HH: as.multicomp – R documentation

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as.multicomp

Support functions in R for MMC (mean–mean multiple comparisons) plots.

Description

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.

Usage

## S3 method for class 'mmc.multicomp'
print(x, ..., width.cutoff=options()$width-5)

## S3 method for class 'multicomp'
print(x, ...)

## print.multicomp.hh(x, digits = 4, ..., height=T) ## S-Plus 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, ...)

Arguments

`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.

`focus`	name of focus factor.
`ylabel`	response variable name on the graph.
`means`	means of the response variable on the `focus` factor.
`lmat, lmat.rows`	`mmc`
`lmat.scale.abs2`	logical, almost always `TRUE`. If it is not `TRUE`, then the contrasts will not be properly placed on the MMC plot.
`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 `glht`.
`order.contrasts, height`	logical. If `TRUE`, order contrasts by `height` (see `mmc`).
`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. `mmc.glht` sets this argument to `TRUE` for the `none` component.
`level`	Confidence level. Defaults to 0.95.
`calpha`	R only. User-specified critical point. See `confint.glht`.
`df, vcov.`	R only. Arguments forwarded through `glht` to `modelparm`.
`method`	R only. See `type` in `confint.glht`.
`width.cutoff`	See `deparse`.

Details

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.

Value

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.

Note

The multiple comparisons calculations in R and S-Plus use completely different functions. MMC plots in R are based on glht. MMC plots in S-Plus are based on multicomp. The MMC plot is the same in both systems. The details of gettting the plot differ.

Author(s)

Richard M. Heiberger <rmh@temple.edu>

References

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://www.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.