post_analy_cor {twl}

Creates and saves correlation plots based on posterior similarity matrices

Description

Creates and saves correlation plots based on posterior similarity matrices

Usage

post_analy_cor(outpu_new, titles, pdf_path, ords = "none")

Arguments

outpu_new

the output of the pairwise_clus function, and a list whose length is the number of datasets being integrated, and each elemnt of which is a posterior similarity matrix. The dimension of each symmetric matrix is the number of samples in the respective dataset, and elements in the matrix are values between 0 and 1, and estimate of the probability 2 samples find themselves in the same clustering.

titles

a vector of strings of length number of integrated datasets. Elements of the vector are titles in the respective correlation plots

pdf_path

file path where the plots will be saved as a pdf.

ords

whether the correlation plots should be reordered according to that of hierarchical clustering for a more comprehensible plot. Defaults to 'none'. Passing any string apart from 'none' (i.e., 'yes') will result in the re-ordering.

Value

dendro_ord regardless of whether correlation plots are reordered according to hierarchical clustering, a list of reorderings is returned of length the number of datasets on which analysis was performed.

Examples

data(data_and_output)
## Not run: clus_save <- TWLsample(misaligned_mat,misaligned,output_every=50,num_its=5000,manip=FALSE)
outpu_new <- pairwise_clus(clus_save,BURNIN=2000)
post_analy_cor(outpu_new,c("title1","title2","title3","title4","title5"),
tempfile(),ords='none') 
clus_labs <- post_analy_clus(outpu_new,clus_save,c(2:6),rep(0.6,5),c("title1","title2",
"title3","title4","title5"),tempfile())
output_nest <- cross_dat_analy(clus_save,4750)

## End(Not run)