[R-sig-ME] [R] how specify lme() with multiple within-subject factors?
ONKELINX, Thierry
Thierry.ONKELINX at inbo.be
Mon Jan 5 00:19:16 CET 2009
Dear Ben,
I'm cc'ing R-sig-mixed-models because that's a more appropriate list for
questions on lme().
Lme() is only able to work with nested random effects, not with crossed
random effects. Therefore you would need lmer() from the lme4 package.
But I don't think you need crossed random effects. Random slopes should
do the trick since wtype and present have only two levels. Try something
like lme(.., .., random = ~wtype * present | subj)
HTH,
Thierry
------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
www.inbo.be
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than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
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ensure that a reasonable answer can be extracted from a given body of
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-----Oorspronkelijk bericht-----
Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
Namens Ben Meijering
Verzonden: zaterdag 3 januari 2009 19:59
Aan: r-help at r-project.org
Onderwerp: [R] how specify lme() with multiple within-subject factors?
I have some questions about the use of lme().
Below, I constructed a minimal dataset to explain what difficulties I
experience:
# two participants
subj <- factor(c(1, 1, 1, 1, 2, 2, 2, 2))
# within-subjects factor Word Type
wtype <- factor(c("nw", "w", "nw", "w", "nw", "w", "nw", "w"))
# within-subjects factor Target Present/Absent
present <- factor(c(0, 0, 1, 1, 0, 0, 1, 1))
# dependend variable Accuracy
acc <- c(.74, .81, .84, .88, .75, .95, .88, .94)
# repeated-measures analysis of variance
acc.aov <- aov(acc ~ wtype * present + Error(subj/wtype*present))
summary(acc.aov)
# to use lme
library(nlme)
# mixed-effects model
acc.lme <- lme(acc ~ wtype * present, random = ~ 1 | subj)
anova(acc.lme)
How do I have to specify the model to have 1 degree of freedom for the
denominator or error-term, as in aov()?
I know how to do this for the first factor:
lme(.., .., random = ~1 | subj/wtype),
or
lme(.., .., random = list( ~ 1 | subj, ~1 | wtype))
, but not how to get the same degrees of freedom as in the specified
aov(), i.e., 1 degree of freedom of the denominator for both factors
and the interaction term.
How do I specify such a model?
~ Ben
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