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

Description Usage Arguments Details Value Examples

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

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

`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.

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.

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)