multicomp.order

From HH v1.5
0th

Percentile

Update a multicomp object by ordering its contrasts.

Update a multicomp object by ordering its contrasts. The default sort.by = "height" matches the order in the MMC plot. An alternate sort.by = "estimate" matches the order of the half-normal plot. Or the argument sort.order can be used to specify any other order.

Keywords
dplot
Usage
multicomp.order(mca, sort.by = "height", sort.order = NULL)multicomp.label.change(x, old="adj", new="new", how.many=2)## S3 method for class 'multicomp':
multicomp.label.change(x, old="adj", new="new", how.many=2)## S3 method for class 'mmc.multicomp':
multicomp.label.change(x, old="adj", new="new", how.many=2)
Arguments
mca
"multicomp" object. This is the result of multicomp in S-Plus or the result from applying as.multicomp to a "hmtest" object in R.
sort.by
Either "height" or "estimate".
sort.order
Vector of indices by which the contrasts are to be sorted. When sort.order in non-NULL, it is used.
x
"multicomp" object.
old
character string to be removed from contrast names.
new
replacement character string to be inserted in contrast names.
how.many
number of times to make the replacement.
Value

• The result is a "multicomp" object containing the same contrasts as the argument. multicomp.order sorts the contrasts (and renames them consistently) according to the specifications. multicomp.label.change changes the contrast names according to the specifications. When sort.by=="height", sort the contrasts by the reverse order of the heights. This provides a "multicomp" object that will be plotted by plot.multicomp in the same order used by plot.mmc.multicomp. If there is not "height" component, the original "multicomp" object is returned.

When sort.by=="estimate", sort the contrasts by the reverse order of the contrast estimates. This provides the same order as the half-normal plot.

When sort.order in non-NULL, sort the contrasts in that order.

Note

S-Plus use the multicomp functions and R uses the multcomp package.

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, R.~M. and Holland, B. (2006, accepted). "Mean--mean multiple comparison displays for families of linear contrasts." Journal of Computational and Graphical Statistics.

MMC, as.hmtest in R, simint.reverse in R, multicomp.reverse in S-Plus

Aliases
• multicomp.order
• multicomp.label.change
• multicomp.label.change.multicomp
• multicomp.label.change.mmc.multicomp
Examples
## continue with the example in simint.mmc in R, or multicomp.mmc in S-Plus
col.names=c("catalyst","concent"))
catalystm$catalyst <- factor(catalystm$catalyst, labels=c("A","B","C","D"))
catalystm1.aov <- aov(concent ~ catalyst, data=catalystm)

if.R(r={
catalystm.mca <-
simint(concent ~ catalyst, data=catalystm, type="Tukey")
catalystm.mca

catalystm.mmc <-
simint.mmc(concent ~ catalyst, data=catalystm, type="Tukey")
## the contrasts have been ordered by height (see ?MMC),
## which in this example corresponds to sort.order=c(1,2,4,3,5,6),
## and reversed, to make the contrast Estimates positive.
as.hmtest(catalystm.mmc$mca) ## now simplify the contrast names by removing the string "catalyst" tmp <- multicomp.label.change(catalystm.mmc$mca, "catalyst", "")
as.hmtest(tmp)

## for consistency with the S-Plus example, we change all factor level
## "A" to "control".
as.hmtest(multicomp.label.change(tmp, "A", "control"))

},s={
## continue with the example in simint.mmc in R, or mmc in S-Plus
catalystm1.aov <- aov(concent ~ catalyst, data=catalystm)
catalystm.mca <-
multicomp(catalystm1.aov, method="Tukey")
catalystm.mca

catalystm.mmc <-
multicomp.mmc(catalystm1.aov, method="Tukey")
## the contrasts have been ordered by height (see ?MMC),
## which in this example corresponds to sort.order=c(1,2,4,3,5,6),
## and reversed, to make the contrast Estimates positive.
catalystm.mmc$mca ## S-Plus multicomp already uses simple names. This function is ## therefore used in more complex two-way ANOVA examples. We illustrate ## here by changing all factor level "A" to "control". multicomp.label.change(catalystm.mmc$mca, "A", "control")

})
Documentation reproduced from package HH, version 1.5, License: GPL version 2 or newer

Community examples

Looks like there are no examples yet.