clusterVariables: Variable clustering (using Normal Mixture Modeling for...

In sambaala/linkspotter: Bivariate Correlations Calculation and Visualization

Variable clustering (using Normal Mixture Modeling for Model-Based Clustering : mclust)

Description

Computation of a variable clustering on a correlation matrix.

Usage

clusterVariables(corMatrix, nbCluster = 1:9)

Arguments

corMatrix	a dataframe corresponding to a correlation matrix
nbCluster	an integer or a vector of integers corresponding to the preferred number of cluster for the unsupervised learning.

Value

a dataframe: the first column contains the variable names, the second column the index of the cluster they are affected to.

Examples

# calculate a correlation dataframe
data(iris)
corDF <- multiBivariateCorrelation(dataset = iris, corMethods = "MaxNMI")
# tranform to correlation matrix
corMatrix <- corCouplesToMatrix(x1_x2_val = corDF[,c('X1','X2',"MaxNMI")])
# perform the clustering
corGroups <- clusterVariables(corMatrix = corMatrix, nbCluster = 3)
print(corGroups)

sambaala/linkspotter documentation built on Oct. 18, 2023, 9:45 p.m.