# simint.mmc

From HH v1.5
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##### MMC (mean--mean multiple comparisons) plots.

Constructs a "mmc.multicomp" object from the formula and other arguments. The object must be explicitly plotted.

Keywords
hplot, htest, design
##### Usage
simint.mmc(y,  ## R
data,
type = "Tukey",
lmat=NULL,
lmat.rows=2:nrow(mca.lmat),
lmat.scale.abs2=TRUE,
estimate.sign=1,
order.contrasts=TRUE,
whichf,
cmatrix=t(lmat),
covariates,
...)

multicomp.mmc(..., comparisons="mca",  ##S-Plus
lmat, lmat.rows=-1,
lmat.scale.abs2=TRUE,
ry,
plot=TRUE,
crit.point,
iso.name=TRUE,
estimate.sign=1,
x.offset=0,
order.contrasts=TRUE,
main,
main2)

"[.mmc.multicomp"(x, ..., drop = TRUE)
##### Arguments
y
Analysis of variance formula.
data
data.frame
type
type of contrasts. See simint for the list.
lmat
contrast matrix as in the S-Plus multicomp. The convention for lmat in R is to use the transpose of the cmatrix component produced by simint. Required for user-specified contrasts.
lmat.rows
rows in lmat for the whichf factor.
whichf
define the factor to compute contrasts of. See simint. This argument is called focus in multicomp.
cmatrix
transpose of the "lmat" argument.
covariates
The current version of multcomp (0.4-8 in R-2.3.1) doesn't handle covariates correctly.
...
other arguments. alternative and base are frequently used with simint.
comparisons
argument to multicomp
lmat.scale.abs2
logical, scale the contrasts in the columns of lmat to make the sum of the absolute values of each column equal 2.
estimate.sign
numeric. If 0, leave contrasts in the default lexicographic direction. If positive, force all contrasts to positive, reversing their names if needed (if contrast A-B is negative, reverse it to B-A). If negative, the force all contrasts
order.contrasts
sort the contrasts in the (mca, none, lmat) components by height on the MMC plot. This will place the contrasts in the multicomp plots in the same order as in the MMC plot.
crit.point
critical value for the tests. The value from the specified multicomp method is used for the user-specified contrasts when lmat is specified. This argument is not available with simint in R.
plot
logical, display the plot if TRUE.
ry, iso.name, x.offset, main, main2
arguments to plot.mmc.multicomp.
x, drop
See "[".
##### Details

By default we plot the isomeans grid and the pairwise comparisons. We get the right contrasts automatically if the aov is oneway. If we specify an lmat for oneway it must have a leading row of 0. For any more complex design, we must study the lmat from the mca component of the result to see how to construct the lmat (with the extra rows as needed) and how to specify the lmat.rows corresponding to the rows for the focus factor. simint in R multcomp version 0.4-8 doesn't work correctly with covariates. simint.mmc works from a formula, not from an "aov" object as multicomp.mmc in S-Plus does.

##### Value

• In R, an "mmc.multicomp" object containing either the first three, or all five, of the following components:
• mca"multicomp" object containing the pairwise comparisons.
• none"multicomp" object comparing each mean to 0.
• hmtest"hmtest" object from simint for the pairwise comparisons.
• lmat"multicomp" object for the contrasts specified in the lmat argument.
• lmat.hmtest"hmtest" object from simint for the contrasts specified in the lmat argument.
• In S-Plus, an "mmc.multicomp" object containing either the first two or all three of the components (
• mca
• ,
• none
• ,
• lmat
• ) as described above. "[.mmc.multicomp" is a subscript method.

##### Note

The multiple comparisons calculations in R and S-Plus use completely different functions. MMC plots in R are constructed by simint.mmc based on simint. 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.

##### 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, R.~M. and Holland, B. (2006, accepted). "Mean--mean multiple comparison displays for families of linear contrasts." Journal of Computational and Graphical Statistics. Hsu, J. and Peruggia, M. (1994). "Graphical representations of {Tukey's} multiple comparison method." Journal of Computational and Graphical Statistics, 3:143--161.

as.multicomp

##### Aliases
• simint.mmc
• mmc
• MMC
• multicomp
• multicomp.mmc
• [.mmc.multicomp
##### Examples
## simint is strictly for R.  Use multicomp.mmc with S-Plus.

