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

# Calculate the contrasts
cc<-contrast(math.lm, a = list(year = c(.5, 1.5, 2.5), size = 380, female

cc

summary(math.lm)

cc$X

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 at aggiemail.usu.edu

https://sites.google.com/site/scoyoc/
=====
Think SNOW!

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