mcnemar_test | R Documentation |
Performs McNemar chi-squared test to compare paired proportions.
Wrappers around the R base function mcnemar.test()
, but
provide pairwise comparisons between multiple groups
mcnemar_test(x, y = NULL, correct = TRUE) pairwise_mcnemar_test( data, formula, type = c("mcnemar", "exact"), correct = TRUE, p.adjust.method = "bonferroni" )
x |
either a two-dimensional contingency table in matrix form, or a factor object. |
y |
a factor object; ignored if |
correct |
a logical indicating whether to apply continuity correction when computing the test statistic. |
data |
a data frame containing the variables in the formula. |
formula |
a formula of the form |
type |
type of statistical tests used for pairwise comparisons. Allowed
values are one of |
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". |
return a data frame with the following columns:
n
: the number of participants.
statistic
: the value of McNemar's 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
: the significance level of p-values.
The returned object has an attribute called args, which is a list holding the test arguments.
mcnemar_test()
: performs McNemar's chi-squared test for comparing two
paired proportions
pairwise_mcnemar_test()
: performs pairwise McNemar's chi-squared test between
multiple groups. Could be used for post-hoc tests following a significant Cochran's Q test.
# Comparing two paired proportions #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # Data: frequencies of smokers before and after interventions xtab <- as.table( rbind(c(25, 6), c(21,10)) ) dimnames(xtab) <- list( before = c("non.smoker", "smoker"), after = c("non.smoker", "smoker") ) xtab # Compare the proportion of smokers mcnemar_test(xtab) # Comparing multiple related proportions # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # Generate a demo data mydata <- data.frame( outcome = c(0,1,1,0,0,1,0,1,1,1,1,1,0,0,1,1,0,1,0,1,1,0,0,1,0,1,1,0,0,1), treatment = gl(3,1,30,labels=LETTERS[1:3]), participant = gl(10,3,labels=letters[1:10]) ) mydata$outcome <- factor( mydata$outcome, levels = c(1, 0), labels = c("success", "failure") ) # Cross-tabulation xtabs(~outcome + treatment, mydata) # Compare the proportion of success between treatments cochran_qtest(mydata, outcome ~ treatment|participant) # pairwise comparisons between groups pairwise_mcnemar_test(mydata, outcome ~ treatment|participant)
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