hasseMod function

Hasse Diagram from Linear Model

This function extracts terms from a linear model object and creates a Hasse diagram of the terms. The function is useful for visualizing the structure of a linear model. If the model contains random effects, these are placed in parentheses in the diagram. Manually placed terms are supported to some extent, see example for usage.

hasseMod(object, manualTerms=NULL, manualParents=NULL, 
         meanName="M", errorName="(E)")

Arguments

• object: A linear model object, e.g., lm.
• manualTerms: A vector of terms that should be placed manually in the diagram.
• manualParents: A list of vectors with the parents of the terms in manualTerms.
• meanName: The name of the mean term (default = "M").
• errorName: The name of the error term (default = "(E)").

Returns

A list with the levels of the diagram and the adjacency matrix.

Author(s)

Kristian Hovde Liland

See Also

lm

Examples

# Random data
dat <- data.frame(A = factor(rep(c(1,2),32)), 
                  B = factor(rep(c(1,1,2,2),16)), 
                  C = factor(rep(c(1,1,1,1,2,2,2,2),8)), 
                  Ind = factor(rep(c(1,1,1,1,2,2,2,2),8)),
                  D = factor(rep(c(rep(1,8),rep(2,8)),4)), 
                  E = factor(rep(c(rep(1,16),rep(2,16)),2)))
dat$y = rnorm(64)

# Linear model with interactions and nested factors
mod <- lm(y~A*B*C + D + E%in%D, data=dat)
(an <- Anova(mod, type="II"))
H <- hasseMod(mod)
## Not run:
 # Requires installation of Rgraphviz
library(Rgraphviz)
hasse(H$hasse, parameters=list(cluster = FALSE, arrows = "none", edgeColor = "darkred"))
## End(Not run)

# Linear model with repeated measures where Ind is nested in A
modv <- lm(y~A*r(B) + r(Ind), data=dat)
(anv <- Anova(mod, type="II"))
Hv <- hasseMod(modv, manualTerms=c("Ind"), manualParents=list(c("A")))
## Not run:
 # Requires installation og Rgraphviz
hasse(Hv$hasse, parameters=list(cluster = FALSE, arrows = "none", edgeColor = "darkred"))
## End(Not run)