MarginalHomogeneityTests {coin} R Documentation

## Marginal Homogeneity Tests

### Description

Testing the marginal homogeneity of a repeated measurements factor in a complete block design.

### Usage

```## 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, ...)
```

### Arguments

 `formula` a formula of the form `y ~ x | block` where `y` and `x` are factors and `block` is an optional factor (which is generated automatically if 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. Defaults to `NULL`. `object` an object inheriting from classes `"table"` (with identical `dimnames` components) or `"SymmetryProblem"`. `...` further arguments to be passed to `symmetry_test`.

### Details

`mh_test` provides the McNemar test, the Cochran Q test, the Stuart(-Maxwell) test and the Madansky test of interchangeability. A general description of these methods is given by Agresti (2002).

The null hypothesis of marginal homogeneity is tested. The response variable and the measurement conditions are given by `y` and `x`, respectively, and `block` is a factor where each level corresponds to exactly one subject with repeated measurements.

This procedure is known as the McNemar test (McNemar, 1947) when both `y` and `x` are binary factors, as the Cochran Q test (Cochran, 1950) when `y` is a binary factor and `x` is a factor with an arbitrary number of levels, as the Stuart(-Maxwell) test (Stuart, 1955; Maxwell, 1970) when `y` is a factor with an arbitrary number of levels and `x` is a binary factor, and as the Madansky test of interchangeability (Madansky, 1963), which implies marginal homogeneity, when both `y` and `x` are factors with an arbitrary number of levels.

If `y` and/or `x` are ordered factors, the default scores, `1:nlevels(y)` and `1:nlevels(x)` respectively, can be altered using the `scores` argument (see `symmetry_test`); this argument can also be used to coerce nominal factors to class `"ordered"`. If both `y` and `x` are ordered factors, a linear-by-linear association test is computed and the direction of the alternative hypothesis can be specified using the `alternative` argument. This extension was given by Birch (1965) who also discussed the situation when either the response or the measurement condition is an ordered factor; see also White, Landis and Cooper (1982).

The conditional null distribution of the test statistic is used to obtain p-values and an asymptotic approximation of the exact distribution is used by default (`distribution = "asymptotic"`). Alternatively, the distribution can be approximated via Monte Carlo resampling or computed exactly for univariate two-sample problems by setting `distribution` to `"approximate"` or `"exact"` respectively. See `asymptotic`, `approximate` and `exact` for details.

### Value

An object inheriting from class `"IndependenceTest"`.

### Note

This function is currently computationally inefficient for data with a large number of pairs or sets.

### References

Agresti, A. (2002). Categorical Data Analysis, Second Edition. Hoboken, New Jersey: John Wiley & Sons.

Birch, M. W. (1965). The detection of partial association, II: The general case. Journal of the Royal Statistical Society B 27(1), 111–124. doi: 10.1111/j.2517-6161.1965.tb00593.x

Cochran, W. G. (1950). The comparison of percentages in matched samples. Biometrika 37(3/4), 256–266. doi: 10.1093/biomet/37.3-4.256

Madansky, A. (1963). Tests of homogeneity for correlated samples. Journal of the American Statistical Association 58(301), 97–119. doi: 10.1080/01621459.1963.10500835

Maxwell, A. E. (1970). Comparing the classification of subjects by two independent judges. British Journal of Psychiatry 116(535), 651–655. doi: 10.1192/bjp.116.535.651

McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2), 153–157. doi: 10.1007/BF02295996

Stuart, A. (1955). A test for homogeneity of the marginal distributions in a two-way classification. Biometrika 42(3/4), 412–416. doi: 10.1093/biomet/42.3-4.412

White, A. A., Landis, J. R. and Cooper, M. M. (1982). A note on the equivalence of several marginal homogeneity test criteria for categorical data. International Statistical Review 50(1), 27–34. doi: 10.2307/1402457

