[R-sig-ME] How do I interpret linear mixed model contrast estimates from multcomp::glht()?
Ken Beath
ken.beath at mq.edu.au
Tue Dec 16 22:24:03 CET 2014
You first need to work out what contrasts you want to calculate (and
possibly find out what a contrast is). Then find out what the appropriate
contrast matrix is, or the routines that will give you one a predefine one.
Then if you use glht it will probably be fairly easy to interpret what
results it is producing.
On 17 December 2014 at 03:28, Matthew Van Scoyoc <scoyoc at gmail.com> wrote:
>
> Sorry, I meant no disrespect. I just thought that by posting the question
> on a more appropriate list, someone who has come across the same problem
> would answer. The problem is actually with contrast::contrast(). The
> function does not produce row names that correspond to the fixed effects or
> the interactions in the model. So how do I figure out which row corresponds
> to what, and how do I pull that out of the contrast() object *cc?*
>
> MVS
> =====
> Matthew Van Scoyoc
>
> <mvanscoyoc at aggiemail.usu.edu>https://sites.google.com/site/scoyoc/
> =====
> Think SNOW!
>
> On Tue, Dec 16, 2014 at 1:28 AM, ONKELINX, Thierry <
> Thierry.ONKELINX at inbo.be
> > wrote:
> >
> > Matthew,
> >
> > Just reposting exactly the same question on a other list is not very
> > polite. I answered it on r-sig-ecology
> >
> http://r-sig-ecology.471788.n2.nabble.com/How-do-I-interpret-linear-mixed-model-contrast-estimates-from-multcomp-glht-td7579236.html#a7579237
> > If the answer is not clear enough, then let us know what you don't
> > understand about it. Have you look at the examples in ?glht
> >
> > ir. Thierry Onkelinx
> > Instituut voor natuur- en bosonderzoek / Research Institute for Nature
> and
> > Forest
> > team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> > Kliniekstraat 25
> > 1070 Anderlecht
> > Belgium
> > + 32 2 525 02 51
> > + 32 54 43 61 85
> > Thierry.Onkelinx at inbo.be
> > www.inbo.be
> >
> > To call in the statistician after the experiment is done may be no more
> > than asking him to perform a post-mortem examination: he may be able to
> say
> > what the experiment died of.
> > ~ Sir Ronald Aylmer Fisher
> >
> > The plural of anecdote is not data.
> > ~ Roger Brinner
> >
> > The combination of some data and an aching desire for an answer does not
> > ensure that a reasonable answer can be extracted from a given body of
> data.
> > ~ John Tukey
> >
> > -----Oorspronkelijk bericht-----
> > Van: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-project.org
> ]
> > Namens Matthew Van Scoyoc
> > Verzonden: vrijdag 12 december 2014 18:23
> > Aan: r-sig-mixed-models at r-project.org
> > Onderwerp: [R-sig-ME] How do I interpret linear mixed model contrast
> > estimates from multcomp::glht()?
> >
> > How do the rows in the summary (e.g. "1 == 0") correspond to the model?
> > The answer is buried *contrast::contrast()*, but I can't figure it out.
> > Consider this modified example I stole from here <
> > https://stat.ethz.ch/pipermail/r-sig-mixed-models/2009q4/003061.html>...
> >
> > > options(contrasts = c(factor = "contr.SAS", ordered = "contr.poly"))
> > > library("mlmRev")
> > > library("lme4")
> > > library("lmerTest")
> > > library("contrast")
> > > library("multcomp")
> > >
> > > data("egsingle")
> > > # Linear mixed model
> > > math.lmm <- lmer(math ~ year * size + female + (1|childid) +
> > (1|schoolid), egsingle)
> > > # Linear model
> > > math.lm <- lm(math ~ year * size + female, data = egsingle) #
> > > Calculate contrast matrix cc<-contrast(math.lm, a = list(year = c(0.5,
> > > 1.5, 2.5), size = 380,
> > female = levels(egsingle$female)), +
> > b = list(year = c(0.5,
> > 1.5, 2.5), size = 800, female = levels(egsingle$female)))
> > > # Calculate estimates
> > > summary(glht(math.lmm, linfct = cc$X))
> >
> > Simultaneous Tests for General Linear Hypotheses
> >
> > Fit: lme4::lmer(formula = math ~ year * size + female + (1 | childid) +
> > (1 | schoolid), data = egsingle)
> >
> > Linear Hypotheses:
> > Estimate Std. Error z value Pr(>|z|)
> > 1 == 0 0.12774 0.08020 1.593 0.1272
> > 2 == 0 0.15322 0.08066 1.900 0.0669 .
> > 3 == 0 0.17870 0.08178 2.185 0.0341 *
> > 4 == 0 0.12774 0.08020 1.593 0.1273
> > 5 == 0 0.15322 0.08066 1.900 0.0669 .
> > 6 == 0 0.17870 0.08178 2.185 0.0342 *
> > ---
> > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted
> p
> > values reported -- single-step method)
> >
> > The row names correspond to the levels of *year* and *female,* and are
> > probably Female:0.5, Female:1.5, Female:2.5, and so on. But how do I pull
> > that out of the contrast() object *cc?* It might be simple with 3 main
> > effects, but my current project has 5 main effects, four 2-way
> > interactions, and one 3-way interaction, and the summary table has 24
> rows.
> > Ultimately I would like to create a dataframe so I can plot the
> contrasts,
> > something like this...
> >
> > > x = summary(glht(math.lmm, linfct = cc$X)) # Contrast data frame
> > > math.contr = data.frame(Effect.Interaction =
> > > reference.something.in.cc,
> > Estimate =
> > x[["test"]]$coefficients, Std.Error = x[["test"]]$sigma)
> >
> > Thanks for the help!
> > Cheers,
> > MVS
> > =====
> > Matthew Van Scoyoc
> >
> > <
> >
> https://mail.google.com/mail/?view=cm&fs=1&tf=1&to=mvanscoyoc@aggiemail.usu.edu
> > >
> > https://sites.google.com/site/scoyoc/
> > =====
> > Think SNOW!
> >
> > [[alternative HTML version deleted]]
> >
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--
*Ken Beath*
Lecturer
Statistics Department
MACQUARIE UNIVERSITY NSW 2109, Australia
Phone: +61 (0)2 9850 8516
Building E4A, room 526
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