stats (version 3.4.3)

mcnemar.test: McNemar's Chi-squared Test for Count Data

Description

Performs McNemar's chi-squared test for symmetry of rows and columns in a two-dimensional contingency table.

Usage

mcnemar.test(x, y = NULL, correct = TRUE)

Arguments

x

either a two-dimensional contingency table in matrix form, or a factor object.

y

a factor object; ignored if x is a matrix.

correct

a logical indicating whether to apply continuity correction when computing the test statistic.

Value

A list with class "htest" containing the following components:

statistic

the value of McNemar's statistic.

parameter

the degrees of freedom of the approximate chi-squared distribution of the test statistic.

p.value

the p-value of the test.

method

a character string indicating the type of test performed, and whether continuity correction was used.

data.name

a character string giving the name(s) of the data.

Details

The null is that the probabilities of being classified into cells [i,j] and [j,i] are the same.

If x is a matrix, it is taken as a two-dimensional contingency table, and hence its entries should be nonnegative integers. Otherwise, both x and 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 correct is TRUE.

References

Alan Agresti (1990). Categorical data analysis. New York: Wiley. Pages 350--354.

Examples

# NOT RUN {
## 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
# }