Constructs a "mmc.multicomp" object from the formula and
other arguments. The constructed object must be explicitly plotted
with the mmcplot function.
mmc(model, ...) ## R# S3 method for glht
mmc(model, ...)
# S3 method for default
mmc(model, ## lm object
linfct=NULL,
focus=
if (is.null(linfct))
{
if (length(model$contrasts)==1) names(model$contrasts)
else stop("focus or linfct must be specified.")
}
else
{
if (is.null(names(linfct)))
stop("focus must be specified.")
else names(linfct)
},
focus.lmat,
ylabel=deparse(terms(model)[[2]]),
lmat=if (missing(focus.lmat)) {
t(linfct)
} else {
lmatContrast(t(none.glht$linfct), focus.lmat)
},
lmat.rows=lmatRows(model, focus),
lmat.scale.abs2=TRUE,
estimate.sign=1,
order.contrasts=TRUE,
level=.95,
calpha=NULL,
alternative = c("two.sided", "less", "greater"),
...
)
multicomp.mmc(x, ## S-Plus
focus=dimnames(attr(x$terms,"factors"))[[2]][1],
comparisons="mca",
lmat,
lmat.rows=lmatRows(x, focus),
lmat.scale.abs2=TRUE,
ry,
plot=TRUE,
crit.point,
iso.name=TRUE,
estimate.sign=1,
x.offset=0,
order.contrasts=TRUE,
main,
main2,
focus.lmat,
...)
# S3 method for mmc.multicomp
[(x, ..., drop = TRUE)
"aov" object in "lm" method.
name of the response variable.
contrast matrix as in the S-Plus multicomp.
The convention for lmat in R is to use
the transpose of the linfct component produced by
glht. Required for user-specified contrasts.
rows in lmat for the focus factor.
define the factor to compute contrasts of.
In R this argument often can be used to simplify the call.
The statement mmc(my.aov, focus="factorA") is interpreted as
mmc(my.aov, factorA="Tukey", `interaction_average`=TRUE, `covariate_average`=TRUE)
With TRUE, TRUE, multcomp::glht always gives the same result as the S-Plus
multcomp function. Without the TRUE, TRUE,
multcomp::glht gives a
different answer when there are interactions or covariates in the model.
See
R only. Contrast matrix used in the user-specified
comparisons of the focus factor. This is the matrix the user
constructs.
Row names must include all levels of the factor.
Column names are the names the user assigns to the contrasts.
Each column must sum to zero. See catalystm.lmat in the Examples
section for an example.
The focus.lmat matrix is multiplied by the lmat from the none
component to create the lmat for the user-specified contrasts.
Display the hibrido.lmat and maiz2.lmat in the maiz
example below to see what is happening.
In R, see
other arguments. alternative and
base are frequently used with glht.
argument to multicomp
logical, scale the contrasts in the columns of
lmat to make the sum of the absolute values of each column equal 2.
numeric. If 0, leave contrasts in the
default lexicographic direction. If positive, force all contrasts to positive,
reversing their names if needed (if contrast A-B is negative, reverse it
to B-A). If negative, the force all contrasts to positive.
sort the contrasts in the (mca, none,
lmat) components by height on the MMC plot. This will place the
contrasts in the multicomp plots in the same order as in the MMC plot.
Direction of alternative hypothesis. See
Confidence level. Defaults to 0.95.
critical value for the tests. The value from the
specified multicomp method is used for the user-specified
contrasts when lmat is specified. This argument is called
crit.point with multicomp in S-Plus and calpha
when used with glht and confint in R.
In R, with a large number of levels for the focus factor, calpha
should
be specified. See notes below for discussion of the timing issues
and the examples for an illustration how to use calpha.
logical, display the plot if TRUE.
arguments to
plot.mmc.multicomp.
See "[".
An "mmc.multicomp" object contains either the first two or all
three of the "multicomp" components mca, none,
lmat described here. Each "multicomp" component in
R also contains a "glht" object.
Object containing the pairwise comparisons.
Object comparing each mean to 0.
Object for the contrasts specified in
the lmat argument.
"[.mmc.multicomp" is a subscript method.
By default, if lmat is not specified, we plot the isomeans grid
and the pairwise comparisons for the focus factor. By default,
we plot the specified contrasts if the lmat is specified.
Each contrast is plotted at a height which is the weighted average of
the means being compared. The weights are scaled to the sum of their
absolute values equals 2.
We get the right contrasts automatically if the aov is oneway. If we specify an lmat for oneway it must have a leading row of 0.
For any more complex design, we must study the lmat from the mca
component of the result to see how to construct the lmat (with the
extra rows as needed) and how to specify the lmat.rows
corresponding to the rows for the focus factor.
mmc in R works from either an "glht" object or an
"aov" object. multicomp.mmc in S-Plus works from an
"aov" object.
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.
Hsu, J. and Peruggia, M. (1994). "Graphical representations of Tukey's multiple comparison method." Journal of Computational and Graphical Statistics, 3:143--161.