lm_qvalue {swfdr} | R Documentation

## Compute qvalues taking into account a matrix of covariates

### 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)

[Package swfdr version 1.16.0 Index]