superpc.train: Train a supervised PCA model

Description Usage Arguments Details Value See Also Examples

View source: R/superPC_model_train.R

Description

Computes feature scores for p_{path} features of a pathway via a linear model fit.

Usage

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superpc.train(
  data,
  type = c("survival", "regression", "categorical"),
  s0.perc = NULL
)

Arguments

data

A list of test data:

  • x : A "tall" pathway data frame (p_{path} \times N).

  • y : A response vector corresponding to type.

  • censoring.status : If type = "survival", the censoring indicator (1 - the observed event indicator. Otherwise, NULL.

  • featurenames : A character vector of the measured -Omes in x.

type

What model relates y and x? Options are "survival", "regression", or "categorical".

s0.perc

A stabilization parameter on the interval [0,1]. This is an internal argument to each of the called functions. The default value is NULL to ensure an appropriate value is determined internally.

Details

This function is a switch call to coxTrain_fun (for type = "survival"), olsTrain_fun (for type = "regression"), or glmTrain_fun (for type = "categorical").

Value

A list containing:

See Also

superpc.st; SuperPCA_pVals

Examples

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  # DO NOT CALL THIS FUNCTION DIRECTLY.
  # Use SuperPCA_pVals() instead
  
## Not run: 
  data("colon_pathwayCollection")
  data("colonSurv_df")
  
  colon_OmicsSurv <- CreateOmics(
    assayData_df = colonSurv_df[,-(2:3)],
    pathwayCollection_ls = colon_pathwayCollection,
    response = colonSurv_df[, 1:3],
    respType = "surv"
  )
  
  asthmaGenes_char <-
    getTrimPathwayCollection(colon_OmicsSurv)[["KEGG_ASTHMA"]]$IDs
    
  data_ls <- list(
    x = t(getAssay(colon_OmicsSurv))[asthmaGenes_char, ],
    y = getEventTime(colon_OmicsSurv),
    censoring.status = getEvent(colon_OmicsSurv),
    featurenames = asthmaGenes_char
  )
  
  superpc.train(
    data = data_ls,
    type = "surv"
  )

## End(Not run)
  

pathwayPCA documentation built on Dec. 15, 2020, 6:14 p.m.