R/RcppExports.R

In caleblareau/RABIT: Regression Analysis with Background InTegration

# This file was generated by Rcpp::compileAttributes
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

#' @title
#' runRABIT
#' @description
#' Performs regression analysis
#'
#' @param x Input variable matrix. Each column represents a variable to be selected.
#' REQUIRED.
#' @param y Response matrix. Each column represents a response vector and Rabit will
#' run variable selection on each response separately. REQUIRED.
#' @param b Background factors. Each column represents a confounding factor to be controlled.
#' Rabit will exclude these effects from the linear model. Default = NULL
#' @param c Background factors for each column of Y. Different from b which inputs background
#' factors shared across all columns of Y, this input is specific for each column of Y. Rabit
#' will search for shared column names between Y and input background factors here and control
#' these factors. default = NULL
#' @param f FDR threshold. Range (0,1]. Rabit estimates the statistical significance of each
#' variable in X and only keeps significant ones in later stepwise forward regression. Set
#' to 1 to skip this step. default = 0.05
#' @param t Transform Y to normal distribution ( = TRUE) or not ( = FALSE). Transforming response
#' Y to normal distribution increases the statistical power of linear regression t-test. However,
#' if Y is very different from the normal distribution (e.g. binary outcome), then this
#' methodology should not be used and logistic regression should be used in this example.
#' default = TRUE
#' @param s Select one best variable in X for each category ( = TRUE) or not ( = FALSE).
#'  When several  variables in X come from the same category (e.g. Transcription factors
#' may have multiple ChIP-Seq profiles available), multiple names appended together
#' with a . (e.g. CatA.V1, CatA.V2 ...) Rabit will chose the best one based on the
#' maximum t-value and these variableswill be returned. default = FALSE
#' @param r Run forward stepwise selection ( = TRUE) or not ( = FALSE). If set to FALSE,
#' Rabit will only calculate the statistical significance of each variable without
#' forward stepwise selection. default = TRUE.
#'
#' @details
#' \code{runRABIT} as a function call is a one-stop shop. Carefully consider each parameter before
#' proceeding.
#'
#' @return
#' Who knows but hopefully it's okay.
#' @import RcppGSL
#' @import RcppArmadillo
#'
#' @export
runRABIT <- function(x, y, b = matrix(0), c = matrix(0), f = 0.05, t = TRUE, s = FALSE, r = TRUE) {
    .Call('RABIT_runRABIT', PACKAGE = 'RABIT', x, y, b, c, f, t, s, r)
}