kernelUnitInterval: Estimate a density on the unit interval or unit square via...

In StoreyLab/fFDR: Functional FDR for multiple hypothesis testing

Description

Provide density estimates that are needed by fqvalue and estimate_fpi0

Usage

kernelUnitInterval(
  x,
  transformation = "probit",
  eval.points = x,
  subsample = 1e+05,
  cv = FALSE,
  epsilon = 1e-15,
  epsilon.max = 0.999,
  maxk = 100,
  trim = 0.02,
  ...
)

Arguments

x	Either a vector or a 2-column matrix
transformation	Either probit (default), complementary log-log, or identity (not recommended)
eval.points	Points at which to evaluate the estimate, default x
subsample	Number of points that are randomly subsampled for computing the fit; useful for computational efficiency and for ensuring the density estimation does not run out of memory. NULL means no the fit is performed on all points
cv	Whether to use generalized cross-validation to choose the nn (nearest neighbor) smoothing parameter
epsilon	How close values are allowed to come to 0
epsilon.max	How close values are allowed to come to 1
maxk	maxk argument passed to locfit
trim	In one-dimensional fitting, the very edges often have high variance. This parameter fixes the estimate on the intervals (0, trim) and (1 - trim, 1).
...	additional arguments to be passed to lp in locfit, used only if cv=FALSE

Examples

set.seed(12)
sim.ttests = simulate_t_tests(m = 1000)
p <- sim.ttests$p
z0 <- sim.ttests$n
z <- rank(z0) / length(z0)
lambda <- 0.3
phi <- as.numeric(p > lambda)
kernelUnitInterval(z[phi == 1], eval.points = z, cv = FALSE)

StoreyLab/fFDR documentation built on March 8, 2021, 10:14 p.m.