tqvalue: The exponential tilting mixture model

In chongma1989/tiltmod: Exponential Tilting Method for Reproducible Screening of Large Scale Testing Problem

Description

This function tilts the mixture model fitted from the training tail-areas (or p-values) by conditioning on the average of local fdr's from the testing tail-areas (or p-values)

Usage

tqvalue(xl, xt, w = NULL, a = NULL, precision = 1e-08, MaxIter = 10000L,
  interval = NULL, adjust = TRUE, method = c("m1", "m2"),
  type = c("left tail area", "pvalue"), alpha = 0.9, q = 0.1,
  ncores = 1, rel.tol = .Machine$double.eps^0.25,
  tol = .Machine$double.eps^0.5, eps = .Machine$double.eps^0.6)

Arguments

xl	The training left-tail areas (or p-values)
xt	The testing left-tail areas (or p-values)
w	A vector of two numeric values, representing the weights of the uniform and Beta distributions. See UBMM.
a	A vector of two initial parameter values for Beta distribution. See UBMM.
precision	The precision for convergence. Default value is 1e-8.
MaxIter	The maximum iteration for the EM algorhthm.
interval	A vector of two numeric values, which determines the range to search the optimal theta. Default is c(-1000L,1000L).
adjust	Whether or not to do the model adjustment. Default is TRUE.
method	A character chosen from m1, m2. Default is m1.
type	A character value, chosen from “left tail area” and “pvalue”. Default is “left tail area”.
alpha	A numeric value. Used in method “m1” to determine the probably null region. Default is 0.9.
q	A numeric value. The global false discovery rate used in method “m2”, to determine the probable null region. Default is 0.1.
ncores	The number of cpus used for implementing this function.
rel.tol	the accuracy used in integrate.
tol	the accuracy used in uniroot.
eps	the smallest positive precision. If x < eps, x = eps; if x > 1-eps, x = 1-eps.

Value

A dataframe includes xl, xt, fdr, FDR, tfdr, and tFDR, respectively. fdr and FDR are the local and global false discovery rate for each value of xt. tfdr and tFDR are the corresponding tilted local and global false discovery rate, respectively.

The optimal theta calculated by solving log(E(exp(thetah(x))))-ctheta, where c=mean(h(xt)).

Examples

xl=c(rbeta(50,0.2,0.2),runif(950))
xt=c(rbeta(50,0.1,0.1),runif(950))

## Not run: 
tqvalue(xl,xt,ncores=4,adjust=FALSE,type="left tail area")

## End(Not run)

chongma1989/tiltmod documentation built on May 7, 2019, 8:38 a.m.