simint.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.

Usage

simint.mean(y, n, ybar, s, alpha=.05,  ## R
            ylabel="ylabel", focus.name="focus.factor", plot=FALSE,
            lmat, labels=NULL,
            method="Tukey",
            bounds="both",
            df=sum(n) - length(n),
            sigmahat=(sum((n-1)*s^2) / df)^.5,
            contrasts, ..., group=y)
simint.mmc.mean(y, n, ybar, s, alpha=.05,  ## R
                ylabel="ylabel", focus.name="focus.factor", plot=FALSE,
                lmat, labels=NULL,
                method="Tukey",
                bounds="both",
                df=sum(n) - length(n),
                sigmahat=(sum((n-1)*s^2) / df)^.5,
                estimate.sign=1,
                order.contrasts=TRUE, ..., group=y)
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, y

character vector of levels

numeric vector of sample sizes

ybar

vector of group means

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(cmatrix) from simint 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 simint

bounds

type of intervals to compute. This is the "bounds" argument to multicomp and the alternative argument to simint. Values are: the default "both" for two-sided intervals; "

scalar, residual degrees of freedom

sigmahat

sqrt(MSE) from the ANOVA table

contrasts

logical, argument in R to contr.Dunnett when method="Dunnett".

...

other arguments

comparisons

argument to S-Plus multicomp only.

estimate.sign, order.contrasts, lmat.rows

See lmat.rows in multicomp.mmc or simint.mmc.

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

crit.point

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

iso.name, x.offset

See plot.mmc.multicomp.

Value

simint.mmc.mean and multicomp.mmc.mean return a "mmc.multicomp" object.
simint.mean returns a "hmtest" object. multicomp.mean returns a "multicomp" object.

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.

Examples

Run this code

## This example is from Hsu and Peruggia

pulmonary <- read.table(hh("datasets/pulmonary.dat"), header=TRUE,
                        row.names="group")
pulmonary

anova.mean(row.names(pulmonary),
           pulmonary$n,
           pulmonary$ybar,
           pulmonary$s,
           ylabel="pulmonary")


## simint or multicomp object
pulmonary.mca <-
if.R(r=
simint.mean(row.names(pulmonary),
            pulmonary$n,
            pulmonary$ybar,
            pulmonary$s,
            ylabel="pulmonary",
            focus="smoker")
,s=
multicomp.mean(row.names(pulmonary),
               pulmonary$n,
               pulmonary$ybar,
               pulmonary$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 <-
if.R(r=
simint.mmc.mean(row.names(pulmonary),
                pulmonary$n,
                pulmonary$ybar,
                pulmonary$s,
                ylabel="pulmonary",
                focus="smoker",
                lmat=pulm.lmat)
,s=
multicomp.mmc.mean(row.names(pulmonary),
                   pulmonary$n,
                   pulmonary$ybar,
                   pulmonary$s,
                   ylabel="pulmonary",
                   focus="smoker",
                   lmat=pulm.lmat,
                   plot=FALSE)
)


gray <- if.R(r="gray", s=16)
red  <- if.R(r="red",  s=8)
blue <- if.R(r="blue", s=6)

old.par <- if.R(s=par(mar=c(5,4,4,4)+.1),
                r=par(mar=c(15,4,4,4)+.1))

## pairwise comparisons
plot(pulmonary.mmc, print.mca=TRUE, print.lmat=FALSE,
     col.mca.signif=red, col.iso=16)

## tiebreaker plot, with contrasts ordered to match MMC plot,
## with all contrasts forced positive, and with names also reversed.
if.R(r={
pulmonary.xlim <- par()$usr[1:2]
plot(pulmonary.mmc$mca, xlim=pulmonary.xlim, xaxs="i", main="", xlab="")
},s={
plot(pulmonary.mmc$mca, col.signif=red, lty.signif=1, xlabel.print=FALSE,
     xaxs="d",  plt=par()$plt+c(0,0,-.25,.05), xrange.include=c(-1, 1.2))
})

## orthogonal contrasts
plot(pulmonary.mmc, print.lmat=TRUE, col.lmat.signif=blue, col.iso=16)

## pairwise and orthogonal contrasts on the same plot
plot(pulmonary.mmc, print.mca=TRUE, print.lmat=TRUE,
     col.mca.signif=red, col.lmat.signif=blue, col.iso=16,
     lty.lmat.not.signif=2)

par(old.par)