using aictab() for lmer and glmer modelsFunction not defined when calling aictabError message in lme4::glmer: “ 'what' must be a character string or a function”After trying various optimzers, model simplification running more iterations, when fitting GLMMs, R still produces warning messagesGeneralised linear mixed model error (binary response)glmer with user-defined link function giving error: (maxstephalfit) PIRLS step-halvings failed to reduce devianceA loop to incorporate random effects into generalized linear mixed models using the function lmer()Getting error messages when comparing negative binomial models (glm.nb) using aictabGetting AICc for multiple negative binomial glm modelsR Structural Equation Model in R with random effect errorMixed GLMM Model - Issues with using weights (Using R package 'lme4'Percentage and glmer

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using aictab() for lmer and glmer models


Function not defined when calling aictabError message in lme4::glmer: “ 'what' must be a character string or a function”After trying various optimzers, model simplification running more iterations, when fitting GLMMs, R still produces warning messagesGeneralised linear mixed model error (binary response)glmer with user-defined link function giving error: (maxstephalfit) PIRLS step-halvings failed to reduce devianceA loop to incorporate random effects into generalized linear mixed models using the function lmer()Getting error messages when comparing negative binomial models (glm.nb) using aictabGetting AICc for multiple negative binomial glm modelsR Structural Equation Model in R with random effect errorMixed GLMM Model - Issues with using weights (Using R package 'lme4'Percentage and glmer






.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty height:90px;width:728px;box-sizing:border-box;








1















I am trying to do AICc model selection using aictab() output. This posting is similar (Function not defined when calling aictab) but does not apply to my problem because it used glm.nb models and I am using lmer models and also glmer(family=binomial). The aictab documentation (https://www.rdocumentation.org/packages/AICcmodavg/versions/2.2-1/topics/aictab) says the function can handle lm, lme and glm models: does that include lmer and glmer?



My code actually worked a few days ago, but recently broke and returned this error code




Error in aictab.default() : Function not yet defined for this object class




m1 <- lmer(y ~ x + (1|z), data=df)
m2 <- lmer(y ~ w + (1|z), data=df)
m3 <- lmer(y ~ v + (1|z), data=df)
cand.set <- list(m1, m2, m3)
names <- list("x", "w", "v")
aictab(cand.set, modnames=names, second.ord=TRUE, nobs=NULL, sort=TRUE)









share|improve this question






























    1















    I am trying to do AICc model selection using aictab() output. This posting is similar (Function not defined when calling aictab) but does not apply to my problem because it used glm.nb models and I am using lmer models and also glmer(family=binomial). The aictab documentation (https://www.rdocumentation.org/packages/AICcmodavg/versions/2.2-1/topics/aictab) says the function can handle lm, lme and glm models: does that include lmer and glmer?



    My code actually worked a few days ago, but recently broke and returned this error code




    Error in aictab.default() : Function not yet defined for this object class




    m1 <- lmer(y ~ x + (1|z), data=df)
    m2 <- lmer(y ~ w + (1|z), data=df)
    m3 <- lmer(y ~ v + (1|z), data=df)
    cand.set <- list(m1, m2, m3)
    names <- list("x", "w", "v")
    aictab(cand.set, modnames=names, second.ord=TRUE, nobs=NULL, sort=TRUE)









    share|improve this question


























      1












      1








      1








      I am trying to do AICc model selection using aictab() output. This posting is similar (Function not defined when calling aictab) but does not apply to my problem because it used glm.nb models and I am using lmer models and also glmer(family=binomial). The aictab documentation (https://www.rdocumentation.org/packages/AICcmodavg/versions/2.2-1/topics/aictab) says the function can handle lm, lme and glm models: does that include lmer and glmer?



      My code actually worked a few days ago, but recently broke and returned this error code




      Error in aictab.default() : Function not yet defined for this object class




      m1 <- lmer(y ~ x + (1|z), data=df)
      m2 <- lmer(y ~ w + (1|z), data=df)
      m3 <- lmer(y ~ v + (1|z), data=df)
      cand.set <- list(m1, m2, m3)
      names <- list("x", "w", "v")
      aictab(cand.set, modnames=names, second.ord=TRUE, nobs=NULL, sort=TRUE)









      share|improve this question
















      I am trying to do AICc model selection using aictab() output. This posting is similar (Function not defined when calling aictab) but does not apply to my problem because it used glm.nb models and I am using lmer models and also glmer(family=binomial). The aictab documentation (https://www.rdocumentation.org/packages/AICcmodavg/versions/2.2-1/topics/aictab) says the function can handle lm, lme and glm models: does that include lmer and glmer?



