R help - Mar 2012 - Using MuMIn - error message

Hello,
I hope that you can bare with me. I am new to models, but I think I have a
pretty godd understanding of how to run them now, including how to use AICc
and Anova. The issue is that I have many factors that I wish to compare so
doing each one at a time would take forever.
I came across the MuMIn package and I was so excited, however I am getting
an error message and i don't know why.
Firstly it is a mixed model that I am running with lme4. The .csv file that
it comes from has more factors than I will probably use but I only listed
the ones that I wanted to test. I had also coded the ones that are labels
and not continuous data using the as.factor command and I ran the most
complex model and ran summary() and it seemed to have worked fine.

My model was:
fm2test<-lmer(Feeding~MF.vs.OF+Age.class+tide.h.l+Site+HDp+(1|Brood),
data=ABMtest.df)

and then I wanted to use the dredge command as so:
dd<-dredge(fm2test, trace=TRUE, rank="AICc", REML=FALSE)

I got an error:
Error in UseMethod("fixef") : 
  no applicable method for 'fixef' applied to an object of class
"mer"

I have no idea how to fix this. I have looked at ?dredge but cannot find
anything there and I am very new to R so any help would be greatly
appreciated. 

I want to run all the possible models using the factors and then want to
identify those models which best explain Feeding. I also have other models
to run and in addition I have more factors to use but wanted to do this
first as a test. 

Could someone also advise me on a way to list all models in order and if
each model is accessible in order to compare with Anova?

Thank you so much in advance.

Rachel

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This was solved by loading each package right before using it (I thought you
loaded both at the beginning). However I now have an additional issue. I ran
a small model with just three factors to try it out and it worked
beautifully, but when I tried to run it with all the factors this is what I
got when I tried to run the model:
> ABmodel<-read.csv(file.choose(), header=T)
> ABmodel$Brood<-as.factor(ABmodel$Brood)
> ABmodel$Site<-as.factor(ABmodel$Site)
> ABmodel$Age.class<-as.factor(ABmodel$Age.class)
> ABmodel$Tide<-as.factor(ABmodel$Tide)
> ABmodel$tide.h.l<-as.factor(ABmodel$tide.h.l)
> ABmodel$tide.inc.out<-as.factor(ABmodel$tide.inc.out)
> ABmodel$MF.vs.OF<-as.factor(ABmodel$MF.vs.OF)
> local({pkg <- select.list(sort(.packages(all.available >
TRUE)),graphics=TRUE)
+ if(nchar(pkg)) library(pkg, character.only=TRUE)})
> 
>
fullABmodel1<-lmer(Feeding~Site+Age.class+tide.h.l+tide.inc.out+Tide+HDp+NHDp+Foraging.Eff+MF.vs.OF+Age+HDr+NHDr+AllDr+(1|Brood),
> data=ABmodel, REML=FALSE)
Error in mer_finalize(ans) : Downdated X'X is not positive definite,
7.
>
fullABmodel1<-lmer(Feeding~Site+Age.class+tide.h.l+tide.inc.out+Tide+HDp+NHDp+Foraging.Eff+MF.vs.OF+Age+HDr+NHDr+AllDr+(1|Brood),
> data=ABmodel, REML=FALSE)
Error in mer_finalize(ans) : Downdated X'X is not positive definite,
7.
>
fullABmodel1<-lmer(Feeding~Site+Age.class+tide.h.l+tide.inc.out+Tide+HDp+NHDp+Foraging.Eff+MF.vs.OF+Age+HDr+NHDr+AllDr+(1|Brood),
> data=ABmodel, REML=FALSE)
Error in mer_finalize(ans) : Downdated X'X is not positive definite,
7.
> summary(fullABmodel)
Error in summary(fullABmodel) : 
  error in evaluating the argument 'object' in selecting a method for
function 'summary': Error: object 'fullABmodel' not
found
> 
I am attaching my raw .csv file. Note those above that are as.factors. The
file has more factors than I am using because I am also going to be looking
at different issues with it. Note that broods are nested within site but I
want to see if site has an effect. I ran a smaller model and it was weighted
and had a delta below 2 but not sure if that is only because so few factors
were being compared, so thought I would run it as a fixed then maybe as a
random later. http://r.789695.n4.nabble.com/file/n4500740/AllBroodmodel.csv
AllBroodmodel.csv 


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Hi Rachel

Do you accidentally have nlme and lme4 loaded at the same time? If so then try
unloading nlme.

regards
Mike
 
________________________________________
From: r-help-bounces at r-project.org [r-help-bounces at r-project.org] On
Behalf Of Dragonwalker [dragonwalkerart at hotmail.com]
Sent: 23 March 2012 21:55
To: r-help at r-project.org
Subject: [R] Using MuMIn - error message

Hello,
I hope that you can bare with me. I am new to models, but I think I have a
pretty godd understanding of how to run them now, including how to use AICc
and Anova. The issue is that I have many factors that I wish to compare so
doing each one at a time would take forever.
I came across the MuMIn package and I was so excited, however I am getting
an error message and i don't know why.
Firstly it is a mixed model that I am running with lme4. The .csv file that
it comes from has more factors than I will probably use but I only listed
the ones that I wanted to test. I had also coded the ones that are labels
and not continuous data using the as.factor command and I ran the most
complex model and ran summary() and it seemed to have worked fine.

My model was:
fm2test<-lmer(Feeding~MF.vs.OF+Age.class+tide.h.l+Site+HDp+(1|Brood),
data=ABMtest.df)

and then I wanted to use the dredge command as so:
dd<-dredge(fm2test, trace=TRUE, rank="AICc", REML=FALSE)

I got an error:
Error in UseMethod("fixef") :
  no applicable method for 'fixef' applied to an object of class
"mer"

I have no idea how to fix this. I have looked at ?dredge but cannot find
anything there and I am very new to R so any help would be greatly
appreciated.

I want to run all the possible models using the factors and then want to
identify those models which best explain Feeding. I also have other models
to run and in addition I have more factors to use but wanted to do this
first as a test.

Could someone also advise me on a way to list all models in order and if
each model is accessible in order to compare with Anova?

Thank you so much in advance.

Rachel

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Hello Mike,

I don't think I did, but I fixed the issue by loading each package before
use. The second issue was solved by removing a variable that was used to
create two other categorical variables. I think it must have been
recognising this.

Thanks for the help.

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