as.multicomp  R Documentation 
MMC plots: In R, functions used to interface the glht
in R to the MMC
functions designed with SPlus multicomp
notation. These are
all internal functions that the user doesn't see.
## S3 method for class 'mmc.multicomp' print(x, ..., width.cutoff=options()$width5) ## S3 method for class 'multicomp' print(x, ...) ## print.multicomp.hh(x, digits = 4, ..., height=T) ## SPlus only ## S3 method for class 'multicomp.hh' print(x, ...) ## R only as.multicomp(x, ...) ## S3 method for class 'glht' as.multicomp(x, ## glht object focus=x$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 class 'multicomp' as.glht(x, ...)
x 

... 
other arguments. 
focus 
name of focus factor. 
ylabel 
response variable name on the graph. 
means 
means of the response variable on the 
lmat, lmat.rows 

lmat.scale.abs2 
logical, almost always 
estimate.sign 
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 
order.contrasts, height 
logical. If 
contrasts.none 
logical. This is an internal detail. The
“contrasts” for the group means are not real contrasts in the
sense they don't compare anything. 
level 
Confidence level. Defaults to 0.95. 
calpha 
R only. Userspecified critical point.
See

df, vcov. 
R only. Arguments forwarded through 
method 
R only. See 
width.cutoff 
See 
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.
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 multiple comparisons calculations in R and SPlus use
completely different functions.
MMC plots in R are based on
glht
.
MMC plots in SPlus are based on
multicomp
.
The MMC plot is the same in both systems. The details of gettting the
plot differ.
Richard M. Heiberger <rmh@temple.edu>
Heiberger, Richard M. and Holland, Burt (2015). Statistical Analysis and Data Display: An Intermediate Course with Examples in R. Second Edition. SpringerVerlag, New York. https://link.springer.com/book/10.1007/9781493921225
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
,
glht
.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.