ztobins: Binning of z-scores and estimation of the probabilities in each bin for the null and non-null states.

Description

For each study, the function discretizes the z-scores into bins and estimates the probabilities in each bin for the null and non-null states.

Usage

ztobins(zmat, n.association.status = 3, n.bins = 120, type = 0, df = 7, central.prop = 0.5)

Arguments

zmat

Matrix of z-scores of the features (in rows) in each study (columns).

n.association.status

either 2 for no-association\association or 3 for no-associtation\negative-association\positive-association.

n.bins

Number of breaks in the discretization of the z-score axis (the number of bins is n.bins- 1). If the number of z-scores per study is small, we set n.bins to a number lower than the default of 120 (about equals to the square root of the number of z-scores).

type

Type of fitting used for f; 0 is a natural spline, 1 is a polynomial, in either case with degrees of freedom df (so total degrees of freedom including the intercept is df+1).

Degrees of freedom for fitting the estimated density f(z).

central.prop

Central proportion of the z-scores used like the area of zero-assumption to estimate pi0.

Value

pdf.binned.z: A 3-dimensional array which contains for each study (first dimension), the probabilities of a z-score to fall in the bin (second dimension), under each hypothesis status (third dimension). The third dimension can be of size 2 or 3, depending on the number of association states: if the association can be either null or only in one direction, the dimension is 2; if the association can be either null, or positive, or negative, the dimension is 3.
binned.z.mat: A matrix of the bin numbers for each the z-scores (rows) in each study (columns).

Details

This utility function outputs the first two arguments to be input in the main function repfdr.

Examples

Run this code

## Not run: 
# 
# # three association states case (H in {-1,0,1}):
# download.file('http://www.math.tau.ac.il/~ruheller/repfdr_RData/zmat.RData',destfile = "zmat.RData")
# load(file = "zmat.RData")
# 
# input.to.repfdr3 <- ztobins(zmat, 3, df = 15)
# pbz    <- input.to.repfdr3$pdf.binned.z
# bz     <- input.to.repfdr3$binned.z.mat
# 
# # two association states case (H in {0,1}):
# data(zmat_sim)
# 
# input.to.repfdr <- ztobins(zmat_sim, 2)
# pbz_sim    <- input.to.repfdr$pdf.binned.z
# bz_sim     <- input.to.repfdr$binned.z.mat
# ## End(Not run)