[R-sig-ME] How do I interpret linear mixed model contrast estimates from multcomp::glht()?

[ONKELINX, Thierry]

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 op 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 op r-project.org] Namens Matthew Van Scoyoc
Verzonden: vrijdag 12 december 2014 18:23
Aan: r-sig-mixed-models op 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/
=====
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