## data and ANOVA
col.names=c("catalyst","concent"))
catalystm$catalyst <- factor(catalystm$catalyst, labels=c("A","B","C","D"))

if.R(r=
bwplot(concent ~ catalyst, data=catalystm,
scales=list(cex=1.5),
ylab=list("concentration", cex=1.5),
xlab=list("catalyst",cex=1.5))
,s=
t(bwplot(catalyst ~ concent, data=catalystm,
scales=list(cex=1.5),
xlab=list("concentration", cex=1.5),
ylab=list("catalyst",cex=1.5)))
)

catalystm1.aov <- aov(concent ~ catalyst, data=catalystm)

catalystm.mca <-
if.R(r=simint(concent ~ catalyst, data=catalystm, type="Tukey"),
s=multicomp(catalystm1.aov, plot=F))
plot(catalystm.mca)
catalystm.mca

## pairwise comparisons
catalystm.mmc <-
if.R(r=simint.mmc(concent ~ catalyst, data=catalystm),
s=multicomp.mmc(catalystm1.aov, plot=F))
if.R(r=catalystm.mmc <-
multicomp.label.change(catalystm.mmc, "catalyst", ""),
s={})
catalystm.mmc
plot(catalystm.mmc)
if.R(r=plot(catalystm.mmc, col.mca.signif="red"),
s={})
plot(catalystm.mmc$mca) plot(catalystm.mmc$none)

### $none works for oneway ANOVA, not sure yet beyond that ## user-specified contrasts catalystm.lmat <- cbind("AB-D" =c(0, 1, 1, 0,-2), "A-B" =c(0, 1,-1, 0, 0), "ABD-C"=c(0, 1, 1,-3, 1)) dimnames(catalystm.lmat)[] <- dimnames(catalystm.mmc$mca$lmat)[] zapsmall(catalystm.lmat) if.R(s=dimnames(catalystm.mca$lmat)[],
r=c("(Intercept)", dimnames(catalystm.mca$cmatrix)[][-1])) if.R(r={ catalystm.mmc <- simint.mmc(concent ~ catalyst, data=catalystm, lmat=catalystm.lmat, lmat.rows=2:5, type="Tukey", whichf="catalyst") catalystm.mmc <- multicomp.label.change(catalystm.mmc, "catalyst", "") }, s={ catalystm.mmc <- multicomp.mmc(catalystm1.aov, lmat=catalystm.lmat, plot=FALSE) }) catalystm.mmc plot(catalystm.mmc) if.R(r=plot(catalystm.mmc, col.lmat.signif="red"), s={}) plot(catalystm.mmc$mca)
plot(catalystm.mmc$none) plot(catalystm.mmc$lmat)

## Dunnett's test
weightloss <- data.frame(loss=unlist(weightloss),
group=rep(names(weightloss), rep(10,5)))
if.R(r=
bwplot(loss ~ group, data=weightloss,
scales=list(cex=1.5),
ylab=list("Weight Loss", cex=1.5),
xlab=list("group",cex=1.5))
,s=
t(bwplot(group ~ loss, data=weightloss,
scales=list(cex=1.5),
xlab=list("Weight Loss", cex=1.5),
ylab=list("group",cex=1.5)))
)

weightloss.aov <- aov(loss ~ group, data=weightloss)
summary(weightloss.aov)

tmp.dunnett <-
if.R(r=
simint(loss ~ group, data=weightloss,
type="Dunnett", alternative="greater", base=4)
,s=
multicomp(weightloss.aov,
method="dunnett", comparisons="mcc",
bounds="lower", control=4,
valid.check=FALSE)
)
plot(tmp.dunnett)

if.R(r={
tmp.dunnett.mmc <-
simint.mmc(loss ~ group, data=weightloss,
type="Dunnett", alternative="greater", base=4)
tmp.dunnett.mmc <- multicomp.label.change(tmp.dunnett.mmc, "group", "")
},s=
tmp.dunnett.mmc <-
multicomp.mmc(weightloss.aov,
method="dunnett", comparisons="mcc",
bounds="lower", control=4,
valid.check=FALSE, plot=FALSE)
)

tmp.dunnett.mmc
plot(tmp.dunnett.mmc)
Documentation reproduced from package HH, version 1.5, License: GPL version 2 or newer

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