iPVs: A super function that runs through all of the necessary...

In hughesevoanth/iPVs: Identification of principal variables and an estimate of the number of variables in a data set.

A super function that runs through all of the necessary staeps for you to identify Independent Principal Variables

Description

A super function that runs through all of the necessary staeps for you to identify Independent Principal Variables

Usage

iPVs(
  variabledata,
  cor_method = "spearman",
  dist_method = "R",
  hclust_meth = "complete",
  cutheight = 0.5,
  cmat = NULL
)

Arguments

variabledata	a data.frame of the variable data used to build your cormat and hclust tree
cor_method	the correlation method used to construct the correlation matrix
dist_method	the distance method to generate the distance matrix
hclust_meth	the clustering method used by hclust
cutheight	tree cut height. Value is equal to a dissimilarity of 1 - Spearman's rho.
cmat	a correlation matrix you can pass to the function, which will skip the generation of a new matrix.

Examples

iPVs()

hughesevoanth/iPVs documentation built on July 26, 2023, 3:21 p.m.