lsmip: Least-squares (predicted marginal) means interaction plot

Description

This function creates an interaction plot of the least-squares means based on a fitted model and a simple formula specification.

Usage

# S3 method for default
lsmip(object, formula, type,
    pch = c(1,2,6,7,9,10,15:20), 
    lty = 1, col = NULL, plotit = TRUE, ...)
    
pmmip(...)

Arguments

object

An object of class lsmobj, or a fitted model of a class supported by lsmeans.

formula

Formula of the form trace.factors ~ x.factors | by.factors. The least-squares means are plotted against x.factor for each level of trace.factors. by.factors is optional, but if present, it determines separate panels. Each element of this formula may be a single factor in the model, or a combination of factors using the * operator.

type

As in predict, this determines whether we want to inverse-transform the predictions (type="response") or not (any other choice). The default is "link", unless the "predict.type" option is in force; see lsm.options.

pch

The plotting characters to use for each group (i.e., levels of trace.factors). They are recycled as needed.

lty

The line types to use for each group. Recycled as needed.

col

The colors to use for each group, recycled as needed. If not specified, the default trellis colors are used.

plotit

If TRUE, the plot is displayed. Otherwise, one may use the "lattice" attribute of the returned object and print it, perhaps after additional manipulation.

…

Additional arguments passed to lsmeans or to xyplot.

Value

(Invisibly), a data.frame with the table of least-squares means that were plotted, with an additional "lattice" attribute containing the trellis object for the plot.

Details

If object is a fitted model, lsmeans is called with an appropriate specification to obtain least-squares means for each combination of the factors present in formula (in addition, any arguments in … that match at, trend, cov.reduce, or fac.reduce are passed to lsmeans). Otherwise, if object is an lsmobj object, its first element is used, and it must contain one lsmean value for each combination of the factors present in formula.

The wrapper pmmip is provided for those who prefer the term ‘predicted marginal means’.

Examples

Run this code

# NOT RUN {
require(lsmeans)
require(lattice)

#--- Two-factor example
warp.lm <- lm(breaks ~ wool * tension, data = warpbreaks)

# Following plot is the same as the usual interaction plot of the data
lsmip(warp.lm, wool ~ tension)

#--- Three-factor example
noise.lm = lm(noise ~ size * type * side, data = auto.noise)

# Separate interaction plots of size by type, for each side
lsmip(noise.lm, type ~ size | side)

# One interaction plot, using combinations of size and side as the x factor
lsmip(noise.lm, type ~ side * size)

# One interaction plot using combinations of type and side as the trace factor
# customize the colors, line types, and symbols to suggest these combinations
lsmip(noise.lm, type * side ~ size, lty=1:2, col=1:2, pch=c(1,1,2,2))

# 3-way interaction is significant, but doesn't make a lot of visual difference...
noise.lm2 = update(noise.lm, . ~ . - size:type:side)
lsmip(noise.lm2, type * side ~ size, lty=1:2, col=1:2, pch=c(1,1,2,2))
# }