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) +
# 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 at aggiemail.usu.edu