termMeans: Calculate Means for a Term in a Multivariate Linear Model
In heplots: Visualizing Hypothesis Tests in Multivariate Linear Models

Description Usage Arguments Value Author(s) See Also Examples

termMeans is a utility function designed to calculate means for the levels of factor(s) for any term in a multivariate linear model.

1	termMeans(mod, term, label.factors=FALSE, abbrev.levels=FALSE)

`mod`	An mlm model object
`term`	A character string indicating a given term in the model. All factors in the term must be included in the model, even if they are in the model data frame.
`label.factors`	If true, the rownames for each row in the result include the name(s) of the factor(s) involved, followed by the level values. Otherwise, the rownames include only the levels of the factor(s), with multiple factors separated by ':'
`abbrev.levels`	Either a logical or an integer, specifying whether the levels values of the factors in the `term` are to be abbreviated in constructing the rownames. An integer specifies the minimum length of the abbreviation for each factor in the term.

Returns a matrix whose columns correspond to the response variables in the model and whose rows correspond to the levels of the factor(s) in the term.

Michael Friendly

aggregate, colMeans

statList, colMeansList

factors <- expand.grid(A=factor(1:3),B=factor(1:2),C=factor(1:2))
n <- nrow(factors)
responses <-data.frame(Y1=10+round(10*rnorm(n)),Y2=10+round(10*rnorm(n)))
test <- data.frame(factors, responses)
mod <- lm(cbind(Y1,Y2) ~ A*B, data=test)

termMeans(mod, "A")
termMeans(mod, "A:B")
termMeans(mod, "A:B", label.factors=TRUE)
## Not run: 
termMeans(mod, "A:B:C")    # generates an error

## End(Not run)

plastic.mod <- lm(cbind(tear, gloss, opacity) ~ rate*additive, data=Plastic)
colors = c("red", "darkblue", "darkgreen", "brown")
heplot(plastic.mod, col=colors, cex=1.25)
# add means for interaction term
intMeans <- termMeans(plastic.mod, 'rate:additive', abbrev=2)
points(intMeans[,1], intMeans[,2], pch=18, cex=1.2, col="brown")
text(intMeans[,1], intMeans[,2], rownames(intMeans), adj=c(0.5,1), col="brown")