NormMixParam {coseq} | R Documentation

## Calculate the mean and covariance for a Normal mixture model

### Description

Calculates the mean and covariance parameters for a normal mixture model of the form pK_Lk_Ck

### Usage

    NormMixParam(
      coseqResults,
      y_profiles = NULL,
      K = NULL,
      digits = 3,
      plot = FALSE,
      ...
    )

### Arguments

`coseqResults` | Object of class `coseqResults` or `RangedSummarizedExperiment` (as output from the `NormMixClus` or `NormMixClusK` functions)
`y_profiles` | y (n x q) matrix of observed profiles for n observations and q variables, required for `x` of class `RangedSummarizedExperiment`
`K` | The model used for parameter estimation for objects `x` of class `coseq` or `NormMixClus`. When `NULL`, the model selected by the ICL criterion is used; otherwise, `K` should designate the number of clusters in the desired model
`digits` | Integer indicating the number of decimal places to be used for output
`plot` | If `true`, produce heatmaps to visualize the estimated per-cluster correlation matrices
`...` | Additional optional parameters to pass to `corrplot`, if desired

### Value

`pi` | Vector of dimension K with the estimated cluster proportions from the Gaussian mixture model, where K is the number of clusters
`mu` | Matrix of dimension K x d containing the estimated mean vector from the Gaussian mixture model, where d is the number of samples in the data `y_profiles` and K is the number of clusters
`Sigma` | Array of dimension d x d x K containing the estimated covariance matrices from the Gaussian mixture model, where d is the number of samples in the data `y_profiles` and K is the number of clusters
`rho` | Array of dimension d x d x K containing the estimated correlation matrices from the Gaussian mixture model, where d is the number of samples in the data `y_profiles` and K is the number of clusters

### Author(s)

Andrea Rau, Cathy Maugis-Rabusseau

### Examples

    ## 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 <- transformRNAseq(countmat, norm="none",
                                transformation="arcsin")$tcounts

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

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

    ## Run the Normal mixture model for K=2
    ## Object of class SummarizedExperiment0
    run2 <- NormMixClusK(y=profiles, K=2, iter=5)

    ## Summary of results
    summary(run)

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

[Package coseq version 1.18.0 Index]