Fisher Exact Test — fisher_exact • structToolbox

A fisher exact test is used to analyse contingency tables by comparing the number of correctly/incorrectly predicted group labels. A multiple test corrected p-value indicates whether the number of measured values is significantly different between groups.

Usage

fisher_exact(alpha = 0.05, mtc = "fdr", factor_name, factor_pred, ...)

Arguments

alpha

(numeric) The p-value cutoff for determining significance. The default is 0.05.

mtc

(character) Multiple test correction method. Allowed values are limited to the following:

"bonferroni": Bonferroni correction in which the p-values are multiplied by the number of comparisons.
"fdr": Benjamini and Hochberg False Discovery Rate correction.
"none": No correction.

The default is "fdr".

factor_name

(character) The name of a sample-meta column to use.

factor_pred

(data.frame) A data.frame, where each column is a factor of predicted group labels to compare with the true groups labels.

...

Additional slots and values passed to struct_class.

Value

A fisher_exact object with the following output slots:

`p_value`	(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.
`significant`	(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.

Inheritance

A fisher_exact object inherits the following struct classes:

[fisher_exact] >> [model] >> [struct_class]

Examples

M = fisher_exact(
      alpha = 0.05,
      mtc = "fdr",
      factor_name = "V1",
      factor_pred = data.frame(id=NA))

# load some data
D=MTBLS79_DatasetExperiment()

# prepare predictions based on NA
pred=as.data.frame(is.na(D$data))
pred=lapply(pred,factor,levels=c(TRUE,FALSE))
pred=as.data.frame(pred)

# apply method
M = fisher_exact(alpha=0.05,mtc='fdr',factor_name='Class',factor_pred=pred)
M=model_apply(M,D)