mmc

From HH v3.1-37 by Richard Heiberger

MMC (Mean--mean Multiple Comparisons) plots.

Constructs a "mmc.multicomp" object from the formula and other arguments. The constructed object must be explicitly plotted with the mmcplot function.

Usage

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)

Arguments

model: "aov" object in "lm" method.
ylabel: name of the response variable.
lmat: 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.
lmat.rows: rows in lmat for the focus factor.
focus: 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
focus.lmat: 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.
linfct: In R, see
…: other arguments. alternative and base are frequently used with glht.
comparisons: argument to multicomp
lmat.scale.abs2: logical, scale the contrasts in the columns of lmat to make the sum of the absolute values of each column equal 2.
estimate.sign: 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.
order.contrasts: 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.
alternative: Direction of alternative hypothesis. See
level: Confidence level. Defaults to 0.95.
crit.point, calpha: 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.
plot: logical, display the plot if TRUE.
ry, iso.name, x.offset, main, main2: arguments to plot.mmc.multicomp.
x, drop: See "[".

Details

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.

Value

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.

mca

Object containing the pairwise comparisons.

none

Object comparing each mean to 0.

lmat

Object for the contrasts specified in the lmat argument.

"[.mmc.multicomp" is a subscript method.

Note

The multiple comparisons calculations in R and S-Plus use completely different functions. MMC plots in R are constructed by mmc based on

References

Heiberger, Richard M. and Holland, Burt (2004b). Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS. Springer Texts in Statistics. Springer. ISBN 0-387-40270-5.

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.

Documentation reproduced from package HH, version 3.1-37, License: GPL (>= 2)

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