Output glmer
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On 15-03-10 07:01 AM, Davide Bellone wrote:
Good afternoon,
I have a little problem in my glmer output. In my model, before run
the model I used
options(contrasts=c("contr.sum", "contr.poly"))
So after stepwise deletion I arrive at the finel output:
First of all, I would caution against stepwise deletion (see e.g. Frank Harrell's book _Regression Modeling Strategies_, or Google "stepwise regression problems"
Formula: y ~ Model$Manage + Model$age + Model$veg + Model$wood + Model$under + Model$veg * Model$Manage + Model$age * Model$veg + Model$under * Model$wood + Model$veg * Model$under + (1 | Model$Site) + (1 | obs)
Second, I would suggest that you leave the "Model$" out of your
formula, and that you recognize that * incorporates both main effects
and interactions: your model can be written more simply as
y ~ Manage + age + veg + wood + under + veg:Manage + veg:age +
under:wood + under:veg + (1|Site) + (1|obs)
or even
y ~ veg*(Manage+age + under) + under*wood + (1|Site) + (1|obs)
(there is one redundant term here -- the main effect of under is
incorporated in both terms -- but R will take care of dropping it
automatically)
or better, retain all two-way interactions:
y ~ (veg+Manage+age+under+wood)^2 + (1|Site) + (1|obs)
AIC BIC logLik deviance df.resid 568.0 604.1 -272.0 544.0 138 Scaled residuals: Min 1Q Median 3Q Max -0.76077 -0.15913 0.00041 0.26754 0.70326 Random effects: Groups Name Variance Std.Dev. obs (Intercept) 11.954 3.458 Model$Site (Intercept) 7.405 2.721 Number of obs: 150, groups: obs, 150; Model$Site, 10 Fixed effects: Estimate d. Error z value Pr(>|z|) (Intercept) 16.67112 7.73426 2.155 0.0311 * Model$Manage1 -5.72100 2.79907 -2.044 0.0410 * Model$age -2.10245 0.84062 -2.501 0.0124 * Model$veg -0.62276 0.29968 -2.078 0.0377 * Model$wood1 0.36500 0.41976 0.870 0.3846 Model$under1 3.69383 2.73372 1.351 0.1766 Model$Manage1:Model$veg 0.20478 0.09007 2.274 0.0230 * Model$age:Model$veg 0.08319 0.03326 2.502 0.0124 * Model$wood1:Model$under1 1.02751 0.44610 2.303 0.0213 * Model$veg:Model$under1 -0.17567 0.08846 -1.986 0.0470 * --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 Manage, wood and under are categorical with 2 levels each. My question is: how can I find the real value of the estimates in the summary output (since I used the contrast)? Also, how it works with the interactions estimate?
What do you mean by the "real value of the estimates"? I think you might want to take a look at the lsmeans or effects packages, or you could use predict() to compute the expected outcome for some particular combination of factors ... The question about contrasts/interpretation of parameters in linear or generalized linear models is not really specific to mixed models. Maybe take a look at Crawley's book, or Faraway's ...
The books that I am reading don?t help much since they don?t show interactions between variables. Usually, they show only one variable with more levels. I Hope this is the right section to ask this question. Thank you for who can help to understand this (maybe simple) problem. Davide
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