ContingencyTests {coin} | R Documentation

## Tests of Independence in Two- or Three-Way Contingency Tables

### Description

Testing the independence of two nominal or ordered factors.

### Usage

    ## S3 method for class 'formula'
    chisq_test(formula, data, subset = NULL, weights = NULL, ...)
    ## S3 method for class 'table'
    chisq_test(object, ...)
    ## S3 method for class 'IndependenceProblem'
    chisq_test(object, ...)

    ## S3 method for class 'formula'
    cmh_test(formula, data, subset = NULL, weights = NULL, ...)
    ## S3 method for class 'table'
    cmh_test(object, ...)
    ## S3 method for class 'IndependenceProblem'
    cmh_test(object, ...)

    ## S3 method for class 'formula'
    lbl_test(formula, data, subset = NULL, weights = NULL, ...)
    ## S3 method for class 'table'
    lbl_test(object, ...)
    ## S3 method for class 'IndependenceProblem'
    lbl_test(object, distribution = c("asymptotic", "approximate", "none"), ...)

### Arguments

`formula` | a formula of the form `y ~ x | block` where `y` and `x` are factors and `block` is an optional factor for stratification.
`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`.
`weights` | an optional formula of the form `~ w` defining integer valued case weights for each observation. Defaults to `NULL`, implying equal weight for all observations.
`object` | an object inheriting from classes `"table"` or `"IndependenceProblem"`.
`distribution` | a character, the conditional null distribution of the test statistic can be approximated by its asymptotic distribution (`"asymptotic"`, default) or via Monte Carlo resampling (`"approximate"`). Alternatively, the functions `asymptotic` or `approximate` can be used. Computation of the null distribution can be suppressed by specifying `"none"`.
`...` | further arguments to be passed to `independence_test`.

### Details

`chisq_test`, `cmh_test` and `lbl_test` provide the Pearson chi-squared test, the generalized Cochran-Mantel-Haenszel test and the linear-by-linear association test. A general description of these methods is given by Agresti (2002).

The null hypothesis of independence, or conditional independence given `block`, between `y` and `x` is tested.

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 `independence_test`); this argument can also be used to coerce nominal factors to class `"ordered"`. (`lbl_test` coerces to class `"ordered"` under any circumstances.) 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. For the Pearson chi-squared test, this extension was given by Yates (1948) who also discussed the situation when either the response or the covariate is an ordered factor; see also Cochran (1954) and Armitage (1955) for the particular case when `y` is a binary factor and `x` is ordered. The Mantel-Haenszel statistic was similarly extended by Mantel (1963) and Landis, Heyman and Koch (1978).

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

The exact versions of the Pearson chi-squared test and the generalized Cochran-Mantel-Haenszel test do not necessarily result in the same p-value as Fisher's exact test (Davis, 1986).

### References

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

Armitage, P. (1955). Tests for linear trends in proportions and frequencies. Biometrics 11(3), 375–386.

Cochran, W.G. (1954). Some methods for strengthening the common χ^2 tests. Biometrics 10(4), 417–451.

Davis, L. J. (1986). Exact tests for 2 x 2 contingency tables. The American Statistician 40(2), 139–141.

Landis, J. R., Heyman, E. R. and Koch, G. G. (1978). Average partial association in three-way contingency tables: a review and discussion of alternative tests. International Statistical Review 46(3), 237–254.

Mantel, N. (1963). Chi-square tests with one degree of freedom: extensions of the Mantel-Haenszel procedure. Journal of the American Statistical Association 58(303), 690–700.

Yates, F. (1948). The analysis of contingency tables with groupings based on quantitative characters. Biometrika 35(1/2), 176–181.

### Examples

    ## Example data
    ## Davis (1986, p. 140)
    davis <- matrix(
        c(3,  6,
          2, 19),
        nrow = 2, byrow = TRUE
    )
    davis <- as.table(davis)

    ## Asymptotic Pearson chi-squared test
    chisq_test(davis)

    ## Approximative (Monte Carlo) Pearson chi-squared test
    ct <- chisq_test(davis,
                     distribution = approximate(B = 10000))
    pvalue(ct)          # standard p-value
    midpvalue(ct)       # mid-p-value
    pvalue_interval(ct) # p-value interval

    ## Exact Pearson chi-squared test (Davis, 1986)
    ## Note: disagrees with Fisher's exact test
    ct <- chisq_test(davis,
                     distribution = "exact")
    pvalue(ct)          # standard p-value
    midpvalue(ct)       # mid-p-value
    pvalue_interval(ct) # p-value interval
    fisher.test(davis)

    ## Laryngeal cancer data
    ## Agresti (2002, p. 107, Tab. 3.13)
    cancer <- matrix(
        c(21, 2,
          15, 3),
        nrow = 2, byrow = TRUE,
        dimnames = list(
            "Treatment" = c("Surgery", "Radiation"),
               "Cancer" = c("Controlled", "Not Controlled")
        )
    )
    cancer <- as.table(cancer)

