# summary.NormMixClus_K: Summarize results from clustering using a Normal mixture... In coseq: Co-Expression Analysis of Sequencing Data

## Description

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

## Usage

 ```1 2``` ```## 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

`NormMixClus`, `NormMixClus_K`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```## 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) ```