mmc.mean {HH} | R Documentation |
Constructs a "mmc.multicomp"
object from the sufficient statistics
for a one-way design. The object must be explicitly plotted.
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)
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 . |
multicomp.mmc.mean
returns a "mmc.multicomp" object.
multicomp.mean
returns a "multicomp" object.
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.
Richard M. Heiberger <rmh@temple.edu>
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). "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.
## This example is from Hsu and Peruggia ## This is the S-Plus version ## See ?aov.sufficient for R if.R(r={}, s={ pulmonary <- read.table(hh("datasets/pulmonary.dat"), header=TRUE, row.names=NULL) names(pulmonary)[3] <- "FVC" names(pulmonary)[1] <- "smoker" pulmonary$smoker <- factor(pulmonary$smoker, levels=pulmonary$smoker) row.names(pulmonary) <- pulmonary$smoker pulmonary pulmonary.aov <- aov.sufficient(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. plot.matchMMC(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) })