[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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