HH (version 1.5)

simint.mmc: MMC (mean--mean multiple comparisons) plots.

Description

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

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 "[".

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.

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.

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.

See Also

as.multicomp

Examples

## simint is strictly for 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=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)[[1]] <- dimnames(catalystm.mmc$mca$lmat)[[1]]
zapsmall(catalystm.lmat)
if.R(s=dimnames(catalystm.mca$lmat)[[1]],
     r=c("(Intercept)", dimnames(catalystm.mca$cmatrix)[[2]][-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 <- 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)

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)

Run the code above in your browser using DataCamp Workspace