Description Usage Arguments Details Value Examples
View source: R/superPC_model_LS.R
Model statistics for Ordinary Least Squares (OLS) regression by gene.
1 | 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.
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