To help Kedar a bit:
Here is one way:
recall <- c(10, 13, 13, 6, 8, 8, 11, 14, 14, 22, 23, 25, 16, 18, 20,
15, 17, 17, 1, 1, 4, 12, 15, 17, 9, 12, 12, 8, 9, 12)
fr <- data.frame(rcl = recall, time = factor(rep(c(1, 2, 5), 10)),
subj = factor(rep(1:10, each = 3)))
(fr.lmer <- lmer(rcl ~ time + (1 | subj), fr))
require(gmodels)
ci(fr.lmer)
Now I have a problem to which I would very much appreciate having a
solution:
The model fr.lmer gives a SE of 1.8793 for the (Intercept) and 0.3507
for the other levels. The reason is that the first took account of the
variability of the effect of subjects. Or using simulation:
Estimate CI lower CI upper Std. Error p-value
(Intercept) 11.107202 6.458765 15.208065 2.1587362 0.004
time2 2.012064 1.301701 2.795128 0.3743050 0.000
time5 3.206834 2.502870 3.939791 0.3694384 0.000
Now if I need to draw CI bars around the three means, it seems to me
that they should be roughly 11, 13, and 16.2, each \pm 0.75, because
I'm trying to estimate the variability of patterns within subjects,
and am not interested in the subject to subject variation in the mean
for the purposes of prediction.
This what the authors in the paper cited below call on p. 402 a
"narrow [as opposed to a broad] inference space." My question: ***How
do I extract the three narrow CIs from the lmer?***
@ARTICLE{BlouinRiopelle2005,
author = {Blouin, David C. and Riopelle, Arthur J.},
title = {On confidence intervals for within-subjects designs},
journal = {Psychological Methods},
year = {2005},
volume = {10},
pages = {397--412},
number = {4},
month = dec,
abstract = {Confidence intervals (CIs) for means are frequently
advocated as alternatives
to null hypothesis significance testing (NHST), for which a common
theme in the debate is that conclusions from CIs and NHST should
be mutually consistent. The authors examined a class of CIs for which
the conclusions are said to be inconsistent with NHST in within-
subjects
designs and a class for which the conclusions are said to be
consistent.
The difference between them is a difference in models. In particular,
the main issue is that the class for which the conclusions are said
to be consistent derives from fixed-effects models with subjects
fixed, not mixed models with subjects random. Offered is mixed model
methodology that has been popularized in the statistical literature
and statistical software procedures. Generalizations to different
classes of within-subjects designs are explored, and comments on
the future direction of the debate on NHST are offered.},
url = {http://search.epnet.com/login.aspx?direct=true&db=pdh&an=met104397
}
}
_____________________________
Professor Michael Kubovy
University of Virginia
Department of Psychology
USPS: P.O.Box 400400 Charlottesville, VA 22904-4400
Parcels: Room 102 Gilmer Hall
McCormick Road Charlottesville, VA 22903
Office: B011 +1-434-982-4729
Lab: B019 +1-434-982-4751
Fax: +1-434-982-4766
WWW: http://www.people.virginia.edu/~mk9y/
On Apr 21, 2008, at 2:24 AM, Dieter Menne wrote:
> kedar nadkarni <nadkarnikedar <at> gmail.com> writes:
>
>> I have been trying to obtain confidence intervals for the fit
>> after having
>> used lmer by using intervals(), but this does not work. intervals()
>> is
>> associated with lme but not with lmer(). What is the equivalent for
>> intervals() in lmer()?
>
> ci in Gregory Warnes' package gmodels can do this. However, think
> twice if you
> really need lmer. Why not lme? It is well documented and has many
> features that
> are currently not in lmer.
>
> Dieter
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