rmd
+++ b/pls_playground.Rmd
@@ -51,4 +51,52 @@
 coefs <- coef(pls.model)
 
 barplot(sort(coefs[,1,1], decreasing = T)[1:4])
+```
+
+
+```{r simulate}
+X <- matrix(rnorm(1100), 100, 11)
+Y <- matrix(rnorm(400), 100, 4)
+
+pls.model <- plsr(Y ~ X, validation = "CV")
+
+cv <- RMSEP(pls.model)
+best_dims <- which.min(cv$val[estimate = "adjCV", , ]) - 1
+pls.model <- plsr(Y ~ X, ncomp = best_dims)
+coefs <- sort(coef(pls.model)[,1,1], decreasing = T)
+
+barplot(coefs)
+
+```
+
+
+```{r cca-simulate}
+X <- matrix(rnorm(1100), 100, 11)
+Y <- matrix(rnorm(400), 100, 4)
+
+M <- cor(cbind(X,Y))
+corrplot(M, method="ellipse", order="hclust", addrect=2, addCoef.col="black")
+cc <- cancor(X, Y)
+
+#NB: cc <- cancor(cbind(rt,correct, accuracy) ~ xt + xl + xtl, data = data)
+
+```
+
+
+```{r plsrglm}
+rm(list = ls())
+library(plsRglm)
+
+data(Cornell)
+df <- Cornell
+x <- subset(df, select = -c(Y))
+y <- df$Y
+## K is the number of folds in CV, and nt is the maximum number of components, 
+#cv.modpls<-cv.plsRglm(dataY=y,dataX=x ,nt=10,modele="pls-glm-logistic",K=8)
+
+modpls <- plsRglm(dataY = y,dataX = x, nt = 10, modele = "pls-glm-logistic", sparse=TRUE,sparseStop=TRUE)
+res.cv.modpls<-cvtable(summary(cv.modpls))
+
+res6<-plsR(Y~.,data=Cornell, nt=6, typeVC="missing", pvals.expli=TRUE)
+
 ```
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