binom_test {rstatix} | R Documentation |
Exact Binomial Test
Description
Performs exact binomial test and pairwise comparisons following a
significant exact multinomial test. Wrapper around the R base function
link[stats]{binom.test}()
that returns a data frame as a result.
Usage
binom_test(
x,
n,
p = 0.5,
alternative = "two.sided",
conf.level = 0.95,
detailed = FALSE
)
pairwise_binom_test(
x,
p.adjust.method = "holm",
alternative = "two.sided",
conf.level = 0.95
)
pairwise_binom_test_against_p(
x,
p = rep(1/length(x), length(x)),
p.adjust.method = "holm",
alternative = "two.sided",
conf.level = 0.95
)
Arguments
x |
numeric vector containing the counts. |
n |
number of trials; ignored if |
p |
a vector of probabilities of success. The length of p must be the same as the number of groups specified by x, and its elements must be greater than 0 and less than 1. |
alternative |
indicates the alternative hypothesis and must be
one of |
conf.level |
confidence level for the returned confidence interval. |
detailed |
logical value. Default is FALSE. If TRUE, a detailed result is shown. |
p.adjust.method |
method to adjust p values for multiple comparisons. Used when pairwise comparisons are performed. Allowed values include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p value (not recommended), use p.adjust.method = "none". |
Value
return a data frame containing the p-value and its significance. with some the following columns:
-
group, group1, group2
: the categories or groups being compared. -
statistic
: the number of successes. -
parameter
: the number of trials. -
p
: p-value of the test. -
p.adj
: the adjusted p-value. -
method
: the used statistical test. -
p.signif, p.adj.signif
: the significance level of p-values and adjusted p-values, respectively. -
estimate
: the estimated probability of success. -
alternative
: a character string describing the alternative hypothesis. -
conf.low,conf.high
: Lower and upper bound on a confidence interval for the probability of success.
The returned object has an attribute called args, which is a list holding the test arguments.
Functions
-
binom_test()
: performs exact binomial test. Wrapper around the R base functionbinom.test
that returns a dataframe as a result. -
pairwise_binom_test()
: performs pairwise comparisons (binomial test) following a significant exact multinomial test. -
pairwise_binom_test_against_p()
: performs pairwise comparisons (binomial test) following a significant exact multinomial test for given probabilities.
See Also
Examples
# Exact binomial test
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Data: 160 mice with cancer including 95 male and 65 female
# Q1: Does cancer affect more males than females?
binom_test(x = 95, n = 160)
# => yes, there are a significant difference
# Q2: compare the observed proportion of males
# to an expected proportion (p = 3/5)
binom_test(x = 95, n = 160, p = 3/5)
# => there are no significant difference
# Multinomial test
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Data
tulip <- c(red = 81, yellow = 50, white = 27)
# Question 1: are the color equally common ?
# this is a test of homogeneity
res <- multinom_test(tulip)
res
attr(res, "descriptives")
# Pairwise comparisons between groups
pairwise_binom_test(tulip, p.adjust.method = "bonferroni")
# Question 2: comparing observed to expected proportions
# this is a goodness-of-fit test
expected.p <- c(red = 0.5, yellow = 0.33, white = 0.17)
res <- multinom_test(tulip, expected.p)
res
attr(res, "descriptives")
# Pairwise comparisons against a given probabilities
pairwise_binom_test_against_p(tulip, expected.p)