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.