Summarize results from clustering using a Normal mixture model

Description

A function to summarize the clustering results obtained from a Normal mixture model.

Usage

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## S3 method for class 'NormMixClus_K'
summary(object, y_profiles, digits = 3, ...)

Arguments

object

An object of class "NormMixClus_K"

y_profiles

y (n x q) matrix of observed profiles for n observations and q variables

digits

Integer indicating the number of decimal places to be used for mixture model parameters

...

Additional arguments

Details

The summary function for an object of class "NormMixClus_K" provides the following summary of results:

1) Number of clusters and model selection criterion used, if applicable.

2) Number of observations across all clusters with a maximum conditional probability greater than 90 observations) for the selected model.

3) Number of observations per cluster with a maximum conditional probability greater than 90 cluster) for the selected model.

4) μ values for the selected model.

5) π values for the selected model.

Author(s)

Andrea Rau

See Also

NormMixClus, NormMixClus_K

Examples

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## Simulate toy data, n = 300 observations
set.seed(12345)
countmat <- matrix(runif(300*4, min=0, max=500), nrow=300, ncol=4)
countmat <- countmat[which(rowSums(countmat) > 0),]
profiles <- transform_RNAseq(countmat, norm="none", 
                             transformation="arcsin")$tcounts

conds <- rep(c("A","B","C","D"), each=2)

## Run the Normal mixture model for K = 2,3
run <- NormMixClus(y=profiles, K=2:3, iter=5)

## Run the Normal mixture model for K=2
run2 <- NormMixClus_K(y=profiles, K=2, iter=5)

## Re-estimate mixture parameters for the model with K=2 clusters
param <- NormMixParam(run2, y_profiles=profiles)

## Summary of results
summary(run, y_profiles=profiles)