MMC plots: In R, functions used to interface the glht in R to the MMC
functions designed with S-Plus multicomp notation. These are
all internal functions that the user doesn't see.
# S3 method for mmc.multicomp
print(x, ..., width.cutoff=options()$width-5)# S3 method for multicomp
print(x, ...)
## print.multicomp.hh(x, digits = 4, ..., height=T) ## S-Plus only
# S3 method for multicomp.hh
print(x, ...) ## R only
as.multicomp(x, ...)
# S3 method for glht
as.multicomp(x, ## glht object
focus=x$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, ...)
# S3 method for multicomp
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.
other arguments.
name of focus factor.
response variable name on the graph.
means of the response variable on the focus factor.
logical, almost always TRUE. If it is
not TRUE, then the contrasts will not be properly placed
on the MMC plot.
numeric. 1: force all contrasts to be positive by
reversing negative contrasts. $-1$: force all contrasts to be negative by
reversing positive contrasts. Leave contrasts as they are constructed
by glht.
logical. If TRUE, order contrasts by
height (see mmc).
logical. This is an internal detail. The
``contrasts'' for the group means are not real contrasts in the
sense they don't compare anything. mmc.glht sets this
argument to TRUE for the none component.
Confidence level. Defaults to 0.95.
R only. User-specified critical point. See
R only. Arguments forwarded through glht to
R only. See type in
See 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.
The 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 (2015). Statistical Analysis and Data Display: An Intermediate Course with Examples in R. Second Edition. Springer-Verlag, New York. https://link.springer.com/us/book/9781493921218
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.