[R-sig-ME] How do I interpret linear mixed model contrast estimates from multcomp::glht()?
Matthew Van Scoyoc
scoyoc at gmail.com
Wed Dec 17 06:24:57 CET 2014
Thanks Ken and Thierry,
The rows in summary.glht() correspond to the row names of the contrast
matrix given in the linfct argument as Thierry stated in my post 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>
(thanks
again Thierry). I'm using the contrast function in the contrast package to
help compute the contrast matrix. The contrast function from the contrast
package produces this...
> # Calculate the contrasts
> cc<-contrast(math.lm, a = list(year = c(.5, 1.5, 2.5), size = 380, female
= levels(egsingle$female)),
+ b = list(year = c(.5, 1.5, 2.5), size = 800, female
= levels(egsingle$female)))
> cc
lm model parameter contrast
Contrast S.E. Lower Upper
t df Pr(>|t|)
0.1671791 0.01739038 0.1330889 0.2012694 9.61 7225 0
0.2148280 0.02235506 0.1710055 0.2586504 9.61 7225 0
0.2624768 0.03167964 0.2003754 0.3245781 8.29 7225 0
0.1671791 0.01739038 0.1330889 0.2012694 9.61 7225 0
0.2148280 0.02235506 0.1710055 0.2586504 9.61 7225 0
0.2624768 0.03167964 0.2003754 0.3245781 8.29 7225 0
So, how do the rows of the contrast object relate to the coefficients of
the mixed or linear model? The summary of *math.lm* produces 5 rows of
coefficients...
> summary(math.lm)
Call:
lm(formula = math ~ year * size + female, data = egsingle)
Residuals:
Min 1Q Median 3Q Max
-3.5631 -0.7775 -0.0523 0.7183 5.0195
Coefficients:
Estimate Std. Error t value
Pr(>|t|)
(Intercept) -5.590e-01 3.687e-02 -15.163 < 2e-16 ***
year 8.521e-01 2.391e-02 35.644 < 2e-16 ***
size -3.413e-04 4.251e-05 -8.030 1.13e-15 ***
femaleFemale -2.232e-02 2.576e-02 -0.867 0.38624
year:size -1.135e-04 2.948e-05 -3.849 0.00012 ***
I keep looking at cc$X (what I'm passing to glht() as the contrast matrix
in the linfct argument), but my brain starts to melt at this point...
> cc$X
(Intercept) year size femaleFemale year:size
1 0 0 -420 0 -210
2 0 0 -420 0 -630
3 0 0 -420 0 -1050
4 0 0 -420 0 -210
5 0 0 -420 0 -630
6 0 0 -420 0 -1050
attr(,"assign")
[1] 0 1 2 3 4
attr(,"contrasts")
attr(,"contrasts")$female
[1] "contr.SAS"
This is my first try at mixed models, and it's been several years since my
linear regression course that was taught in SAS, so any help is appreciated.
Cheers,
MVS
=====
Matthew Van Scoyoc
<mvanscoyoc at aggiemail.usu.edu>https://sites.google.com/site/scoyoc/
=====
Think SNOW!
On Tue, Dec 16, 2014 at 2:24 PM, Ken Beath <ken.beath at mq.edu.au> wrote:
>
> 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*
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> Statistics Department
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