[R] Equivalent of intervals() in lmer
Douglas Bates
bates at stat.wisc.edu
Mon Apr 21 13:56:25 CEST 2008
On 4/21/08, Michael Kubovy <kubovy at virginia.edu> wrote:
> 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.
If you want to examine the three means then you should fit the model as
lmer(rcl ~ time - 1 + (1 | subj), fr)
> 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
>
>
> [[alternative HTML version deleted]]
>
>
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