chisq_test | R Documentation |
Performs chi-squared tests, including goodness-of-fit, homogeneity and independence tests.
chisq_test( x, y = NULL, correct = TRUE, p = rep(1/length(x), length(x)), rescale.p = FALSE, simulate.p.value = FALSE, B = 2000 ) pairwise_chisq_gof_test(x, p.adjust.method = "holm", ...) pairwise_chisq_test_against_p( x, p = rep(1/length(x), length(x)), p.adjust.method = "holm", ... ) chisq_descriptives(res.chisq) expected_freq(res.chisq) observed_freq(res.chisq) pearson_residuals(res.chisq) std_residuals(res.chisq)
x |
a numeric vector or matrix. |
y |
a numeric vector; ignored if |
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 |
p |
a vector of probabilities of the same length of |
rescale.p |
a logical scalar; if TRUE then |
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. |
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 |
res.chisq |
an object of class |
return a data frame with some the following columns:
n
: the number of participants.
group, group1, group2
:
the categories or groups being compared.
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.
NA if the p-value is computed by Monte Carlo simulation.
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.
observed
: observed counts.
expected
: the expected counts under the null hypothesis.
The returned object has an attribute called args, which is a list holding the test arguments.
chisq_test()
: performs chi-square tests including goodness-of-fit,
homogeneity and independence tests.
pairwise_chisq_gof_test()
: perform pairwise comparisons between groups following a global
chi-square goodness of fit test.
pairwise_chisq_test_against_p()
: perform pairwise comparisons after a global
chi-squared test for given probabilities. For each group, the observed and
the expected proportions are shown. Each group is compared to the sum of
all others.
chisq_descriptives()
: returns the descriptive statistics of the chi-square
test. These include, observed and expected frequencies, proportions,
residuals and standardized residuals.
expected_freq()
: returns the expected counts from the chi-square test result.
observed_freq()
: returns the observed counts from the chi-square test result.
pearson_residuals()
: returns the Pearson residuals, (observed - expected) / sqrt(expected)
.
std_residuals()
: returns the standardized residuals
# Chi-square goodness of fit test #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% tulip <- c(red = 81, yellow = 50, white = 27) # Q1: Are the colors equally common? chisq_test(tulip) pairwise_chisq_gof_test(tulip) # Q2: comparing observed to expected proportions chisq_test(tulip, p = c(1/2, 1/3, 1/6)) pairwise_chisq_test_against_p(tulip, p = c(0.5, 0.33, 0.17)) # Homogeneity of proportions between groups #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # Data: Titanic xtab <- as.table(rbind( c(203, 118, 178, 212), c(122, 167, 528, 673) )) dimnames(xtab) <- list( Survived = c("Yes", "No"), Class = c("1st", "2nd", "3rd", "Crew") ) xtab # Chi-square test chisq_test(xtab) # Compare the proportion of survived between groups pairwise_prop_test(xtab)
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