PermTestCateg.Rd
Given an OmicsCateg
object and a list of pathway PCs from
the ExtractAESPCs
function, test if each pathway with
features recorded in the bioassay design matrix is significantly related
to the categorical response.
PermTestCateg( OmicsCateg, pathwayPCs_ls, numReps = 0L, parallel = FALSE, numCores = NULL, ... ) # S4 method for OmicsCateg PermTestCateg( OmicsCateg, pathwayPCs_ls, numReps = 0L, parallel = FALSE, numCores = NULL, ... )
OmicsCateg  A data object of class 

pathwayPCs_ls  A list of pathway PC matrices returned by the

numReps  How many permutations to estimate the \(p\)value? Defaults
to 0 (that is, to estimate the \(p\)value parametrically). If

parallel  Should the computation be completed in parallel? Defaults to

numCores  If 
...  Dots for additional internal arguments (currently unused). 
A named vector of pathway permutation \(p\)values.
This function takes in a list of the first principal components
from each pathway and an object of class OmicsCateg
. This function
will then calculate the AIC of a multivariate generalized linear model (via
the glm
function with a binomial
error family) with the original observations as response and the pathway
principal components as the predictor matrix.
Then, this function will create numReps
permutations of the
categorical response, fit models to each of these permuted responses
(holding the path predictor matrix fixed), and calculate the AIC of each
model. This function will return a named vector of permutation
\(p\)values, where the value for each pathway is the proportion of
models for which the AIC of the permuted response model is less than the
AIC of the original model. Note that the AIC and loglikelihood are
proportional because the number of parameters in each pathway is constant.
In future versions, this function will also be able to calculate permuted \(p\)values for multinomial logistic regression and proportional odds logistic regression models, for nary and ordered categorical responses, respectively.
# DO NOT CALL THIS FUNCTION DIRECTLY. # Use AESPCA_pVals() instead if (FALSE) { ### Load the Example Data ### data("colonSurv_df") data("colon_pathwayCollection") ### Create an OmicsSurv Object ### colon_Omics < CreateOmics( assayData_df = colonSurv_df[, (2:3)], pathwayCollection_ls = colon_pathwayCollection, response = colonSurv_df[, c(1,3)], respType = "categ" ) ### Extract Pathway PCs and Loadings ### colonPCs_ls < ExtractAESPCs( object = colon_Omics, parallel = TRUE, numCores = 2 ) ### Pathway pValues ### PermTestCateg( OmicsCateg = colon_Omics, pathwayPCs_ls = colonPCs_ls$PCs, parallel = TRUE, numCores = 2 ) }