Hi! I was trying with different values and setting the correlations in the design matrix to 0.05 is a point where I start to see a clear differenciation between the groups.
I am confused because, when calculating individually the correlation between two blocks I get high results, for example:
pls.resAB = pls(X$A, X$B, ncomp = 3)
AB<-mean(cor(pls.resAB$variates$A, pls.resAB$variates$B) %>% diag())
0.9538261
I tried first to fill the design matrix for DIABLO with the corresponding values for each block of data, but using more than 0.05 gives no good discrimination between the groups. What do you think I should do? My interest is to find biomarkers which in the future could be used to predict to which of the groups a patient belongs. I would like to integrate the blocks of data but the discrimination aspect is key to me.
Note: I was based on this thread to perform this code (https://mixomics-users.discourse.group/t/choosing-diablo-design-matrix/204?u=jeni)
Thanks!