mmc

0th

Percentile

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
glht.mmc(model, ...)  ## R

## S3 method for class 'glht':
glht.mmc(model, ...)


## S3 method for class 'lm':
glht.mmc(model,       ## lm object
           linfct=NULL,
           focus=
           if (is.null(linfct))
           {
             if (length(model$contrasts)==1) names(model$contrasts)
             else stop("focus or linfct must be specified.")
           }
           else
           {
             if (is.null(names(linfct)))
               stop("focus must be specified.")
             else names(linfct)
           },
           ylabel=as.character(terms(model)[[2]]),
           lmat=t(linfct),
           lmat.rows=-1,
           lmat.scale.abs2=TRUE,
           estimate.sign=1,
           order.contrasts=TRUE,
           level=.95,
           calpha=NULL,
           alternative = c("two.sided", "less", "greater"),
           ...
           )
	   
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
model
"aov" object in "lm" method.
ylabel
name of the response variable.
lmat
contrast matrix as in the S-Plus multicomp. The convention for lmat in R is to use the transpose of the linfct component produced by glht. Required for user-specified contrasts.
lmat.rows
rows in lmat for the focus factor.
focus
define the factor to compute contrasts of. See mcp in R.
linfct
In R, see glht.
...
other arguments. alternative and base are frequently used with glht.
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 al
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.
alternative
Direction of alternative hypothesis. See confint in R. S-Plus multicomp uses the argument bounds for this concept.
level
Confidence level. Defaults to 0.95.
crit.point, calpha
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 called crit.point with multicomp
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, if lmat is not specified, we plot the isomeans grid and the pairwise comparisons for the focus factor. By default, we plot the specified contrasts if the lmat is specified. 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. glht.mmc in R works from either an "glht" object or an "aov" object. multicomp.mmc in S-Plus works from an "aov" object.

Value

  • An "mmc.multicomp" object contains either the first two or all three of the "multicomp" components
  • mca
  • ,
  • none
  • ,
  • lmat
  • described here. Each "multicomp" component in R also contains a "glht" object.
  • mcaObject containing the pairwise comparisons.
  • noneObject comparing each mean to 0.
  • lmatObject for the contrasts specified in the lmat argument.
  • "[.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 glht.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. plot.mmc.multicomp chooses sensible defaults for its many arguments. They will often need manual adjustment. The examples show several types of adjustments. We have changed the centering and scaling to avoid overprinting of label information. By default the significant contrasts are shown in a more intense color than the nonsignificant contrasts. We have an option to reduce the color intensity of the isomeans grid.

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, December). "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.

See Also

as.multicomp, plot.mmc.multicomp

Aliases
  • mmc
  • MMC
  • multicomp
  • multicomp.mmc
  • glht.mmc
  • glht.mmc.glht
  • glht.mmc.lm
  • [.mmc.multicomp
Examples
## Use glht.mmc with R.
## Use multicomp.mmc with S-Plus.

## data and ANOVA
catalystm <- read.table(hh("datasets/catalystm.dat"), header=FALSE,
                       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=glht(catalystm1.aov, linfct = mcp(catalyst = "Tukey")),
     s=multicomp(catalystm1.aov, plot=FALSE))
plot(catalystm.mca)
catalystm.mca


## pairwise comparisons
catalystm.mmc <-
if.R(r=glht.mmc(catalystm1.aov, linfct = mcp(catalyst = "Tukey")),
     s=multicomp.mmc(catalystm1.aov, plot=FALSE))
catalystm.mmc
plot(catalystm.mmc)
plot(catalystm.mmc$mca)
plot(catalystm.mmc$none)



## 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))
if.R(r=catalystm.lmat <- catalystm.lmat[-2,],
     s={})
dimnames(catalystm.lmat)[[1]] <- dimnames(catalystm.mmc$mca$lmat)[[1]]
zapsmall(catalystm.lmat)
if.R(s=dimnames(catalystm.mca$lmat)[[1]],
     r=dimnames(catalystm.mca$linfct)[[2]])

catalystm.mmc <-
if.R(r=glht.mmc(catalystm1.aov, linfct = mcp(catalyst = "Tukey"),
                lmat=catalystm.lmat)
    ,s=multicomp.mmc(catalystm1.aov, lmat=catalystm.lmat,
                     plot=FALSE)
)

catalystm.mmc
plot(catalystm.mmc)

plot(catalystm.mmc$mca)
plot(catalystm.mmc$none)
plot(catalystm.mmc$lmat)




## Dunnett's test
weightloss <- read.table(hh("datasets/weightloss.dat"), header=TRUE)
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)

if.R(r={
group.count <- table(weightloss$group)
},s={})

tmp.dunnett <- 
if.R(r=
glht(weightloss.aov,
     linfct=mcp(group=contrMat(group.count, base=4)),
     alternative="greater")
,s=
multicomp(weightloss.aov,
          method="dunnett", comparisons="mcc",
          bounds="lower", control=4,
          valid.check=FALSE)
)
plot(tmp.dunnett)

tmp.dunnett.mmc <-
if.R(r=
   glht.mmc(weightloss.aov,
            linfct=mcp(group=contrMat(group.count, base=4)),
            alternative="greater")
,s=
   multicomp.mmc(weightloss.aov,
                 method="dunnett", comparisons="mcc",
                 bounds="lower", control=4,
                 valid.check=FALSE, plot=FALSE)
)

tmp.dunnett.mmc
plot(tmp.dunnett.mmc)



