lm_pi0: Estimation of pi0, proportion of p-values consistent with a...

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

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)

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