McNemar's Chi-squared Test for Count Data
Performs McNemar's chi-squared test for symmetry of rows and columns in a two-dimensional contingency table.
mcnemar.test(x, y = NULL, correct = TRUE)
- either a two-dimensional contingency table in matrix form, or a factor object.
- a factor object; ignored if
xis a matrix.
- a logical indicating whether to apply continuity correction when computing the test statistic.
The null is that the probabilities of being classified into cells
[j,i] are the same.
x is a matrix, it is taken as a two-dimensional contingency
table, and hence its entries should be nonnegative integers.
y must be vectors or factors of the
same length. Incomplete cases are removed, vectors are coerced into
factors, and the contingency table is computed from these.
Continuity correction is only used in the 2-by-2 case if
A list with class
- the value of McNemar's statistic.
- the degrees of freedom of the approximate chi-squared distribution of the test statistic.
- the p-value of the test.
- a character string indicating the type of test performed, and whether continuity correction was used.
- a character string giving the name(s) of the data.
"htest"containing the following components:
Alan Agresti (1990). Categorical data analysis. New York: Wiley. Pages 350--354.
## Agresti (1990), p. 350. ## Presidential Approval Ratings. ## Approval of the President's performance in office in two surveys, ## one month apart, for a random sample of 1600 voting-age Americans. Performance <- matrix(c(794, 86, 150, 570), nrow = 2, dimnames = list("1st Survey" = c("Approve", "Disapprove"), "2nd Survey" = c("Approve", "Disapprove"))) Performance mcnemar.test(Performance) ## => significant change (in fact, drop) in approval ratings