olsTrain_fun: Gene-specific Regularized Ordinary Least Squares fit...

In gabrielodom/pathwayPCA: Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection

Gene-specific Regularized Ordinary Least Squares fit statistics for supervised PCA

Description

Model statistics for Ordinary Least Squares (OLS) regression by gene.

Usage

olsTrain_fun(x, y, s0.perc = NULL)

Arguments

x	An p \times n predictor matrix.
y	A response vector.
s0.perc	Percentile of the standard error of the slope estimate to be used for regularization. The Default value of NULL will use the median of this distribution.

Details

This function calculates the Sxx, Syy, and Sxy sums from the gene- specific OLS models, then calculates estimates of the regression slopes for each gene and their corresponding regularized test statistics,

t = \hat{\beta} / (sd + e),

where e is a regularization parameter.

If s0.perc is NULL, then e is median of the sd values. Otherwise, e is set equal to quantile(sd, s0.perc).

Value

A list of OLS model statistics:

• tt : The Student's t test statistic the slopes (\beta).

• numer : The estimate of \beta.

• sd : The standard error of the estimates for \beta (the standard error divided by the square root of Sxx).

• fudge : A regularization parameter. See Details for description.

Examples

  # DO NOT CALL THIS FUNCTION DIRECTLY.
  # Use SuperPCA_pVals() instead
  
## Not run: 
  p <- 500
  n <- 50

  x_mat <- matrix(rnorm(n * p), nrow = p, ncol = n)
  time_int <- rpois(n, lambda = 365 * 2)
  
  olsTrain_fun(
    x = x_mat,
    y = time_int
  )

## End(Not run)
  

gabrielodom/pathwayPCA documentation built on July 10, 2023, 3:32 a.m.