      My code actually worked a few days ago, but recently broke and returned this error code




      Error in aictab.default() : Function not yet defined for this object class




      m1 <- lmer(y ~ x + (1|z), data=df)
      m2 <- lmer(y ~ w + (1|z), data=df)
      m3 <- lmer(y ~ v + (1|z), data=df)
      cand.set <- list(m1, m2, m3)
      names <- list("x", "w", "v")
      aictab(cand.set, modnames=names, second.ord=TRUE, nobs=NULL, sort=TRUE)






      r lme4






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Mar 22 at 0:38









      Ben Bolker

      136k13229322




      136k13229322










      asked Mar 21 at 23:57









      user7898623user7898623

      83




      83






















          1 Answer
          1






          active

          oldest

          votes


















          2














          tl;dr you loaded the lmerTest package, so your models have a different class, which is confusing aictab(). You could either make sure you have lme4 and not lmerTest loaded when you fit the models, or use bbmle::AICctab(), which seems slightly more robust. (It might be worth contacting the package maintainer of AICcmodavg about this ...)



          Set up with lme4:



          library(lme4)
          library(AICcmodavg)

          ss <- transform(sleepstudy, junk1=rnorm(nrow(sleepstudy)),
          junk2=rnorm(nrow(sleepstudy)))
          m1 <- lmer(Reaction ~ Days + (1|Subject), data=ss, REML=FALSE)
          m2 <- update(m1, . ~ . - Days + junk1)
          m3 <- update(m1, . ~ . - Days + junk2)
          cand.set <- list(m1, m2, m3)
          names <- c("Days", "junk1", "junk2") ## this should be a vector, not a list ...


          This works fine:



          aictab(cand.set, modnames=names, second.ord=TRUE, nobs=NULL, sort=TRUE)
          ## K AICc Delta_AICc AICcWt Cum.Wt LL
          ## Days 4 1802.31 0.00 1 1 -897.04
          ## junk2 4 1918.47 116.16 0 1 -955.12
          ## junk1 4 1918.71 116.40 0 1 -955.24


          Now load lmerTest and refit the models (we could just do e.g. m1 <- as(m1, "lmerModLmerTest") but it's easy enough to refit).



          library(lmerTest)
          m1 <- lmer(Reaction ~ Days + (1|Subject), data=ss, REML=FALSE)
          m2 <- update(m1, . ~ . - Days + junk1)
          m3 <- update(m1, . ~ . - Days + junk2)
          cand.set <- list(m1, m2, m3)
          aictab(cand.set, modnames=names, second.ord=TRUE, nobs=NULL, sort=TRUE)



          Error in aictab.default(cand.set, modnames = names, second.ord = TRUE, :
          Function not yet defined for this object class




          The bbmle::AICctab() function is a little bit more robust because it relies only on the logLik method being defined for a class (by default it gives a table with only the delta-AIC and df)



          library(bbmle)
          AICctab(m1, m2, m3, mnames=names, base=TRUE, weights=TRUE, logLik=TRUE)
          ## logLik AICc dLogLik dAICc df weight
          ## Days -897.0 1802.3 58.2 0.0 4 1
          ## junk2 -955.1 1918.5 0.1 116.2 4 <0.001
          ## junk1 -955.2 1918.7 0.0 116.4 4 <0.001





          share|improve this answer























          • Thank you so much for the response. Detaching the lmerTest package worked perfectly.

            – user7898623
            Mar 22 at 0:59











          • if this answered your question you're encouraged to click the check-mark to accept it ...

            – Ben Bolker
            Mar 22 at 1:03











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          2














          tl;dr you loaded the lmerTest package, so your models have a different class, which is confusing aictab(). You could either make sure you have lme4 and not lmerTest loaded when you fit the models, or use bbmle::AICctab(), which seems slightly more robust. (It might be worth contacting the package maintainer of AICcmodavg about this ...)



