multcomp (version 0.3-8)

respiratory: Respiratory Health Data Set

Description

Sub-group and whole-group analysis of a respiratory therapy drug; data set taken from Westfall et al. (1999, p. 195). Three-way ANOVA with weighted contrasts.

Usage

data(respiratory)

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. 195)

Examples

data(respiratory)

# compute the contrast matrix in several steps 
# overall active vs. placebo
CA  <- c(0, 13, 0, 11, 0, 13, 0, 17, 0)
CP  <- c(0, 0, 14, 0, 12,  0, 19, 0, 12)
CA  <- CA/sum(CA)
CP  <- CP/sum(CP)
C1  <- CP-CA

# for older subgroup only
CAO <- c( 0, 13,  0,  0,  0, 13,  0,  0,  0 ) 
CPO <- c( 0,  0, 14,  0,  0,  0, 19,  0,  0 ) 
CAO <- CAO/sum(CAO)
CPO <- CPO/sum(CPO)
C2  <- CPO - CAO

# for younger subgroup only 
CAY <- c( 0,  0,  0, 11,  0,  0,  0, 17,  0 ) 
CPY <- c( 0,  0,  0,  0, 12,  0,  0,  0, 12 ) 
CAY <- CAY/sum(CAY)
CPY <- CPY/sum(CPY)
C3  <- CPY - CAY

# subgroup with inital good health
CAG <- c( 0, 13,  0, 11,  0,  0,  0,  0,  0 ) 
CPG <- c( 0,  0, 14,  0, 12,  0,  0,  0,  0 ) 
CAG <- CAG/sum(CAG)
CPG <- CPG/sum(CPG)
C4  <- CPG - CAG

# subgroup with inital poor health
CAP <- c( 0,  0,  0,  0,  0, 13,  0, 17,  0 ) 
CPP <- c( 0,  0,  0,  0,  0,  0, 19,  0, 12 ) 
CAP <- CAP/sum(CAP)
CPP <- CPP/sum(CPP)
C5  <- CPP - CAP

# all 4 subgroup combinations of age and initial health condition 
C6  <- c( 0, -1,  1,  0,  0,  0,  0,  0,  0 )
C7  <- c( 0,  0,  0,  0,  0, -1,  1,  0,  0 )
C8  <- c( 0,  0,  0, -1,  1,  0,  0,  0,  0 )
C9  <- c( 0,  0,  0,  0,  0,  0,  0, -1,  1 )

# contrast matrix, note: first column is zero and corresponds to the
# intercept implicitly given in the formula
C   <- rbind(C1, C2, C3, C4, C5, C6, C7, C8, C9)   

# numerate the contrasts
colnames(C) <- NULL
rownames(C) <- c("Overall", "Older", "Younger", "Good Init", "Poor Init",
                 "Old x Good", "Old x Poor", "Young x Good", "Young x Poor") 

# remove the intercept (not needed, simtest can deal with contrast matrices
# with and without a column of zeros for the intercept
C <- C[,-1]

summary(simtest(Score ~ Treatment:AgeGroup:InitHealth,
                data=respiratory, ttype="logical",
                alternative="greater", cmatrix=C))