compareICL {coseq} | R Documentation

## Compare corrected ICL values after data transformation

### Description

Compare the corrected ICL values after applying the arcsin, logit, and logMedianRef transformations in a coseq analysis

### Usage

    compareICL(x)

### Arguments

`x` | A list made up of `coseqResults` objects. At the current time, this function only supports the comparison of `coseqResults` objects using `model="Normal"` and `transformation = c("arcsin", "logit", "logMedianRef")`

### Value

A plot of corrected ICL values for the models included in `x` (the list of `coseqResults` objects)

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

    ## Run the Normal mixture model for K = 2,3,4
    run_arcsin <- coseq(object=countmat, K=2:4, iter=5, transformation="arcsin",
                        model="Normal", seed=12345)
    run_arcsin

    ## Plot and summarize results
    plot(run_arcsin)
    summary(run_arcsin)

    ## Compare ARI values for all models (no plot generated here)
    ARI <- compareARI(run_arcsin, plot=FALSE)

    ## Compare ICL values for models with arcsin and logit transformations
    run_logit <- coseq(object=countmat, K=2:4, iter=5, transformation="logit",
                       model="Normal")
    compareICL(list(run_arcsin, run_logit))

    ## Use accessor functions to explore results
    clusters(run_arcsin)
    likelihood(run_arcsin)
    nbCluster(run_arcsin)
    ICL(run_arcsin)

    ## Examine transformed counts and profiles used for graphing
    tcounts(run_arcsin)
    profiles(run_arcsin)

    ## Run the K-means algorithm for logclr profiles for K = 2,..., 20
    run_kmeans <- coseq(object=countmat, K=2:20, transformation="logclr",
                        model="kmeans")
    run_kmeans

[Package coseq version 1.14.0 Index]