Calculate Means for a Term in a Multivariate Linear Model

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

Usage

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

Arguments

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.

Value

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.

Author

Michael Friendly

Examples


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")
#>      Y1    Y2
#> 1 17.00 12.25
#> 2  9.50 10.75
#> 3 13.75  2.00
termMeans(mod, "A:B")
#>       Y1   Y2
#> 1:1 10.0 10.0
#> 2:1  3.0 19.5
#> 3:1 17.5  6.0
#> 1:2 24.0 14.5
#> 2:2 16.0  2.0
#> 3:2 10.0 -2.0
termMeans(mod, "A:B", label.factors=TRUE)
#>         Y1   Y2
#> A1:B1 10.0 10.0
#> A2:B1  3.0 19.5
#> A3:B1 17.5  6.0
#> A1:B2 24.0 14.5
#> A2:B2 16.0  2.0
#> A3:B2 10.0 -2.0
if (FALSE) { # \dontrun{
termMeans(mod, "A:B:C")    # generates an error
} # }

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")