Pearson's Chi-squared Test of Independence

Computes the Pearson's Chi-squared test of independence for each row vector in x.

See the Details section below for further information.

Usage

rowPearsonChiSq(x, g, correct = TRUE, simulate.p.value = FALSE, B = 2000)

Arguments

x

matrix or data.frame.

g

a vector or factor object giving the group for the corresponding elements of x.

correct

a logical indicating whether to apply continuity correction when computing the test statistic for 2 by 2 tables: one half is subtracted from all \(|O - E|\) differences; however, the correction will not be bigger than the differences themselves. No correction is done if simulate.p.value = TRUE.

simulate.p.value

a logical indicating whether to compute p-values by Monte Carlo simulation.

B

an integer specifying the number of replicates used in the Monte Carlo test.

Value

A list containing two elements:

statistic

A numeric vector, the values of the test statistic

significance

A numeric vector, the p-values of the selected test

Details

It is a wrapper to chisq.test function.

Author

Alessandro Barberis

Examples

#Seed
set.seed(1010)

#Data
x = rbind(
  matrix(sample(c("mut", "wt"),30,TRUE), 1, 30),
  matrix(sample(c("m", "f")   ,30,TRUE), 1, 30)
)
g = sample(c("a","b","c"), 30, replace = TRUE)

#Compute
rowPearsonChiSq(x = x, g = g, simulate.p.value = TRUE)
#> $statistic
#> [1] 0.2020202 2.5689935
#> 
#> $significance
#> [1] 1.0000000 0.3098451
#>