# plotFDR: Plot an FDR regression model. In FDRreg: False discovery rate regression

## Description

Plots the results of a fitted FDR regression model from FDRreg.

## Usage

 `1` ```plotFDR(fdrr, Q=0.1, showrug=TRUE, showfz=TRUE, showsub=TRUE) ```

## Arguments

 `fdrr` A fitted model object from FDRreg. `Q` The desired level at which FDR should be controlled. Defaults to 0.1, or 10 percent. `showrug` Logical flag indicating whether the findings at the specified FDR level should be displayed in a rug plot beneath the histogram. Defaults to TRUE. `showfz` Logical flag indicating the fitted marginal density f(z) should be plotted. Defaults to TRUE. `showsub` Logical flag indicating whether a subtitle should be displayed describing features of the plot. Defaults to TRUE.

## Details

It is important to remember that localfdr (and therefore global FDR) is not necessarily monotonic in z, because the regression model allows the prior probability that z[i] is a signal to change with covariates x[i].

No return value.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30``` ```library(FDRreg) # Simulated data P = 2 N = 10000 betatrue = c(-3.5,rep(1/sqrt(P), P)) X = matrix(rnorm(N*P), N,P) psi = crossprod(t(cbind(1,X)), betatrue) wsuccess = 1/{1+exp(-psi)} # Some theta's are signals, most are noise gammatrue = rbinom(N,1,wsuccess) table(gammatrue) # Density of signals thetatrue = rnorm(N,3,0.5) thetatrue[gammatrue==0] = 0 z = rnorm(N, thetatrue, 1) hist(z, 100, prob=TRUE, col='lightblue', border=NA) curve(dnorm(x,0,1), add=TRUE, n=1001) ## Not run: # Fit the model fdr1 <- FDRreg(z, covars=X, nmc=2500, nburn=100, nmids=120, nulltype='theoretical') # Show the empirical-Bayes estimate of the mixture density # and the findings at a specific FDR level Q = 0.1 plotFDR(fdr1, Q=Q, showfz=TRUE) ## End(Not run) ```

FDRreg documentation built on May 2, 2019, 12:36 a.m.