From: Michael Kubovy <kubovy_at_virginia.edu>

Date: Mon, 21 Apr 2008 09:30:55 -0400

Professor Michael Kubovy

University of Virginia

Department of Psychology

WWW: http://www.people.virginia.edu/~mk9y/

R-help_at_r-project.org mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Mon 21 Apr 2008 - 13:34:05 GMT

Date: Mon, 21 Apr 2008 09:30:55 -0400

Sorry, I meant to say: "For the moment I wonder if the solution is not to use CIs based on the two low SEs produced by the ~ time model, and to treat them as least-significant difference intervals."

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 9:23 AM, Michael Kubovy wrote:

> Thanks Doug,

*>
**> You write: "If you want to examine the three means then you should fit
**> the model as lmer(rcl ~ time - 1 + (1 | subj), fr)"
**>
**> I do just that (which is what Dieter just sent). But the CIs are much
**> too big compared to the CIs for differences between means (which
**> should be bigger than the CIs on the means themselves). If you write
**> the model as ~ 1 - time, then the CIs are roughly of the same (large)
**> size. But I'm really interested in the CIs on the means that capture
**> the variability *within* subjects. I believe that this is what
**> experimentalists in psychology need (and have been debating for a long
**> time what the correct analysis is that produces these error bars). The
**> theory is not about generalizing to people, but generalizing to
**> responses to different situations within people. The article by
**> Brillouin and Riopelle (2005) is the only one that tries to do this
**> within the framework of LMEMs that I know of, and it's couched in
**> terms of SAS.
**>
**> For the moment I wonder if the solution is not to use CIs based on the
**> two low SEs produced by the ~ 1 - time model, and to treat them as
**> least-significant difference intervals.
**>
**> On Apr 21, 2008, at 7:56 AM, Douglas Bates wrote:
**>
**>> 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
**>>> }
**>>> }
**>>>
**>>> 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]]

R-help_at_r-project.org mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Mon 21 Apr 2008 - 13:34:05 GMT

Archive maintained by Robert King, hosted by
the discipline of
statistics at the
University of Newcastle,
Australia.

Archive generated by hypermail 2.2.0, at Mon 21 Apr 2008 - 14:30:29 GMT.

*
Mailing list information is available at https://stat.ethz.ch/mailman/listinfo/r-help.
Please read the posting
guide before posting to the list.
*