MMC (Mean–mean Multiple Comparisons) plots from the sufficient statistics for a one-way design.

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Description

Constructs a "mmc.multicomp" object from the sufficient statistics for a one-way design. The object must be explicitly plotted. This is the S-Plus version. See ?aovSufficient for R

Usage

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multicomp.mean(group, n, ybar, s, alpha=.05,  ## S-Plus
               ylabel="ylabel", focus.name="focus.factor", plot=FALSE,
               lmat, labels=NULL, ...,
               df=sum(n) - length(n),
               sigmahat=(sum((n-1)*s^2) / df)^.5)

multicomp.mmc.mean(group, n, ybar, s, ylabel, focus.name,  ## S-Plus
                   lmat,
                   ...,
                   comparisons="mca",
                   lmat.rows=seq(length=length(ybar)),
                   ry,
                   plot=TRUE,
                   crit.point,
                   iso.name=TRUE,
                   estimate.sign=1,
                   x.offset=0,
                   order.contrasts=TRUE,
                   method="tukey",
                   df=sum(n)-length(n),
                   sigmahat=(sum((n-1)*s^2)/df)^.5)

Arguments

group

character vector of levels

n

numeric vector of sample sizes

ybar

vector of group means

s

vector of group standard deviations

alpha

Significance levels of test

ylabel

name of response variable

focus.name

name of factor

plot

logical. Should the "mmc.multicomp" object be automatically plotted? ignored in R.

lmat

lmat from multicomp in S-Plus or t(linfct) from glht in R.

labels

labels argument for multicomp in S-Plus. Not used in R.

method

method for critical point calculation. This corresponds to method in S-Plus multicomp and to type in R glht

df

scalar, residual degrees of freedom

sigmahat

sqrt(MSE) from the ANOVA table

...

other arguments

comparisons

argument to S-Plus multicomp only.

estimate.sign, order.contrasts, lmat.rows

See lmat.rows in mmc.

ry

See argument ry.mmc in plot.mmc.multicomp.

crit.point

See argument crit.point in S-Plus multicomp. The equivalent is not in glht.

iso.name, x.offset

See plot.mmc.multicomp.

Value

multicomp.mmc.mean returns a "mmc.multicomp" object.

multicomp.mean returns a "multicomp" object.

Note

The multiple comparisons calculations in R and S-Plus use completely different functions. MMC plots in R are constructed by mmc based on glht. MMC plots in S-Plus are constructed by multicomp.mmc based on the S-Plus multicomp. The MMC plot is the same in both systems. The details of getting the plot differ.

Author(s)

Richard M. Heiberger <rmh@temple.edu>

References

Heiberger, Richard M. and Holland, Burt (2004b). Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS. Springer Texts in Statistics. Springer. ISBN 0-387-40270-5.

Heiberger, Richard M. and Holland, Burt (2006). "Mean–mean multiple comparison displays for families of linear contrasts." Journal of Computational and Graphical Statistics, 15:937–955.

Hsu, J. and Peruggia, M. (1994). "Graphical representations of Tukey's multiple comparison method." Journal of Computational and Graphical Statistics, 3:143–161.

See Also

mmc

Examples

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## This example is from Hsu and Peruggia

## This is the S-Plus version
## See ?aovSufficient for R

if.R(r={},
s={

data(pulmonary)
pulmonary.aov <- aovSufficient(FVC ~ smoker,
                               data=pulmonary)
summary(pulmonary.aov)


## multicomp object
pulmonary.mca <-
multicomp.mean(pulmonary$smoker,
               pulmonary$n,
               pulmonary$FVC,
               pulmonary$s,
               ylabel="pulmonary",
               focus="smoker")


pulmonary.mca
## lexicographic ordering of contrasts, some positive and some negative
plot(pulmonary.mca)



pulm.lmat <- cbind("npnl-mh"=c( 1, 1, 1, 1,-2,-2), ## not.much vs lots
                   "n-pnl"  =c( 3,-1,-1,-1, 0, 0), ## none vs light
                   "p-nl"   =c( 0, 2,-1,-1, 0, 0), ## {} arbitrary 2 df
                   "n-l"    =c( 0, 0, 1,-1, 0, 0), ## {} for 3 types of light
                   "m-h"    =c( 0, 0, 0, 0, 1,-1)) ## moderate vs heavy
dimnames(pulm.lmat)[[1]] <- row.names(pulmonary)
pulm.lmat


## mmc.multicomp object
pulmonary.mmc <-
multicomp.mmc.mean(pulmonary$smoker,
                   pulmonary$n,
                   pulmonary$FVC,
                   pulmonary$s,
                   ylabel="pulmonary",
                   focus="smoker",
                   lmat=pulm.lmat,
                   plot=FALSE)


old.omd <- par(omd=c(0,.95, 0,1))

## pairwise comparisons
plot(pulmonary.mmc, print.mca=TRUE, print.lmat=FALSE)

## tiebreaker plot, with contrasts ordered to match MMC plot,
## with all contrasts forced positive and with names also reversed,
## and with matched x-scale.
plotMatchMMC(pulmonary.mmc$mca)

## orthogonal contrasts
plot(pulmonary.mmc)

## pairwise and orthogonal contrasts on the same plot
plot(pulmonary.mmc, print.mca=TRUE, print.lmat=TRUE)

par(old.omd)
})

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