plot.multicomp
plot.multicomp.hh
coerce their argument
to an "glht"
object and plots
that with the appropriate plot
method.
In R, plot.multicomp.adjusted
replaces the bounds
calculated by multcomp:::confint.glht
with bounds based on
a common standard error for a set of anova tables that are
partitioned for the simple effects on an analysis conditioned on
the levels of one of the factors.
In S-Plus,
plot.multicomp.hh
augments the standard plot.multicomp
to
give additional user arguments to control the appearance of the plot. plotMatchMMC
uses the plot.multicomp.hh
code.
plotMatchMMC
must immediately follow a plot of an
mmc.multicomp
object and is applied to either the $mca
or $lmat
component of the mmc.multicomp
object.
plotMatchMMC
is used as a tiebreaker plot for the MMC
plot. plotMatchMMC
matches the horizontal scaling of the
MMC
plot and displays the individual contrasts in the same
order as the MMC
plot. See mmc
for examples.
These functions are no longer recommended. Use mmcplot
instead.
"plot"(x, ...) ## R only
"plot"(x, ylabel = x$ylabel, href = 0, uniform = TRUE, plt.in = c(0.2, 0.9, 0.1, 0.9), x.label.adj=1, xrange.include=href, xlim, comparisons.per.page=21, col.signif=1, col.not.signif=1, lty.signif=4, lty.not.signif=4, lwd.signif=1, lwd.not.signif=1, ..., xlabel.print=TRUE, y.axis.side=2, ylabel.inside=FALSE)
plotMatchMMC(x, ..., xlabel.print=FALSE, cex.axis=par()$cex.axis, col.signif='red', main="", ylabel.inside=FALSE, y.axis.side=4, adjusted=FALSE)
"multicomp"
object. plotMatchMMC
will also
accept a mmc.multicomp
object. It will use the lmat
component if there is one, otherwise it will use the mca
component."multicomp"
object. We move them to the
right when matching the x-axis of an MMC plot.plot.multicomp
.FALSE
(the default), the
plotMatchMMC
right-axis labels are in the margin. If
TRUE
, the right-axis labels are in the figure area.
Setting the argument to
TRUE
makes sense when plotting the lmat
component of an
mmc.multicomp
object.xlim
will be extended to include these values. S-Plus only.TRUE
and the plots fill
more than one page, the scale will be uniform across pages.par("plt")
to make better
use of the space on the plotting page.par("adj")
applied
to the x-location of the y.labels on the multicomp
plot.plot.multicomp
hardwires this to 21, which allows
for all pairwise comparisons of 7 levels taken 2 at a time.
The HH plot.multicomp
makes it a variable.
Use it together with plt.in
to make better use of the space
on the plot. S-Plus only.plot.multicomp
. Both R and S-Plus for plotMatchMMC
.TRUE
, the caption under the
plot is printed. When FALSE
, the caption under the plot is not
printed. It is helpful to set this to FALSE
when
the multicomp
plot is used as a tiebreaker plot for the MMC plot. S-Plus only.cex
for axis ticklabels.TRUE
,
HH:::plot.multicomp.adjusted
is used to replace the standard
confidence bounds
calculated by multcomp:::confint.glht
, with bounds
calculated by as.multicomp.glht
with a rescaled critical
value based on rescaling the standard error. This rescaling is
used to construct a common standard error for a set of anova tables that are
partitioned for the simple effects on an analysis conditioned on
the levels of one of the factors. See the
clover.commonstrMS.clov.mmc
example in file hh("scripts/Ch12-tway.r")
.plot.multicomp
plots a "multicomp"
object. In S-Plus, this
masks the standard plot.multicomp
in order to provide additional
arguments for controlling the appearance. It defaults to the standard
appearance. In R, it coerces its argument to a "glht"
object and plots
that with the appropriate plot
method.
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.
mmc
in both languages,## data and ANOVA
data(catalystm)
catalystm1.aov <- aov(concent ~ catalyst, data=catalystm)
summary(catalystm1.aov)
catalystm.mca <-
if.R(r=glht(catalystm1.aov, linfct = mcp(catalyst = "Tukey")),
s=multicomp(catalystm1.aov, plot=FALSE))
if.R(s=plot(catalystm.mca),
r=plot(confint(catalystm.mca, calpha=qtukey(.95, 4, 12)/sqrt(2))))
## calpha is strongly recommended in R with a large number of levels
## See ?MMC for details.
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