PermMisClassRate {FeatureImpCluster}

Permutation misclassification rate for single variable

Description

Answers the following question: Using the current partion as a baseline, what is the misclassification rate if a given feature is permuted?

Usage

PermMisClassRate(
  clusterObj,
  data,
  varName,
  basePred = NULL,
  predFUN = NULL,
  sub = 1,
  biter = 5,
  seed = 123
)

Arguments

clusterObj

a "typical" cluster object. The only requirement is that there must be a prediction function which maps the data to an integer

data

data.table with the same features as the data set used for clustering (or the simply the same data)

varName

character; variable name

basePred

should be equal to results of predFUN(clusterObj,newdata=data); this option saves time when data is a very large data set

predFUN

predFUN(clusterObj,newdata=data) should provide the cluster assignment as a numeric vector; typically this is a wrapper around a build-in prediction function

sub

integer between 0 and 1(=default), indicates that only a subset of the data should be used if <1

biter

the permutation is iterated biter(=5, default) times

seed

value for random seed

Value

vector of length biter with the misclassification rate

Examples

set.seed(123)
dat <- create_random_data(n=1e3)$data # random data

library(flexclust)
res <- kcca(dat,k=4)
PermMisClassRate(res,dat,varName="x")