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 |

`a` |
Fitted parameter |

`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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