Description Usage Arguments Value Author(s) References Examples
The function fits a beta-uniform mixture model to a given p-value distribution. The BUM method was introduced by Stan Pounds and Steve Morris to model the p-value distribution as a signal-noise decompostion. The signal component is assumed to be B(a,1)-distributed, whereas the noise component is uniform-distributed under the null hypothesis.
1 | fitBumModel(x, plot = TRUE, starts=10)
|
x |
Numeric vector of p-values. |
plot |
Boolean value, whether to plot a histogram and qqplot of the p-values with the fitted model. |
starts |
Numeric value giving the number of starts for the optimization. |
Maximum likelihood estimator object for the fitted bum model. List of class fb with the following elements:
lambda |
Fitted parameter lambda for the beta-uniform mixture model. |
a |
Fitted parameter a for the beta-uniform mixture model. |
negLL |
Negative log-likelihood. |
pvalues |
P-value vector. |
Daniela Beisser
S. Pounds, S.W. Morris (2003) Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values. Bioinformatics, 19(10): 1236-1242.
1 2 3 4 | data(pvaluesExample)
pvals <- pvaluesExample[,1]
bum.mle <- fitBumModel(pvals, plot=TRUE)
bum.mle
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