| simint.mean {HH} | R Documentation |
Constructs a "mmc.multicomp" object from the sufficient statistics
for a one-way design. The object must be explicitly plotted.
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)
group, y |
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(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; "lower" for
intervals with infinite upper bounds; and, "upper" for
intervals with infinite lower bounds. In R, the S-Plus values are
translated to "two.sided", "greater", and
"less". Or the user can enter the values "two.sided",
"greater", and "less". |
df |
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. |
ry |
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. |
simint.mmc.mean and multicomp.mmc.mean return a
"mmc.multicomp" object.
simint.mean returns a "hmtest" 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 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.
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, 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.
## 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)