Helen McCallin
2018-07-13 17:17:48 UTC
Hi
I would like to post the following question to the forum please?
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.
This is what I have input.
ae <- read.csv(file=file.choose())
options(na.action="na.fail")
global.model<-glmer(
cbind(numerator,total-numerator)~d+s+t+d:s:t+d:s+d:t+s:t+(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+d:s:t+d:s+d:t+s:t+(1|random),
data=ae,family=binomial)
models <- dredge(ae.model)
summary(model.avg(get.models(models,subset=delta<5)))
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?
I am happy to provide Dropbox link to data.
Thanks in advance for any help given. Summary of data below:
summary(ae)
p t day hour scan random behaviour
ae:182 blood :42 Min. :1.000 Min. :1.000 Min. : 0 ae_blood_1_1: 7 alert:182
egg :35 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:10 ae_blood_1_2: 7
repellentfree:63 Median :2.000 Median :2.000 Median :30 ae_blood_1_3: 7
wolf :42 Mean :1.654 Mean :1.962 Mean :30 ae_blood_2_1: 7
3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:50 ae_blood_2_2: 7
Max. :3.000 Max. :3.000 Max. :60 ae_blood_2_3: 7
(Other) :140
numerator total proportion percentage d s
Min. : 0.0000 Min. :17 Min. :0.00000 Min. : 0.000 E :14 1 - very light wind:21
1st Qu.: 0.0000 1st Qu.:17 1st Qu.:0.00000 1st Qu.: 0.000 SE:84 2 - light wind :70
Median : 0.0000 Median :17 Median :0.00000 Median : 0.000 SW:35 3 - moderate wind :77
Mean : 0.5824 Mean :17 Mean :0.03426 Mean : 3.426 W :49 4 - heavy wind :14
3rd Qu.: 0.0000 3rd Qu.:17 3rd Qu.:0.00000 3rd Qu.: 0.000
Max. :16.0000 Max. :17 Max. :0.94118 Max. :94.118
Many thanks for your help
Helen McCallin
[[alternative HTML version deleted]]
I would like to post the following question to the forum please?
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.
This is what I have input.
ae <- read.csv(file=file.choose())
options(na.action="na.fail")
global.model<-glmer(
cbind(numerator,total-numerator)~d+s+t+d:s:t+d:s+d:t+s:t+(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+d:s:t+d:s+d:t+s:t+(1|random),
data=ae,family=binomial)
models <- dredge(ae.model)
summary(model.avg(get.models(models,subset=delta<5)))
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?
I am happy to provide Dropbox link to data.
Thanks in advance for any help given. Summary of data below:
summary(ae)
p t day hour scan random behaviour
ae:182 blood :42 Min. :1.000 Min. :1.000 Min. : 0 ae_blood_1_1: 7 alert:182
egg :35 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:10 ae_blood_1_2: 7
repellentfree:63 Median :2.000 Median :2.000 Median :30 ae_blood_1_3: 7
wolf :42 Mean :1.654 Mean :1.962 Mean :30 ae_blood_2_1: 7
3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:50 ae_blood_2_2: 7
Max. :3.000 Max. :3.000 Max. :60 ae_blood_2_3: 7
(Other) :140
numerator total proportion percentage d s
Min. : 0.0000 Min. :17 Min. :0.00000 Min. : 0.000 E :14 1 - very light wind:21
1st Qu.: 0.0000 1st Qu.:17 1st Qu.:0.00000 1st Qu.: 0.000 SE:84 2 - light wind :70
Median : 0.0000 Median :17 Median :0.00000 Median : 0.000 SW:35 3 - moderate wind :77
Mean : 0.5824 Mean :17 Mean :0.03426 Mean : 3.426 W :49 4 - heavy wind :14
3rd Qu.: 0.0000 3rd Qu.:17 3rd Qu.:0.00000 3rd Qu.: 0.000
Max. :16.0000 Max. :17 Max. :0.94118 Max. :94.118
Many thanks for your help
Helen McCallin
[[alternative HTML version deleted]]