If "associationMeasures", similarities between variables are assessed by combination of appropriate measures of association for different pairs of data types. Then a dendrogram is derived by standard hierarchical clustering (hclust with default options). If "ClustOfVar", variables are clustered by the ClustOfVar{ClustOfVar} approach.
associationFun
By default, appropriate association measures are chosen for each pair of variables, see association for details. But the user can also define a function that for any two variables calculates a similarity measure. Ignored if dist.variables.method = "ClustOfVar"
check.psd
If TRUE, it is checked if the variable's similarity matrix S is positive semi-definite (p.s.d.), and if not it is transformed to a p.s.d. one by nearPD, see dist.variables for details. Ignored if dist.variables.method = "ClustOfVar"
Details
Clustering of variables can either be done similarity-based or by the ClustOfVar approach, which uses principal components analysis for mixed data.
Value
An object of class dendrogram
Author(s)
Manuela Hummel
References
Chavent M, Kuentz-Simonet V, Liquet B, Saracco J (2012). ClustOfVar: An R Package for the Clustering of Variables. Journal of Statistical Software, 50:1-16.
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(CluMix)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/CluMix/dendro.variables.Rd_%03d_medium.png", width=480, height=480)
> ### Name: dendro.variables
> ### Title: Variables dendrogram
> ### Aliases: dendro.variables
> ### Keywords: cluster
>
> ### ** Examples
>
> data(mixdata)
>
> dend <- dendro.variables(mixdata)
> plot(dend)
>
>
>
>
>
> dev.off()
null device
1
>