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)))