    ## Exact Pearson chi-squared test (Agresti, 2002, p. 108, Tab. 3.14)
    ## Note: agrees with Fishers's exact test
    (ct <- chisq_test(cancer,
                      distribution = "exact"))
    midpvalue(ct)       # mid-p-value
    pvalue_interval(ct) # p-value interval
    fisher.test(cancer)

    ## Homework conditions and teacher's rating
    ## Yates (1948, Tab. 1)
    yates <- matrix(
        c(141, 67, 114, 79, 39,
          131, 66, 143, 72, 35,
           36, 14,  38, 28, 16),
        byrow = TRUE, ncol = 5,
        dimnames = list(
               "Rating" = c("A", "B", "C"),
            "Condition" = c("A", "B", "C", "D", "E")
        )
    )
    yates <- as.table(yates)

    ## Asymptotic Pearson chi-squared test (Yates, 1948, p. 176)
    chisq_test(yates)

    ## Asymptotic Pearson-Yates chi-squared test (Yates, 1948, pp. 180-181)
    ## Note: 'Rating' and 'Condition' as ordinal
    (ct <- chisq_test(yates,
                      alternative = "less",
                      scores = list("Rating" = c(-1, 0, 1),
                                    "Condition" = c(2, 1, 0, -1, -2))))
    statistic(ct)^2 # chi^2 = 2.332

    ## Asymptotic Pearson-Yates chi-squared test (Yates, 1948, p. 181)
    ## Note: 'Rating' as ordinal
    chisq_test(yates,
               scores = list("Rating" = c(-1, 0, 1))) # Q = 3.825

    ## Change in clinical condition and degree of infiltration
    ## Cochran (1954, Tab. 6)
    cochran <- matrix(
        c(11,  7,
          27, 15,
          42, 16,
          53, 13,
          11,  1),
        byrow = TRUE, ncol = 2,
        dimnames = list(
                  "Change" = c("Marked", "Moderate", "Slight",
                               "Stationary", "Worse"),
            "Infiltration" = c("0-7", "8-15")
        )
    )
    cochran <- as.table(cochran)

    ## Asymptotic Pearson chi-squared test (Cochran, 1954, p. 435)
    chisq_test(cochran) # X^2 = 6.88

    ## Asymptotic Cochran-Armitage test (Cochran, 1954, p. 436)
    ## Note: 'Change' as ordinal
    (ct <- chisq_test(cochran,
                      scores = list("Change" = c(3, 2, 1, 0, -1))))
    statistic(ct)^2 # X^2 = 6.66

    ## Change in size of ulcer crater for two treatment groups
    ## Armitage (1955, Tab. 2)
    armitage <- matrix(
        c( 6, 4, 10, 12,
          11, 8,  8,  5),
        byrow = TRUE, ncol = 4,
        dimnames = list(
            "Treatment" = c("A", "B"),
               "Crater" = c("Larger", "< 2/3 healed",
                            "=> 2/3 healed", "Healed")
        )
    )
    armitage <- as.table(armitage)

    ## Approximative (Monte Carlo) Pearson chi-squared test (Armitage, 1955, p. 379)
    chisq_test(armitage,
               distribution = approximate(B = 10000)) # chi^2 = 5.91

    ## Approximative (Monte Carlo) Cochran-Armitage test (Armitage, 1955, p. 379)
    (ct <- chisq_test(armitage,
                      distribution = approximate(B = 10000),
                      scores = list("Crater" = c(-1.5, -0.5, 0.5, 1.5))))
    statistic(ct)^2 # chi_0^2 = 5.26

    ## Relationship between job satisfaction and income stratified by gender
    ## Agresti (2002, p. 288, Tab. 7.8)

    ## Asymptotic generalized Cochran-Mantel-Haenszel test (Agresti, p. 297)
    cmh_test(jobsatisfaction) # CMH = 10.2001

    ## Asymptotic generalized Cochran-Mantel-Haenszel test (Agresti, p. 297)
    ## Note: 'Job.Satisfaction' as ordinal
    cmh_test(jobsatisfaction,
             scores = list("Job.Satisfaction" = c(1, 3, 4, 5))) # L^2 = 9.0342

    ## Asymptotic linear-by-linear association test (Agresti, p. 297)
    ## Note: 'Job.Satisfaction' and 'Income' as ordinal
    (lt <- lbl_test(jobsatisfaction,
                    scores = list("Job.Satisfaction" = c(1, 3, 4, 5),
                                  "Income" = c(3, 10, 20, 35))))
    statistic(lt)^2 # M^2 = 6.1563

[Package coin version 1.1-3 Index]