On 25 Jul 2018, at 16:09, Phillip Alday <phillip.alday at mpi.nl> wrote:
(Please keep the list in CC.)
The output of
get.models(models,subset=delta<5)
would be more interesting. Or even better:
lapply(get.models(models,subset=delta<5), formula)
So that we see which formulas are being labelled as identical.
Phillip
On 07/25/2018 04:56 PM, Helen McCallin wrote:
Hi Phil
Thank you so much for your reply. Please find the codes I am using
below. Is this what you mean?
ae <- read.csv(file=file.choose())
options(na.action="na.fail")
global.model<-glmer(
cbind(numerator,total-numerator)~d+s+t+p+d:s:t:p+d:s:t+d:s:p+d:t:p+s:t:p+d:t+d:s+d:p+s:t+s:p+t:p+(1|random),
data=ae, family=binomial)
options(max.print=1000000)
dredge(global.model,beta=c("none"),evaluate=TRUE,rank="AICc")
ae.model <- glmer(
cbind(numerator,total-numerator)~d+s+t+p+d:s:t:p+d:s:t+d:s:p+d:t:p+s:t:p+d:t+d:s+d:p+s:t+s:p+t:p+(1|random),
data=ae,family=binomial)
models <- dredge(ae.model)
summary(model.avg(get.models(models,subset=delta<5)))
Many thanks for any help.
Best wishes
Helen
On 25 Jul 2018, at 13:50, Phillip Alday <phillip.alday at mpi.nl
<mailto:phillip.alday at mpi.nl>> wrote:
Hi Helen,
model.avg() tells you which models are duplicates. What do the formulas
look like for those models? Seeing the formulae may help identify what
model.avg() gets stuck on.
Best,
Phillip
On 07/23/2018 11:33 AM, Helen McCallin wrote:
Hi
I am running a glmer model on a response variable with binomial
distribution and random term. My data has 3 explanatory categorical
variables and I have successfully run dredge() on them and their
interactions to get AICc values.
I want model averaging to provide output with coefficients and an
index of relative importance of fixed effects from those models;
within a delta constraint that I specify.I can get this using the
code below for alternative datasets but not for this dataset.
model.avg() produces this error message:
Error in model.avg.default(get.models(models, subset = delta < 5)) :
models are not unique. Duplicates: '2 = 3 = 4' and '10 = 11'
This doesn't make sense, DREDGE does not (cannot) produce duplicate
models ? each model is a unique iteration within the full model, yet
the error message indicates that MODEL AVERAGE identified ?duplicate?
models from within DREDGE output. R fails to run MODEL AVERAGE under
these circumstances - producing no further output.
Has anyone else experienced similar problem (with 'not unique',
duplicate models) via MODEL AVERAGE?
Is there a workaround for the error that prevents me running MODEL
AVERAGE due to perceived ?duplicate? models in DREDGE?
Many thanks for any help anyone can provide.
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