COMP: yet another lib

DESCRIPTION

@@ -49,6 +49,7 @@ Suggests:
     ggplot2,
     glasso,
     ggpubr,
+    ggdendro,
     grid,
     hdf5r,
     igraph,
@@ -58,6 +59,7 @@ Suggests:
     Matrix,
     mFilter,
     moments,
+    patchwork,
     pixmap,
     png,
     pracma,

vignettes/simlr_interpretation.Rmd

@@ -190,11 +190,13 @@ popdf = data.frame( age = outcome, cog=outcome1, mat1, mat2, mat3 )
 temp2=apply_simlr_matrices( popdf, simres2, 
     center=TRUE, scale=TRUE, absolute_value=rep(TRUE,length(matlist)) )
 simnames=temp2[[2]]
-zz=exploratory_visualization( antsrimpute(temp2[[1]][, temp2[[2]]]) )
+zz=exploratory_visualization( temp2[[1]][, temp2[[2]]], dotsne=FALSE )
 print( zz$plot )
 ```
 
 Now we can icorporate the "modalities" in order to predict the simulated outcome matrix.
+Note that contributions exist from each modality in the regression (in some cases).  This 
+is one of the advantages of SiMLR: it provides systematic guidance for building these types of integrative models.
 
 ```{r dataintegration2,fig.width=12,fig.height=6.5}
 # multi-view regression