### Examples

```## Performance of prime minister
## Agresti (2002, p. 409)
performance <- matrix(
c(794, 150,
86, 570),
nrow = 2, byrow = TRUE,
dimnames = list(
"First" = c("Approve", "Disprove"),
"Second" = c("Approve", "Disprove")
)
)
performance <- as.table(performance)
diag(performance) <- 0 # speed-up: only off-diagonal elements contribute

## Asymptotic McNemar Test
mh_test(performance)

## Exact McNemar Test
mh_test(performance, distribution = "exact")

## Effectiveness of different media for the growth of diphtheria
## Cochran (1950, Tab. 2)
cases <- c(4, 2, 3, 1, 59)
n <- sum(cases)
cochran <- data.frame(
diphtheria = factor(
unlist(rep(list(c(1, 1, 1, 1),
c(1, 1, 0, 1),
c(0, 1, 1, 1),
c(0, 1, 0, 1),
c(0, 0, 0, 0)),
cases))
),
media = factor(rep(LETTERS[1:4], n)),
case =  factor(rep(seq_len(n), each = 4))
)

## Asymptotic Cochran Q test (Cochran, 1950, p. 260)
mh_test(diphtheria ~ media | case, data = cochran) # Q = 8.05

## Approximative Cochran Q test
mt <- mh_test(diphtheria ~ media | case, data = cochran,
distribution = approximate(nresample = 10000))
pvalue(mt)             # standard p-value
midpvalue(mt)          # mid-p-value
pvalue_interval(mt)    # p-value interval
size(mt, alpha = 0.05) # test size at alpha = 0.05 using the p-value

## Opinions on Pre- and Extramarital Sex
## Agresti (2002, p. 421)
opinions <- c("Always wrong", "Almost always wrong",
"Wrong only sometimes", "Not wrong at all")
PreExSex <- matrix(
c(144, 33, 84, 126,
2,  4, 14,  29,
0,  2,  6,  25,
0,  0,  1,   5),
nrow = 4,
dimnames = list(
"Premarital Sex" = opinions,
"Extramarital Sex" = opinions
)
)
PreExSex <- as.table(PreExSex)

## Asymptotic Stuart test
mh_test(PreExSex)

## Asymptotic Stuart-Birch test
## Note: response as ordinal
mh_test(PreExSex, scores = list(response = 1:length(opinions)))

## Vote intention
vote <- array(
c(120, 1,  8, 2,   2,  1, 2, 1,  7,
6, 2,  1, 1, 103,  5, 1, 4,  8,
20, 3, 31, 1,   6, 30, 2, 1, 81),
dim = c(3, 3, 3),
dimnames = list(
"July" = c("Republican", "Democratic", "Uncertain"),
"August" = c("Republican", "Democratic", "Uncertain"),
"June" = c("Republican", "Democratic", "Uncertain")
)
)
vote <- as.table(vote)

## Asymptotic Madansky test (Q = 70.77)
mh_test(vote)

## Cross-over study
## http://www.nesug.org/proceedings/nesug00/st/st9005.pdf
dysmenorrhea <- array(
c(6, 2, 1,  3, 1, 0,  1, 2, 1,
4, 3, 0, 13, 3, 0,  8, 1, 1,
5, 2, 2, 10, 1, 0, 14, 2, 0),
dim = c(3, 3, 3),
dimnames =  list(
"Placebo" = c("None", "Moderate", "Complete"),
"Low dose" = c("None", "Moderate", "Complete"),
"High dose" = c("None", "Moderate", "Complete")
)
)
dysmenorrhea <- as.table(dysmenorrhea)

## Asymptotic Madansky-Birch test (Q = 53.76)
## Note: response as ordinal
mh_test(dysmenorrhea, scores = list(response = 1:3))

## Asymptotic Madansky-Birch test (Q = 47.29)
## Note: response and measurement conditions as ordinal
mh_test(dysmenorrhea, scores = list(response = 1:3,
conditions = 1:3))
```

[Package coin version 1.4-1 Index]