olsTrain_fun {pathwayPCA} | R Documentation

## 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{β} / (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 (β).

- `numer` : The estimate of β.

- `sd` : The standard error of the estimates for β (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)

[Package pathwayPCA version 1.0.0 Index]