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 row-wise z-test of two proportions,
the post-hoc tests following a significant chi-square test of homogeneity
for 2xc and rx2 contingency tables.
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, ...)a vector of counts of successes, a one-dimensional table with two entries, or a two-dimensional table (or matrix) with 2 columns, giving the counts of successes and failures, respectively.
a vector of counts of trials; ignored if x is a
matrix or a table.
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.
a character string specifying the alternative
hypothesis, must be one of "two.sided" (default),
"greater" or "less". You can specify just the initial
letter. Only used for testing the null that a single proportion
equals a given value, or that two proportions are equal; ignored
otherwise.
a logical indicating whether Yates' continuity correction should be applied where possible.
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.
logical value. Default is FALSE. If TRUE, a detailed result is shown.
a cross-tabulation (or contingency table) with two columns and multiple rows (rx2 design). The columns give the counts of successes and failures respectively.
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 prop_test().
return a data frame with some the following columns:
n: the number of participants.
group: the categories in the row-wise proportion tests.
statistic: the value of Pearson's chi-squared test statistic.
df: the degrees of freedom of the approximate chi-squared
distribution of the test statistic.
p: p-value.
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: 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.
prop_test(): performs one-sample and two-samples z-test of
proportions. Wrapper around the function prop.test().
pairwise_prop_test(): pairwise comparisons between proportions, a post-hoc
tests following a significant chi-square test of homogeneity for 2xc
design. Wrapper around pairwise.prop.test()
row_wise_prop_test(): performs row-wise z-test of two proportions, a post-hoc tests following a significant chi-square test
of homogeneity for rx2 contingency table. The z-test of two proportions is calculated for each category (row).
# Comparing an observed proportion to an expected proportion
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
prop_test(x = 95, n = 160, p = 0.5, detailed = TRUE)
#> # A tibble: 1 × 11
#> n n1 estimate statistic p df conf.low conf.high method alter…¹
#> * <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <chr> <chr>
#> 1 160 95 0.594 5.26 0.0219 1 0.513 0.670 Prop t… two.si…
#> # … with 1 more variable: p.signif <chr>, and abbreviated variable name
#> # ¹alternative
# 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
#> smoker
#> group yes no
#> grp1 490 10
#> grp2 400 100
# compare the proportion of smokers
prop_test(xtab, detailed = TRUE)
#> # A tibble: 1 × 13
#> n n1 n2 estimate1 estimate2 statistic p df conf.…¹ conf.…²
#> * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1000 500 500 0.98 0.8 80.9 2.36e-19 1 0.141 0.219
#> # … with 3 more variables: method <chr>, alternative <chr>, p.signif <chr>, and
#> # abbreviated variable names ¹conf.low, ²conf.high
# 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
#> Groups
#> Smokers grp1 grp2 grp3 grp4
#> Yes 50 100 139 80
#> No 56 13 17 22
# Compare the proportions of smokers between groups
prop_test(xtab, detailed = TRUE)
#> # A tibble: 1 × 15
#> n n1 n2 n3 n4 estim…¹ estim…² estim…³ estim…⁴ stati…⁵ p
#> * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 477 106 113 156 102 0.472 0.885 0.891 0.784 75.5 2.82e-16
#> # … with 4 more variables: df <dbl>, method <chr>, alternative <chr>,
#> # p.signif <chr>, and abbreviated variable names ¹estimate1, ²estimate2,
#> # ³estimate3, ⁴estimate4, ⁵statistic
# Pairwise comparison between groups
pairwise_prop_test(xtab)
#> # A tibble: 6 × 5
#> group1 group2 p p.adj p.adj.signif
#> * <chr> <chr> <dbl> <dbl> <chr>
#> 1 grp1 grp2 1.25e-10 6.23e-10 ****
#> 2 grp1 grp3 3.09e-13 1.86e-12 ****
#> 3 grp2 grp3 1 e+ 0 1 e+ 0 ns
#> 4 grp1 grp4 6.41e- 6 2.56e- 5 ****
#> 5 grp2 grp4 7.01e- 2 1.4 e- 1 ns
#> 6 grp3 grp4 3.06e- 2 9.19e- 2 ns
# 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
#> Class
#> Survived 1st 2nd 3rd Crew
#> No 122 167 528 673
#> Yes 203 118 178 212
# Compare the proportion of survived between groups
pairwise_prop_test(xtab)
#> # A tibble: 6 × 5
#> group1 group2 p p.adj p.adj.signif
#> * <chr> <chr> <dbl> <dbl> <chr>
#> 1 1st 2nd 3.13e- 7 9.38e- 7 ****
#> 2 1st 3rd 2.55e-30 1.27e-29 ****
#> 3 2nd 3rd 6.9 e- 7 1.38e- 6 ****
#> 4 1st Crew 1.62e-35 9.73e-35 ****
#> 5 2nd Crew 1.94e- 8 7.75e- 8 ****
#> 6 3rd Crew 6.03e- 1 6.03e- 1 ns
# Row-wise 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
#> Gender
#> Class Male Female
#> 1st 180 145
#> 2nd 179 106
#> 3rd 510 196
#> Crew 862 23
# Compare the proportion of males and females in each category
row_wise_prop_test(xtab)
#> # A tibble: 4 × 7
#> group n statistic df p p.adj p.adj.signif
#> * <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 1st 2201 121. 1 3.4 e-28 1.02e-27 ****
#> 2 2nd 2201 47.8 1 4.65e-12 9.3 e-12 ****
#> 3 3rd 2201 24.9 1 6.18e- 7 6.18e- 7 ****
#> 4 Crew 2201 308. 1 5.51e-69 2.2 e-68 ****