| MarginalHomogeneityTest {coin} | R Documentation |
Testing marginal homogeneity in a complete block design.
## S3 method for class 'formula':
mh_test(formula, data, subset = NULL, ...)
## S3 method for class 'table':
mh_test(object, ...)
## S3 method for class 'SymmetryProblem':
mh_test(object, distribution = c("asymptotic", "approximate"), ...)
formula |
a formula of the form y ~ x | block where y
is a factor giving the data values and
x a factor with two or more levels giving the corresponding
replications. block is an
optional factor (which is generated automatically when omitted). |
data |
an optional data frame containing the variables in the model formula. |
subset |
an optional vector specifying a subset of observations to be used. |
object |
an object inheriting from class SymmetryProblem or a
table with identical dimnames attributes. |
distribution |
a character, the null distribution of the test statistic
can be approximated by its asymptotic distribution (asymptotic)
or via Monte-Carlo resampling (approximate).
Alternatively, the functions
approximate or asymptotic can be
used to specify how the exact conditional distribution of the test statistic
should be calculated or approximated. |
... |
further arguments to be passed to or from methods. |
The null hypothesis of independence of row and column totals is tested.
The corresponding test for binary factors x and y is known
as McNemar test.
Scores must be a list of length one (row and column scores coincide). When
scores are given or if x is ordered, the corresponding
linear association test is computed (see Agresti, 2002).
An object inheriting from class IndependenceTest with
methods show, pvalue and statistic.
Alan Agresti (2002), Categorical Data Analysis. Hoboken, New Jersey: John Wiley & Sons.
### Opinions on Pre- and Extramarital Sex, Agresti (2002), page 421
opinions <- c("always wrong", "almost always wrong",
"wrong only sometimes", "not wrong at all")
PreExSex <- as.table(matrix(c(144, 33, 84, 126,
2, 4, 14, 29,
0, 2, 6, 25,
0, 0, 1, 5), nrow = 4,
dimnames = list(PremaritalSex = opinions,
ExtramaritalSex = opinions)))
### treating response as nominal
mh_test(PreExSex)
### and as ordinal
mh_test(PreExSex, scores = list(response = 1:length(opinions)))