mercuryfish {coin}R Documentation

Chromosomal effects of mercury contaminated fish consumption

Description

The mercury level in the blood, the proportion of cells with abnormalities and the proportion of cells with chromosome aberrations for a group of consuments of mercury contaminated fish and a control group.

Usage

data(mercuryfish)

Format

A data frame with 39 observations on the following 4 variables.

group
a factor with levels control and exposed.
mercury
the level of mercury in the blood.
abnormal
the proportion of cells with structural abnormalities.
ccells
the proportion of cells with asymmetrical or incomplete-symmetrical chomosome aberrations called C_u cells.

Details

Subjects who ate contaminated fish for more than three years in the exposed group and subjects of a control group are to be compared. Instead of a multivariate comparison, Rosenbaum (1994) applied a coherence criterion. The observations are partially ordered: an observation is than another when all three variables (mercury, abnormal and ccells) are smaller and a score reflecting the `ranking' is attached to each observation. The distribution of the scores in both groups is to be compared and the corresponding test is called `POSET-test' (partially ordered sets).

Source

S. Skerfving, K. Hansson, C. Mangs, J. Lindsten, N. Ryman (1974), Methylmercury-induced chromosome damage in men. Environmental Research, 7, 83–98.

References

P. R. Rosenbaum (1994), Coherence in Observational Studies. Biometrics, 50, 368–374.

Examples

data(mercuryfish)

coherence <- function(data) {
    x <- as.matrix(data)
    matrix(apply(x, 1, function(y)
        sum(colSums(t(x) < y) == ncol(x)) - 
        sum(colSums(t(x) > y) == ncol(x))), ncol = 1)
}

### POSET-test
poset <- independence_test(mercury + abnormal + ccells ~ group, data =
                           mercuryfish, ytrafo = coherence)

### linear statistic (T in Rosenbaum's, 1994, notation)
statistic(poset, "linear")

### expectation
expectation(poset)

### variance (Rosenbaum, 1994, uses the unconditional approach)
covariance(poset)

### the standardized statistic
statistic(poset)

### and asymptotic p-value
pvalue(poset)

### exact p-value
independence_test(mercury + abnormal + ccells ~ group, data =
                  mercuryfish, ytrafo = coherence, distribution = "exact")

### multivariate analysis
mvtest <- independence_test(mercury + abnormal + ccells ~ group, 
                            data = mercuryfish)

### global p-value
pvalue(mvtest)

### adjusted univariate p-value
pvalue(mvtest, adjusted = TRUE)


[Package coin version 0.2-12 Index]