Function for combining p-values by performing Fisher's method. The approach as described by Fisher's combines p-values to a statistic

*S = -2 ∑^k \log p*

, which follows a *χ^2* distribution
with *2k* degrees of freedom.

1 2 | ```
fisher.method(pvals, method = c("fisher"), p.corr = c("bonferroni",
"BH", "none"), zero.sub = 1e-05, na.rm = FALSE, mc.cores=NULL)
``` |

`pvals` |
A matrix or data.frame containing the p-values from the single tests |

`method` |
A string indicating how to combine the p-values for deriving a sumary p-value. Currently only the classical approach described by Fisher is implemented. |

`p.corr` |
Method for correcting the summary p-values. BH: Benjamini-Hochberg (default); Bonferroni's method or no ('none') correction are currently supported. |

`zero.sub` |
Replacement for p-values of 0 |

`na.rm` |
A flag indicating whether NA values should be removed from the analysis. |

`mc.cores` |
Currently ignored |

As `log(0)`

results in `Inf`

we replace p-values of 0 by default with a
small float. If you want to keep them as 0 you have to provide 0
as a parameter in `zero.sub`

.

Note that only p-values between 0 and 1 are allowed to be passed to this method.

This method returns a data.frame containing the following columns

`S ` |
The statistic |

`num.p ` |
The number of p-values used to calculate S |

`p.value ` |
The overall p-value |

`p.adj` |
The adjusted p-value |

Karl Kugler <karl@eigenlab.net>

Fisher, R.A. (1925). Statistical Methods for Research Workers. Oliver and Boyd (Edinburgh). Moreau, Y.et al. (2003). Comparison and meta-analysis of microarray data: from the bench to the computer desk. Trends in Genetics, 19(10), 570-577.

`fisher.sum`

, `fisher.method.perm`

1 2 3 4 5 | ```
set.seed(123)
pp <- matrix(c(runif(20),c(0.001,0.02,0.03,0.001)), ncol=4)
pp[2,3] <- NA
fisher.method(pp) #returns one NA row
fisher.method(pp, na.rm=TRUE) #ignore NA entry in that row
``` |

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