Displaying 5 results from an estimated 5 matches for "glmm1".
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2008 Nov 19
1
F-Tests in generalized linear mixed models (GLMM)
...;- 5 + 1.1*x1 + 0.16 * x2
> mu <- exp(true.lp)
> sigma <- mu * 1
> a <- mu^2/sigma^2
> s <- sigma^2/mu
> y <- rgamma(n, shape=a, scale=s)
>
> library(mgcv)
>
> # a mixed model without Gamma-distribution and without log-link works
as follows:
> glmm1 <- gamm(y ~ x1 + x2, random=list(random1 = ~1))
> glmm2 <- gamm(y ~ 1, random=list(random1 = ~1))
>
> anova(glmm1$lme)
numDF denDF F-value p-value
X 3 295 103.4730 <.0001
> anova(glmm2$lme, glmm1$lme)
Model df AIC BIC logLik Test L.Ratio p-value
glmm2$lme 1 3 4340.060 4351....
2009 Jan 23
1
predict function problem for glmmPQL
Hi all,
I am using cross-validation to validate a generalized linear mixed effects model fitted using glmmPQL. i found that the predict function has a problem and i wonder if anyone has encountered the same problem?
glmm1 = glmmPQL(y~aX+b,random=~1|sample,data=traindata)
predict(glmm1,newdata=testdata,level=1,type="response")
gives me all "NA"s. it works for level=0 (the fixed effects), but not for level=1. When i use newdata=traindata, predict function works perfectly.
i wonder if this is a p...
2004 Nov 09
1
Some questions to GLMM
...plants with
approx 25 parts each.
Preference of the insects for a certain characteristic is usually unimodal.
As far as I understood, I have to use a model with random intercepts and
slopes, because the observations within each plant are not independent.
So far so good
========(lme4)=========
glmm1<-GLMM(count~thick+I(thick^2),random=~thick+I(thick^2)
|plantid,poisson,data=Dataset,control=list(PQLmaxIt=10000))
> summary(glmm1)
Generalized Linear Mixed Model
Family: poisson family with log link
Fixed: lixt ~ thick + I(thick^2)
Data: Dataset
AIC BIC logLik
-125.2406 -83....
2012 Dec 06
2
lme4 glmer general help wanted - code included
...tion: Is there is difference in abundance between sitetypes (blue or yellow)?
#If my 'initial remarks' statement is correct (please tell me if not), then I think a generalized linear mixed model is appropriate and would be something along these lines:
# Fitting the model:
require(lme4) glmm1=glmer(abundance~time+sitetype+(1|site/replicate),family="poisson",data=data) #I chose to use poisson as abundance is count data... not sure if that's a good reason... summary(glmm1) #Output:
################################################################Generalized linear mixed mo...
2007 Aug 07
0
help on glmmML
...ior.mode as an estimate for the random effects.
These can be very different from the estimates obtained using SAS , NLMIXED
in the random with out= option. (all the fixed and standard error of random
effect estimators are almost identical)
Can someone explain to me why is that.
The codes I use:
R:
glmm1<-glmmML(mort30 ~ x , data=dat2,cluster=hospital,family=binomial)
print(sort(glmm1$posterior.mode))
SAS:
*
proc* *nlmixed* data*=*dat*;*
eta = b0 + b1*x+ u;
expeta = exp(eta);
p = expeta/(*1*+expeta);
model mort30 ~ binomial(*1*,p);
random u ~ normal(*0*,s2) subject=hospital out=blue;
*ru...