calf_exact_binary_subset {CALF}

calf_exact_binary_subset

Description

Runs Coarse Approximation Linear Function on a random subset of binary data provided, with the ability to precisely control the number of case and control data used.

Usage

calf_exact_binary_subset(
  data,
  nMarkers,
  nCase,
  nControl,
  times = 1,
  optimize = "pval",
  verbose = FALSE
)

Arguments

data

Matrix or data frame. First column must contain case/control dummy coded variable.

nMarkers

Maximum number of markers to include in creation of sum.

nCase

Numeric. A value indicating the number of case data to use.

nControl

Numeric. A value indicating the number of control data to use.

times

Numeric. Indicates the number of replications to run with randomization.

optimize

Criteria to optimize. Indicate "pval" to optimize the p-value corresponding to the t-test distinguishing case and control. Indicate "auc" to optimize the AUC.

verbose

Logical. Indicate TRUE to print activity at each iteration to console. Defaults to FALSE.

Value

A data frame containing the chosen markers and their assigned weight (-1 or 1)

The optimal AUC or pval for the classification. If multiple replications are requested, a data.frame containing all optimized values across all replications is returned.

aucHist A histogram of the AUCs across replications, if applicable.

Examples

calf_exact_binary_subset(data = CaseControl, nMarkers = 6, nCase = 5, nControl = 8, times = 5)