prop_test {rstatix}  R Documentation 
Proportion Test
Description
Performs proportion tests to either evaluate the homogeneity of proportions (probabilities of success) in several groups or to test that the proportions are equal to certain given values.
Wrappers around the R base function prop.test()
but have
the advantage of performing pairwise and rowwise ztest of two proportions,
the posthoc tests following a significant chisquare test of homogeneity
for 2xc and rx2 contingency tables.
Usage
prop_test(
x,
n,
p = NULL,
alternative = c("two.sided", "less", "greater"),
correct = TRUE,
conf.level = 0.95,
detailed = FALSE
)
pairwise_prop_test(xtab, p.adjust.method = "holm", ...)
row_wise_prop_test(xtab, p.adjust.method = "holm", detailed = FALSE, ...)
Arguments
x 
a vector of counts of successes, a onedimensional table with two entries, or a twodimensional table (or matrix) with 2 columns, giving the counts of successes and failures, respectively. 
n 
a vector of counts of trials; ignored if 
p 
a vector of probabilities of success. The length of

alternative 
a character string specifying the alternative
hypothesis, must be one of 
correct 
a logical indicating whether Yates' continuity correction should be applied where possible. 
conf.level 
confidence level of the returned confidence interval. Must be a single number between 0 and 1. Only used when testing the null that a single proportion equals a given value, or that two proportions are equal; ignored otherwise. 
detailed 
logical value. Default is FALSE. If TRUE, a detailed result is shown. 
xtab 
a crosstabulation (or contingency table) with two columns and multiple rows (rx2 design). The columns give the counts of successes and failures respectively. 
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". 
... 
Other arguments passed to the function 
Value
return a data frame with some the following columns:

n
: the number of participants. 
group
: the categories in the rowwise proportion tests. 
statistic
: the value of Pearson's chisquared test statistic. 
df
: the degrees of freedom of the approximate chisquared distribution of the test statistic. 
p
: pvalue. 
p.adj
: the adjusted pvalue. 
method
: the used statistical test. 
p.signif, p.adj.signif
: the significance level of pvalues and adjusted pvalues, respectively. 
estimate
: a vector with the sample proportions x/n. 
estimate1, estimate2
: the proportion in each of the two populations. 
alternative
: a character string describing the alternative hypothesis. 
conf.low,conf.high
: Lower and upper bound on a confidence interval. a confidence interval for the true proportion if there is one group, or for the difference in proportions if there are 2 groups and p is not given, or NULL otherwise. In the cases where it is not NULL, the returned confidence interval has an asymptotic confidence level as specified by conf.level, and is appropriate to the specified alternative hypothesis.
The returned object has an attribute called args, which is a list holding the test arguments.
Functions

prop_test()
: performs onesample and twosamples ztest of proportions. Wrapper around the functionprop.test()
. 
pairwise_prop_test()
: pairwise comparisons between proportions, a posthoc tests following a significant chisquare test of homogeneity for 2xc design. Wrapper aroundpairwise.prop.test()

row_wise_prop_test()
: performs rowwise ztest of two proportions, a posthoc tests following a significant chisquare test of homogeneity for rx2 contingency table. The ztest of two proportions is calculated for each category (row).
Examples
# Comparing an observed proportion to an expected proportion
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
prop_test(x = 95, n = 160, p = 0.5, detailed = TRUE)
# Comparing two proportions
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Data: frequencies of smokers between two groups
xtab < as.table(rbind(c(490, 10), c(400, 100)))
dimnames(xtab) < list(
group = c("grp1", "grp2"),
smoker = c("yes", "no")
)
xtab
# compare the proportion of smokers
prop_test(xtab, detailed = TRUE)
# Homogeneity of proportions between groups
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# H0: the proportion of smokers is similar in the four groups
# Ha: this proportion is different in at least one of the populations.
#
# Data preparation
grp.size < c( 106, 113, 156, 102 )
smokers < c( 50, 100, 139, 80 )
no.smokers < grp.size  smokers
xtab < as.table(rbind(
smokers,
no.smokers
))
dimnames(xtab) < list(
Smokers = c("Yes", "No"),
Groups = c("grp1", "grp2", "grp3", "grp4")
)
xtab
# Compare the proportions of smokers between groups
prop_test(xtab, detailed = TRUE)
# Pairwise comparison between groups
pairwise_prop_test(xtab)
# Pairwise proportion tests
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Data: Titanic
xtab < as.table(rbind(
c(122, 167, 528, 673),
c(203, 118, 178, 212)
))
dimnames(xtab) < list(
Survived = c("No", "Yes"),
Class = c("1st", "2nd", "3rd", "Crew")
)
xtab
# Compare the proportion of survived between groups
pairwise_prop_test(xtab)
# Rowwise proportion tests
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Data: Titanic
xtab < as.table(rbind(
c(180, 145), c(179, 106),
c(510, 196), c(862, 23)
))
dimnames(xtab) < list(
Class = c("1st", "2nd", "3rd", "Crew"),
Gender = c("Male", "Female")
)
xtab
# Compare the proportion of males and females in each category
row_wise_prop_test(xtab)