Predict Case Probabilities with Full Bayesian Logic Regression
Predicts case probabilities for binary data (usually SNP data dichotomized with
snp2bin
) based on an MCMC sample of Bayesian logic regression models
obtained with fblr
.
predictFBLR(file, bin, kmax = 10, int.level = 2)
file |
character string naming file where MCMC sample is stored. |
bin |
matrix of binary variables to make predictions for. One row is one
observation. The number of binary variables has to be the same as used in |
kmax |
the maximum number of allowed logic predictors used in |
int.level |
the maximum number of allowed binaries in a logic predictor
used in |
Vector of length nrow(bin)
with predicted case probabilities.
Arno Fritsch, arno.fritsch@uni-dortmund.de
## Not run: # Use fblr on some simulated SNP data snp <- matrix(rbinom(500 * 20, 2, 0.3), ncol = 20) bin <- snp2bin(snp) int <- apply(bin,1,function(x) (x[1] == 1 & x[3] == 0)*1) case.prob <- exp(-0.5+log(5)*int)/(1+exp(-0.5+log(5)*int)) y <- rbinom(nrow(snp),1,prob=case.prob) fblr(y, bin, niter=1000, nburn=0) # Prediction for some new observations newbin <- snp2bin(matrix(rbinom(100 * 20, 2, 0.3), ncol = 20)) predictFBLR("fblr_mcmc.txt",newbin) ## End(Not run)
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