compGoM {CountClust} | R Documentation

## compGoM: compare GoM model fits across K or across different runs through log-likelihood, BIC and null loglikelihood

### Description

This function takes the `FitGoM`/maptpx fitted model and computes log likelihood, BIC and null model loglikelihood for the fitted GoM models.

### Usage

    compGoM(data, model)

### Arguments

`data` | matrix on which GoM model is fitted (samples along rows, genes along columns)
`model` | `FitGoM` or`maptpx::topics` function output (either a class `topics` or a `list` of class `topics`).

### Value

compGoM_models a vector list that returns the BIC and loglikelihood values for each of the fitted models in `model`.

### Examples

    read.data <- function() {
      x <- tempfile()
      download.file(paste0("https://cdn.rawgit.com/kkdey/",
                             "singleCellRNASeqMouseDeng2014",
                             "/master/data/Deng2014MouseEsc.rda"),
                    destfile = x, quiet = TRUE)
      z <- get(load((x)))
      return(z)
      }
    Deng2014MouseESC <-read.data()

    # Extract observed counts
    deng.counts <- Biobase::exprs(Deng2014MouseESC)

    # Import GoM fitting results
    data("MouseDeng2014.FitGoM")
    names(MouseDeng2014.FitGoM)

    compGoM(data = t(deng.counts),
               model = MouseDeng2014.FitGoM)
    compGoM(data = t(deng.counts),
               model = MouseDeng2014.FitGoM$clust_3)

[Package CountClust version 1.12.0 Index]