HH: mmc.mean – R documentation

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mmc.mean

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

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

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 (2015). Statistical Analysis and Data Display: An Intermediate Course with Examples in R. Second Edition. Springer-Verlag, New York. https://www.springer.com/us/book/9781493921218

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.

Examples

## 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)
})

HH

Statistical Analysis and Data Display: Heiberger and Holland

v3.1-43

GPL (>= 2)

Authors

Richard M. Heiberger

Initial release

2020-11-27