Performs a G-test for comparing response probabilities (i.e. when the response variable is a binary variable). The function is in fact a wrapper to the G-test for comparison of proportions on a contingency table. If the p-value of the test is significant, the function performs pairwise comparisons by using G-tests.
G.bintest(formula, data, alpha = 0.05, p.method = "fdr")
a character string giving the name of the global test computed.
a character string giving the name(s) of the data.
a character string describing the alternative hypothesis.
the estimated probabilities.
the value of the difference in probabilities under the null hypothesis, always 0.
test statistics.
test degrees of freedom.
p-value of the global test.
significance level.
method for p-values correction.
data frame of pairwise comparisons result.
a character string giving the name of the test computed for pairwise comparisons.
a formula of the form a ~ b, where a and b give the data values and corresponding groups, respectively. a can be a numeric vector or a factor, with only two possible values (except NA).
an optional data frame containing the variables in the formula formula. By default the variables are taken from environment(formula).
significance level to compute pairwise comparisons.
method for p-values correction. See help of p.adjust.
Maxime HERVE <maxime.herve@univ-rennes1.fr>
If the response is a 0/1 variable, the probability of the '1' group is tested. In any other cases, the response is transformed into a factor and the probability of the second level is tested.
Since a G-test is an approximate test, an exact test is preferable when the number of individuals is small (200 is a reasonable minimum). See fisher.bintest in that case.
chisq.bintest, fisher.bintest
response <- c(rep(0:1,c(40,60)),rep(0:1,c(55,45)),rep(0:1,c(65,35)))
fact <- gl(3,100,labels=LETTERS[1:3])
G.bintest(response~fact)
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