GumbelMixpValues {pathwayPCA} | R Documentation

## Calculate the p-values from an optimal mixture of Weibull Extreme Value distributions for supervised PCA

### Description

Calculate the p-values of test statistics from a mixture of two Weibull Extreme Value distributions.

### Usage

    GumbelMixpValues(tScore_vec, pathwaySize_vec, optimParams_vec)

### Arguments

`tScore_vec` | A vector of the maximum absolute t-scores for each pathway (returned by the `pathway_tScores` function) when under the alternative model.
`pathwaySize_vec` | A vector of the number of genes in each pathway.
`optimParams_vec` | The NAMED vector of optimal Weibull Extreme Value mixture distribution parameters returned by the `OptimGumbelMixParams` function.

### Details

The likelihood function is equation (4) in Chen et al (2008): a mixture of two Gumbel Extreme Value probability density functions, with mixing proportion p. Within the code of this function, the values `mu1`, `mu2` and `s1`, `s2` are placeholders for the mean and precision, respectively.

See https://doi.org/10.1093/bioinformatics/btn458 for more information.

### Value

A named vector of the estimated raw p-values for each gene pathway.

### See Also

`OptimGumbelMixParams`; `pathway_tScores`; `SuperPCA_pVals`

### Examples

      # DO NOT CALL THIS FUNCTION DIRECTLY.
      # Use SuperPCA_pVals() instead.

    ## Not run:
      ###  Load the Example Data  ###
      data("colon_pathwayCollection")
      n_int <- lengths(colon_pathwayCollection$pathways)

      ###  Simulate Maximum Absolute Control t-Values  ###
      # The SuperPCA algorithm defaults to 20 threshold values; the example
      #   pathway collection has 15 pathways.
      t_mat <- matrix(rt(15 * 20, df = 5), nrow = 15)

      absMax <- function(vec){
        vec[which.max(abs(vec))]
      }
      tAbsMax_num <- apply(t_mat, 1, absMax)

      ###  Calculate Optimal Parameters for the Gumbel Distribution  ###
      optParams_num <- OptimGumbelMixParams(
        max_tControl_vec = tAbsMax_num,
        pathwaySize_vec = n_int
      )

      ###  Simulate Maximum Absolute t-Values  ###
      tObs_mat <- matrix(rt(15 * 20, df = 3), nrow = 15)
      tObsAbsMax_num <- apply(tObs_mat, 1, absMax)

      ###  Calculate Observed-t-score p-Values  ###
      GumbelMixpValues(
        tScore_vec = tObsAbsMax_num,
        pathwaySize_vec = n_int,
        optimParams_vec = optParams_num
      )

    ## End(Not run)

[Package pathwayPCA version 1.8.0 Index]