| olsTrain_fun {pathwayPCA} | R Documentation |
Model statistics for Ordinary Least Squares (OLS) regression by gene.
olsTrain_fun(x, y, s0.perc = NULL)
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 |
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).
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.
# 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)