[R-sig-ME] Plotting means and error bars for fixed effects in a ME model

[Dan Mirman]

I think both the effects and lsmeans packages can do this for you:

library(effects)
as.data.frame(effect("Condition", m.1))

library(lsmeans)
lsmeans(m.1, "Condition")

On Sun, Feb 7, 2016 at 4:51 PM, Ché Lucero <chelucero at uchicago.edu> wrote:

> Hi PJ.
>
> An example:
>
> library(lme4)
> dat <- expand.grid(Condition = c('A', 'B'), Subject = 1:10,
>                    Item = c('popcorn', 'butter', 'rosemary', 'salt',
> 'yeast'))
> dat$Subject <- factor(dat$Subject)
> size <- nrow(dat)
> dat$DV <- numeric(size)
> dat[dat$Condition == 'A', 'DV'] <- rnorm(size/2, 8, 1)
> dat[dat$Condition == 'B', 'DV'] <- rnorm(size/2, 9, 1)
>
> barplot(tapply(dat$DV, dat$Condition, FUN=mean))
>
> m.1 <- lmer(DV ~ Condition + (1|Subject) + (1|Item), data=dat)
> m.2 <- update(m.1, . ~ . - Condition)
> anova(m.1, m.2)
>
> I don't want a standard error on anything from the model per se. I want to
> build a standard error of the mean for the two Condition means for
> charting. My example lmer model takes two random effects into account:
> Subject and Item. I want to make a plot in which the standard error of the
> mean takes into account both the Subject and Item variance (like my model).
> I am only aware of how construct the standard error of the mean taking one
> of those into account at a time. Hope I was clearer this time!
>
> Thanks,
>
> -Ché
>
> On Sun, Feb 7, 2016 at 3:21 PM Paul Johnson <pauljohn32 at gmail.com> wrote:
>
> > On Sun, Feb 7, 2016 at 2:56 PM, Ché Lucero <chelucero at uchicago.edu>
> wrote:
> > > Hello, list.
> > >
> > > I have a question about error bars on barplotted means of data that has
> > > been analyzed with mixed effects models.
> > >
> > > Mixed effects models allow us to simultaneously model variance from
> > > multiple random-effects sources (such as Subjects and Items), a much
> > needed
> > > solution to a problem pointed out (most potently) in my field by Herb
> > Clark
> > > in 1973.
> > >
> > Hi.
> >
> > You will get better help if you rephrase your question with a fitted
> > lme4 object and let us see what you are trying to do.  As it is, your
> > question asks us to wade throught a lot of ANOVA jargon. I'd rather
> > see your model, data, and the number for which you want the CI.
> >
> > It appears that even for glm without random effects, there is no
> > settled upon idea for CI on predicted values. You can fall down a
> > rabbit hole reading math articles about that. I don't think the
> > presence of random effects makes that tougher, its just another
> > element to add in the error bands.  The model you specify when you fit
> > dictates what's correlated.
> >
> > I had to improvise some CIs last semester. In the package "arm"
> > (support for the Gelman & Hill textbook), there's a function called
> > se.coef and they do the usual plus or minus 2 of those to make error
> > bars that look like CIs for predicted values. That works because if
> > you calculate some linear sum, you can use the variances of individual
> > elements to get a Wald type CI.
> >
> > I am pretty sure the best answer is simulation with lme4 objects,
> > however. But don't have code to share to you.
> >
> > Good luck, think about showing everybody code and such that they can
> focus
> > on.
> >
> > pj
> >
> > > However, to my knowledge, calculating confidence interval/SEM for means
> > has
> > > not advanced to take this modeling approach into account. A series of
> > > papers - Loftus and Masson 94 (Using confidence intervals in
> > within-subject
> > > designs)  Cousineau 05  (Confidence intervals in within-subject
> designs:
> > a
> > > simpler solution) and Morey 05 (Confidence Intervals from Normalized
> > Data:
> > > A correction to Cousineau)   has specified ways to calculate error bars
> > in
> > > which the within-Subject error has been accounted for.
> > >
> > > That approach to calculating error bars for plotting seems to be well
> > > matched for when the statistical procedure is a simple repeated
> measures
> > > (for Subjects OR Items) ANOVA. But what about the case where I have
> both
> > > within-Subjects and within-Items (and within-XYZ random factors too)?
> Is
> > > there a way to construct standard errors of the mean for levels of a
> > fixed
> > > factor (e.g. Condition) taking more than one random-effect into account
> > as
> > > mixed effects models do? It would be really great to have the charts
> > > visually suggest the same inferences that the ME statistics do, but I
> > can't
> > > find any literature about it.
> > >
> > > Thanks for your time.
> > >
> > > -Ché
> > >
> > >         [[alternative HTML version deleted]]
> > >
> > > _______________________________________________
> > > R-sig-mixed-models at r-project.org mailing list
> > > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
> >
> >
> > --
> > Paul E. Johnson
> > Professor, Political Science        Director
> > 1541 Lilac Lane, Room 504      Center for Research Methods
> > University of Kansas                 University of Kansas
> > http://pj.freefaculty.org              http://crmda.ku.edu
> >
>
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>
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>



-- 
-----------------------------------------------------
Dan Mirman
Assistant Professor
Department of Psychology
Drexel University
http://www.danmirman.org
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