lm_pi0 {swfdr} | R Documentation

## Estimation of pi0, proportion of p-values consistent with a null hypothesis

### Description

Estimation of pi0, proportion of p-values consistent with a null hypothesis

### Usage

    lm_pi0(
      p,
      lambda = seq(0.05, 0.95, 0.05),
      X,
      type = c("logistic", "linear"),
      smooth.df = 3,
      threshold = TRUE,
      smoothing = c("unit.spline", "smooth.spline")
    )

### Arguments

`p` | numeric vector, p-values
`lambda` | numeric vector, thresholds used to bin pvalues, must be in [0,1).
`X` | numeric matrix, covariates that might be related to p values (one test per row, one variable per column).
`type` | character, type of regression used to fit features to pvalues
`smooth.df` | integer, degrees of freedom when estimating pi0(x) with a smoother.
`threshold` | logical, if TRUE, all estimates are thresholded into unit interval; if FALSE, all estimates are left as they are computed
`smoothing` | character, type of smoothing used to fit pi0

### Value

pi0 numerical vector of smoothed estimate of pi0(x). The length is the number of rows in X.

pi0.lambda numeric matrix of estimated pi0(x) for each value of lambda. The number of columns is the number of tests, the number of rows is the length of lambda.

lambda numeric vector of thresholds used in calculating pi0.lambda

pi0.smooth (only output with smoothing="smooth.spline") Matrix of fitted values from the smoother fit to the pi0(x) estimates at each value of lambda (same number of rows and columns as pi0.lambda)

### Examples

    # define a covariate
    X <- seq(-1,2,length=1000)
    # set probability of being null
    pi0 <- 1/4*X + 1/2
    # generate null/alternative p-values
    nullI <- rbinom(1000,prob=pi0,size=1)> 0
    # vector of p-values
    pValues <- rep(NA,1000)
    pValues[nullI] <- runif(sum(nullI)) # from U(0,1)
    pValues[!nullI] <- rbeta(sum(!nullI),1,2) # from Beta
    pi0x <- lm_pi0(pValues, X=X)

[Package swfdr version 1.16.0 Index]