# Plot an FDR regression model.

### Description

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

### Usage

1 |

### 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].

### Value

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)
``` |

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