fitBumModel {BioNet} | R Documentation

## Fit beta-uniform mixture model to a p-value distribution

### Description

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.

### Usage

    fitBumModel(x, plot = TRUE, starts=10)

### Arguments

`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.

### Value

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.

### Author(s)

Daniela Beisser

### References

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.

### Examples

    data(pvaluesExample)
    pvals <- pvaluesExample[,1]
    bum.mle <- fitBumModel(pvals, plot=TRUE)
    bum.mle

[Package BioNet version 1.40.0 Index]