plotSim {adjclust}  R Documentation 
Heatmap of the (dis)similarity matrix
plotSim( mat, type = c("similarity", "dissimilarity"), clustering = NULL, dendro = NULL, palette = heat.colors, breaks = 10, log = TRUE, h = NULL, stats = c("R.squared", "D.prime"), main = NULL, col.clust = "darkblue", lwd.clust = 2, xaxis = FALSE, naxis = 10 )
mat 
matrix to plot. It can be of class 
type 
input matrix type. Can be either 
clustering 
vector of length the number of rows (columns) of the
matrix that contains a contiguity constrained clustering (as provided by

dendro 

palette 
color palette. Default to 
breaks 
number of breaks used to set colors from the palette. Those
are based on the quantiles of the matrix entries and for skewed distributions
the actual number used to set the palette can be lower than 
log 
logical. Should the breaks be based on logscaled values of the
matrix entries. Default to 
h 
if 
stats 
if 
main 
graphic title. 
col.clust 
color for the borders of the clusters (if 
lwd.clust 
line width for the borders of the clusters (if

xaxis 
logical. Should a xaxis be displayed? Default to 
naxis 
number of breaks to display on the xaxis. For

This function produces a heatmap for the used (dis)similarity matrix that can be used as a diagnostic plot to check the consistency between the obtained clustering and the original (dis)similarity
# input as HiTC::HTCexp object ## Not run: if (require("HiTC", quietly = TRUE)) { load(system.file("extdata", "hic_imr90_40_XX.rda", package = "adjclust")) plotSim(hic_imr90_40_XX) # with a constrained clustering res < hicClust(hic_imr90_40_XX, log = TRUE) selected.capushe < select(res) plotSim(hic_imr90_40_XX, clustering = selected.capushe, xaxis = TRUE) plotSim(hic_imr90_40_XX, clustering = selected.capushe, dendro = res) } ## End(Not run) plotSim(dist(iris[ ,1:4]), log = FALSE)