lm_qvalue: Compute qvalues taking into account a matrix of covariates

In swfdr: Science-wise false discovery rate and proportion of true null hypotheses estimation

Description

The recipe for turning pvalues into qvalues is adapted from package 'qvalue' and articles by Storey, Tibshirani, Taylor, Siegmund.

Usage

lm_qvalue(
  p,
  X,
  pfdr = FALSE,
  pi0 = NULL,
  smoothing = c("unit.spline", "smooth.spline"),
  ...
)

Arguments

p	numeric vector of p-values
X	matrix of covariates (can be missing if pi0 is specified instead)
pfdr	logical, making estimates robust for small p-values and a small sample size
pi0	list with pi0 estimates from lm_pi0
smoothing	character, type of smoothing used to fit pi0. Note the default in this function is different than in lm_pi0.
...	other parameters (passed on to lm_pi0 if pi0 is not provided)

Value

list

Examples

# define a covariate
X <- rep(c(0, 1), each=1000)
# generate p-values, randomly for group 0 and with low values for group 1
pVal <- c(runif(1000), rbeta(1000, 0.2, 1))
# compute qvalues, using the covariate
qVal <- lm_qvalue(pVal, X=X)

swfdr documentation built on Nov. 8, 2020, 8:29 p.m.