MCA multiple comparisons analysis (pairwise)

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Description

MCA multiple comparisons analysis (pairwise). We calculate the contrast matrix for all pairwise comparisons, taking account of covariates and interactions.

Usage

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mcalinfct(model, focus,
          mmm.data=model$model,
          formula.in=terms(model),
          linfct.Means=

          
          multcomp.meanslinfct(model, focus, mmm.data, formula.in,
                         contrasts.arg=model$contrasts),
          type="Tukey"
          )

Arguments

model

aov object

focus

name of one of the factors in the model, as a character object.

mmm.data

data.frame from which the model was estimated. Normally, the default is the correct value.

formula.in

formula of the model which was estimated. Normally, the default is the correct value. The use of the terms function honors the keep.order=TRUE if it was specified.

linfct.Means

Contrast matrix for the adjusted means of each level of the focus factor. Normally, the default is the correct value.

type

Name of the multiple comparison procedure to be used. See contrMat.

Value

Matrix to be used as a value for the linfct argument to glht.

Note

This function provides results similar to the mcp(focusname="Tukey") argument to glht. I think it provides better values for covariate and interaction terms.

Author(s)

Richard M. Heiberger <rmh@temple.edu>

See Also

MMC

Examples

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## See the examples in HH/scripts/MMC.cc176.R

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