PlotLMER in LanguageR package

[mishery]

Has anyone used this?

I can understand about 80% of what is in the LanguageR v0.95
documentation and the examples given there, but I am clearly missing
some critical aspects.

I want to plot the interaction of two continuous corpus frequency
variables, surface and morph, in the following model of lexical
decision response times.

mfreqIA.lmer=lmer(RT ~ PrevRT + PrevLex + PrevErr + PCA1 + PCA2 +
surface*morph + (1|AllSubj) + (1|ItemNum), data=rtdat)

I have tried to adapt the example Harald Baayen gives, but keep
getting the error message "number of colours and number of
conditioning values mismatch"

Any help gratefully received!

[Stefan Th. Gries]

Can you give a replicable example with its code, plz? (For example,
using Harald's lexdec data.)

library(languageR)
m<-lmer(RT ~ Frequency + Length +(1|Subject) + (1|Word), data=lexdec)
summary(m)

Now, with this model, what is the code that you use for plotting etc.
and that creates the error?

STG
--
Stefan Th. Gries
-----------------------------------------------
University of California, Santa Barbara
http://www.linguistics.ucsb.edu/faculty/stgries
-----------------------------------------------

[mishery]

The newer LanguageR package contains a function written by Harald for
plotting mixed models.

Below are bits copied from the documentation. It is is the interaction
bit that I am messing up as I can plot a version of the model without
the interaction.

******************

Usage
plotLMER.fnc(model, xlabel = NA, ylabel = NA, ylimit = NA, fun = NA,
pred = NA, n = 100, intr = NA, mcmcMat = NA, lockYlim = TRUE,
addlines = FALSE, withList = FALSE, cexsize = 0.5, ...)

********************

intr
a list specifying an interaction to be graphed; obligatory arguments
are (1) the
name of the interaction variable, followed by (2) a vector of values
for that
variable, followed by (3) the position for interaction labels
(’"beg"’, ’"mid"’, or
’"end"’, or ’NA’ if no labels are desired), optionally followed by (4)
a list with as
first element a vector of colors and as second element a vector of
line types. The
number of elements in both vectors should match the number of values
specified
under (2) for the interaction predictor.

********************************

This the code given that I am trying to adapt. Here a three-way
interaction LogWordFreq*LogAffixFreq*Sex in an initial model is being
examined by splitting the data by sex and running separate models.

********************************

males.lmer = lmer(LogRT ~ PC1 + PC2 + PrevError + Rank +
ResidSemRating + ResidFamSize + LogWordFreq*LogAffixFreq +
poly(LogCUP, 2, raw=TRUE) + LogUP + LogCUPtoEnd +
plotLMER.fnc 89
(1|Subject) + (1|Word) + (1|Affix), data = males)
females.lmer = lmer(LogRT ~ PC1 + PC2 + PrevError + Rank +
ResidSemRating + ResidFamSize + LogWordFreq*LogAffixFreq +
poly(LogCUP, 2, raw=TRUE) + LogUP + LogCUPtoEnd +
(1|Subject) + (1|Word) + (1|Affix), data = females)
femMCMC = pvals.fnc(females.lmer, nsim=500, withMCMC=TRUE,
addPlot=FALSE)
# for which we then plot the interaction
# of word frequency by affix frequency
par(mfrow=c(2,2),mar=c(5,5,2,2))
plotLMER.fnc(females.lmer, pred = "LogAffixFreq",
intr=list("LogWordFreq", round(quantile(females$LogWordFreq),3),
"beg",
list(c("red", "green", "blue", "yellow", "purple"), rep(1,5))),
ylimit=c(6.5,7.0),
mcmcMat=femMCMC$mcmc, xlabel = "log affix frequency", ylabel = "log RT
auditory lexical decision")
mtext("females", line=0.5, cex=0.8)
plotLMER.fnc(males.lmer, pred = "LogAffixFreq",
intr=list("LogWordFreq", round(quantile(males$LogWordFreq),3), "beg",
list(c("red", "green", "blue", "yellow", "purple"), 1:5)),
ylimit=c(6.5,7.0))
mtext("males", line=0.5, cex=0.8)
plotLMER.fnc(females.lmer, pred = "LogWordFreq",
intr=list("LogAffixFreq", round(quantile(females$LogAffixFreq),3),
"beg"),
ylimit=c(6.5,7.0), cexsize=1.2)
mtext("females", line=0.5, cex=0.8)
plotLMER.fnc(males.lmer, pred = "LogWordFreq",
intr=list("LogAffixFreq", round(quantile(males$LogAffixFreq),3),
"end"),
ylimit=c(6.5,7.0), cexsize=1.0)
mtext("males", line=0.5, cex=0.8)
par(mfrow=c(1,1))

Will post my attempt at code soon

[mishery]

mfreqIA.lmer=lmer(RT ~ PrevRT + PrevLex + PrevErr + PCA1 + PCA2 +
surface*morph + (1|AllSubj) + (1|ItemNum), data=rtdat)

plotLMER.fnc(mfreqIA.lmer, pred = "morph", intr=list("surface",
round(quantile(rtdat$surface),3), "beg",

list(c("red", "green", "blue", "yellow", "purple"), rep(1,5))),

ylimit=c(4.0,8.0), xlabel = "Morph freq", ylabel = "RT")

I don't really understand what the code for the interaction is doing,
what the rep() statement is for. And not sure about how many colours I
need. I will try running Harald's example, it might illuminate things.
Previously I plotted interactions in mixed models by plotting the
effect of a variable adjusted for the median, minimum and maximum of
the other variable, so three lines. I think this is Harald's book. I
assumed the new function does something similar but with quantiles. I
will post again once I have run Harald's example to say whether this
has made things clear.

Thank you

- Mike

[mishery]

Sorted now, it was the colour coding. Although plot doesn't seem to
show an interaction despite the t values

surface -10.22
morph -3.32
surface:morph 3.44

Possible effect of outliers, I guess. The morph variable has a few
outliers, but I though it was still passably close to normal.