multcomp (version 0.3-5)

cholesterol: 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)

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.

Details

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

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")