Tools for FDR problems, including false discovery rate regression. Fits models whereby the local false discovery rate may depend upon covariates, either via a linear or additive logistic regression model.

Package: | FDRreg |

Type: | Package |

Version: | 1.0 |

Date: | 2014-02-25 |

License: | GPL (>=3) |

The workhouse function is FDRreg(z,X, ...), where z is an observed vector of z statistics, and X is a matrix of covariates. Do not add a column of ones to X to get an intercept term; the function does that for you, just like R's base lm() and glm() functions.

Author: James G. Scott, with contributions from Rob Kass and Jesse Windle.

Maintainer: James G. Scott <james.scott@mccombs.utexas.edu>

False discovery rate regression: application to neural synchrony detection in primary visual cortex. James G. Scott, Ryan C. Kelly, Matthew A. Smith, Pengcheng Zhou, and Robert E. Kass. arXiv:1307.3495 [stat.ME].

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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | ```
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)
# Posterior distribution of the intercept
hist(fdr1$betasave[,1], 20)
# Compare actual versus estimated prior probabilities of being a signal
plot(wsuccess, fdr1$priorprob)
# Covariate effects
plot(X[,1], log(fdr1$priorprob/{1-fdr1$priorprob}), ylab='Logit of prior probability')
plot(X[,2], log(fdr1$priorprob/{1-fdr1$priorprob}), ylab='Logit of prior probability')
# Local FDR
plot(z, fdr1$localfdr, ylab='Local false-discovery rate')
# Extract findings at level FDR = Q
myfindings = which(fdr1$FDR <= Q)
## End(Not run)
``` |

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