## two-way ANOVA
display <- read.table(hh("datasets/display.dat"), header=TRUE)
display$panel <- factor(display$panel)  ## display$panel <- positioned(display$panel, value=(1:3)+.5)
display$emergenc <- factor(display$emergenc)

displayf.aov <- aov(time ~ emergenc * panel, data=display)
anova(displayf.aov)

## multiple comparisons
tmp <- if.R(
   r=glht(displayf.aov, linfct=mcp(panel="Tukey")),
   s=multicomp(displayf.aov, "panel", plot=FALSE))
zapsmall(
  if.R(r=t(tmp$linfct),
       s=tmp$lmat)
)

## MMC plot
displayf.mmc <-
if.R(r=glht.mmc(displayf.aov, linfct=mcp(panel="Tukey"), focus="panel", lmat.rows=5:6),
     s=multicomp.mmc(displayf.aov, "panel", lmat.rows=6:8, plot=FALSE))
plot(displayf.mmc)

## orthogonal contrasts
zapsmall(mca.lmat <- displayf.mmc$mca$lmat)
panel.lmat <- cbind("3-12"=mca.lmat[,1] + mca.lmat[,2],
                     "1-2"=mca.lmat[,3])
displayf.mmc <-
if.R(r=glht.mmc(displayf.aov, linfct=mcp(panel="Tukey"), focus="panel",
                lmat.rows=5:6, lmat=panel.lmat),
     s=multicomp.mmc(displayf.aov, "panel", lmat.rows=6:8,
                     lmat=panel.lmat, plot=FALSE))
plot(displayf.mmc)



## split plot design with tiebreaker plot
##
## This example is based on the query by Tomas Goicoa to R-news
## http://article.gmane.org/gmane.comp.lang.r.general/76275/match=goicoa
## It is a split plot similar to the one in HH Section 14.2 based on
## Yates 1937 example.  I am using the Goicoa example here because its
## MMC plot requires a tiebreaker plot.



maiz <- read.table(hh("datasets/maiz.dat"), header=TRUE)
maiz$hibrido <- factor(maiz$hibrido,
                       levels=c("P3747","P3732","Mol17","A632","LH74"))
maiz$nitrogeno <- factor(maiz$nitrogeno)
position(maiz$nitrogeno) <- c(1, 2.5, 4, 5.5) ## forces class="ordered"

interaction2wt(yield ~ hibrido+nitrogeno+bloque, data=maiz)
interaction2wt(yield ~ hibrido+nitrogeno, data=maiz)

maiz.aov <- aov(yield ~ nitrogeno*hibrido + Error(bloque/nitrogeno), data=maiz) 

summary(maiz.aov)
summary(maiz.aov,
        split=list(hibrido=list(P3732=1, Mol17=2, A632=3, LH74=4)))

## multicomp(maiz.aov, focus="hibrido")         ## can't use 'aovlist' objects
## glht(maiz.aov, linfct=mcp(hibrido="Tukey"))  ## can't use 'aovlist' objects

sapply(maiz[-1], contrasts)
if.R(r={
  ## R glht.mmc requires treatment contrasts
  contrasts(maiz$nitrogeno) <- "contr.treatment"
  sapply(maiz[-1], contrasts)
},
     s={})

## Both R and S-Plus require aov, not aovlist
maiz2.aov <- aov(terms(yield ~ bloque*nitrogeno + hibrido/nitrogeno,
                       keep.order=TRUE), data=maiz)
summary(maiz2.aov)

if.R(s={
  maiz2.mca <- multicomp(maiz2.aov, focus="hibrido")
  ## plot(maiz2.mca)
  dimnames(maiz2.mca$lmat)[[1]]
  maiz2.mmc <- multicomp.mmc(maiz2.aov, focus="hibrido",
                             lmat.rows=16:20, plot=FALSE)
  old.mar <- par(mar=c(15,4,4,7)+.1)
  plot(maiz2.mmc)
  par(mar=c(2,4,28,7)+.1, new=TRUE)
  old.cex <- par(cex=.8)
  plot(maiz2.mmc$mca, col.signif=8, lty.signif=1, xlabel.print=FALSE,
       xaxs="d", plt=par()$plt+c(0,0,-.25,.05), xrange.include=c(-30,40))
  par(old.cex)
  par(old.mar)
},r={
  maiz2.mca <- glht(maiz2.aov, linfct=mcp(hibrido="Tukey"))
  dimnames(maiz2.mca$linfct)[[2]]
  maiz2.mmc <- glht.mmc(maiz2.aov, linfct=mcp(hibrido="Tukey"), lmat.rows=9:12)
  old.oma <- par(oma=c(12,3,0,4))
  plot(maiz2.mmc)
  par(oma=c(0,3,22,4), new=TRUE)
  plot(maiz2.mmc$mca,
       xlim=par()$usr[1:2], xaxs="i",
       main="", xlab="", cex.axis=.7)
  par(old.oma)
})
Documentation reproduced from package HH, version 1.18-1, License: GPL version 2 or newer

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