cholesterol {multcomp}R Documentation

Cholesterol Reduction Data Set

Description

Cholesterol reduction for five treatments; data set taken from Westfall et al. (1999, p. 153). All pairwise comparisons according to Tukey in a balanced one-way layout.

Usage

data(cholesterol)

Format

This data frame contains the following variables

trt
Treatment at 5 levels: A, B, C, D, E
response
Response variable.

Details

See Westfall et al. (1999, p. 153)

Source

P. H. Westfall, R. D. Tobias, D. Rom, R. D. Wolfinger, Y. Hochberg (1999). Multiple Comparisons and Multiple Tests Using the SAS System. Cary, NC: SAS Institute Inc.

Examples

data(cholesterol)

# adjusted p-values for all-pairwise comparisons in a one-way layout 
# tests for restricted combinations
simtest(response ~ trt, data=cholesterol, type="Tukey",
        ttype="logical")

# adjusted p-values all-pairwise comparisons in a one-way layout 
# (tests for free combinations -> p-values will be larger)
simtest(response ~ trt, data=cholesterol, type="Tukey",
        ttype="free")

# enter now the estimates as parameters
# begin with degrees of freedom
nu <- as.integer(45)
# estimates
parm <- c(10.6151, -4.8331, -1.3901, 1.7597, 4.7461, 10.3325) 
# build the covariance matrix
N <- rep(2, 5)
contrast <- contrMat(N, type="Tukey")
covm <- rep(-0.20254649, 36)
covm <- matrix(covm, ncol=6)
covm[1,2:6] <- rep(0.02893521, 5)
covm[2:6,1] <- rep(0.02893521, 5)
covm[1,1] <- 0.14467606
for (i in  2:6) { covm[i,i] <- 0.83912115 } 

# use the work-horse directly (and add zero column for the intercept)

csimint(estpar=parm, df=nu, covm=covm, cmatrix=cbind(0, contrast))    
csimtest(estpar=parm, df=nu, covm=covm, cmatrix=cbind(0, contrast),
         ttype="logical")      


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