# as.multicomp

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

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 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, ...)

##### 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
- 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

- df, vcov.
R only. Arguments forwarded through

`glht`

to- method
R only. See

`type`

in- 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 libraries. MMC plots in R are 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.

*Documentation reproduced from package HH, version 3.1-35, License: GPL (>= 2)*