glht in R to the MMC
functions designed with S-Plus multicomp notation. These are
all internal functions that the user doesn't see.
"print"(x, ..., width.cutoff=options()$width-5)
"print"(x, ...)
## print.multicomp.hh(x, digits = 4, ..., height=T) ## S-Plus only
"print"(x, ...) ## R only
as.multicomp(x, ...)
"as.multicomp"(x, ## glht object focus=x$focus, ylabel=deparse(terms(x$model)[[2]]), means=model.tables(x$model, type="means", cterm=focus)$tables[[focus]], height=rev(1:nrow(x$linfct)), lmat=t(x$linfct), lmat.rows=lmatRows(x, focus), lmat.scale.abs2=TRUE, estimate.sign=1, order.contrasts=TRUE, contrasts.none=FALSE, level=0.95, calpha=NULL, method=x$type, df, vcov., ... )
as.glht(x, ...)
"as.glht"(x, ...)"glht" object for as.multicomp.
A "mmc.multicomp" object for print.mmc.multicomp.
A "multicomp" object for as.glht and print.multicomp.focus factor.TRUE. If it is
not TRUE, then the contrasts will not be properly placed
on the MMC plot.glht.TRUE, order contrasts by
height (see mmc).mmc.glht sets this
argument to TRUE for the none component.
S-Plus
confint.glht.
S-Plus
confint.glht.
glht to S-Plus
modelparm.
S-Plus
modelparm.
type in S-Plus
confint.glht.
S-Plus
confint.glht.
deparse.as.multicomp is a generic function to change its argument to a
"multicomp" object.as.multicomp.glht changes an "glht" object to a
"multicomp" object. If the model component of the argument "x"
is an "aov" object then the standard error is taken from the
anova(x$model) table, otherwise from the summary(x).
With a large number of levels for the focus factor, the
summary(x)
function is exceedingly slow (80 minutes for 30 levels on 1.5GHz Windows
XP).
For the same example, the anova(x$model) takes a fraction of
a second.mmc.multicomp print
method displays the confidence intervals and heights on the
MMC plot for each component of the mmc.multicomp object.print.multicomp displays the confidence intervals and heights for
a single component.
Heiberger, Richard M. and Holland, Burt (2006). "Mean--mean multiple comparison displays for families of linear contrasts." Journal of Computational and Graphical Statistics, 15:937--955.
mmc,