How do I interpret linear mixed model contrast estimates from multcomp::glht()?

wrote:

Dear Matthew,

The part before "== 0" are the rownames of the matrix passed to linfct.
When the rownames are missing, the rownumbers are used.

Best regards,

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

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than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of.
~ Sir Ronald Aylmer Fisher

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ensure that a reasonable answer can be extracted from a given body of data.
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-----Oorspronkelijk bericht-----
Van: R-sig-ecology [mailto:r-sig-ecology-bounces at r-project.org] Namens
Matthew Van Scoyoc
Verzonden: vrijdag 12 december 2014 0:08
Aan: r-sig-ecology at r-project.org
Onderwerp: [R-sig-eco] How do I interpret linear mixed model contrast
estimates from multcomp::glht()?

So, what do the rows correspond to in the summary (e.g. "1 == 0")? I was
thinking the answer was buried *cc*, 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(.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)))

# 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)

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 = ..., 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 at aggiemail.usu.edu

https://sites.google.com/site/scoyoc/
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