          Set up with lme4:



          library(lme4)
          library(AICcmodavg)

          ss <- transform(sleepstudy, junk1=rnorm(nrow(sleepstudy)),
          junk2=rnorm(nrow(sleepstudy)))
          m1 <- lmer(Reaction ~ Days + (1|Subject), data=ss, REML=FALSE)
          m2 <- update(m1, . ~ . - Days + junk1)
          m3 <- update(m1, . ~ . - Days + junk2)
          cand.set <- list(m1, m2, m3)
          names <- c("Days", "junk1", "junk2") ## this should be a vector, not a list ...


          This works fine:



          aictab(cand.set, modnames=names, second.ord=TRUE, nobs=NULL, sort=TRUE)
          ## K AICc Delta_AICc AICcWt Cum.Wt LL
          ## Days 4 1802.31 0.00 1 1 -897.04
          ## junk2 4 1918.47 116.16 0 1 -955.12
          ## junk1 4 1918.71 116.40 0 1 -955.24


          Now load lmerTest and refit the models (we could just do e.g. m1 <- as(m1, "lmerModLmerTest") but it's easy enough to refit).



          library(lmerTest)
          m1 <- lmer(Reaction ~ Days + (1|Subject), data=ss, REML=FALSE)
          m2 <- update(m1, . ~ . - Days + junk1)
          m3 <- update(m1, . ~ . - Days + junk2)
          cand.set <- list(m1, m2, m3)
          aictab(cand.set, modnames=names, second.ord=TRUE, nobs=NULL, sort=TRUE)



          Error in aictab.default(cand.set, modnames = names, second.ord = TRUE, :
          Function not yet defined for this object class




          The bbmle::AICctab() function is a little bit more robust because it relies only on the logLik method being defined for a class (by default it gives a table with only the delta-AIC and df)



          library(bbmle)
          AICctab(m1, m2, m3, mnames=names, base=TRUE, weights=TRUE, logLik=TRUE)
          ## logLik AICc dLogLik dAICc df weight
          ## Days -897.0 1802.3 58.2 0.0 4 1
          ## junk2 -955.1 1918.5 0.1 116.2 4 <0.001
          ## junk1 -955.2 1918.7 0.0 116.4 4 <0.001





          share|improve this answer























          • Thank you so much for the response. Detaching the lmerTest package worked perfectly.

            – user7898623
            Mar 22 at 0:59











          • if this answered your question you're encouraged to click the check-mark to accept it ...

            – Ben Bolker
            Mar 22 at 1:03















          2














          tl;dr you loaded the lmerTest package, so your models have a different class, which is confusing aictab(). You could either make sure you have lme4 and not lmerTest loaded when you fit the models, or use bbmle::AICctab(), which seems slightly more robust. (It might be worth contacting the package maintainer of AICcmodavg about this ...)



          Set up with lme4:



          library(lme4)
          library(AICcmodavg)

          ss <- transform(sleepstudy, junk1=rnorm(nrow(sleepstudy)),
          junk2=rnorm(nrow(sleepstudy)))
          m1 <- lmer(Reaction ~ Days + (1|Subject), data=ss, REML=FALSE)
          m2 <- update(m1, . ~ . - Days + junk1)
          m3 <- update(m1, . ~ . - Days + junk2)
          cand.set <- list(m1, m2, m3)
          names <- c("Days", "junk1", "junk2") ## this should be a vector, not a list ...


          This works fine:



          aictab(cand.set, modnames=names, second.ord=TRUE, nobs=NULL, sort=TRUE)
          ## K AICc Delta_AICc AICcWt Cum.Wt LL
          ## Days 4 1802.31 0.00 1 1 -897.04
          ## junk2 4 1918.47 116.16 0 1 -955.12
          ## junk1 4 1918.71 116.40 0 1 -955.24


          Now load lmerTest and refit the models (we could just do e.g. m1 <- as(m1, "lmerModLmerTest") but it's easy enough to refit).



          library(lmerTest)
          m1 <- lmer(Reaction ~ Days + (1|Subject), data=ss, REML=FALSE)
          m2 <- update(m1, . ~ . - Days + junk1)
          m3 <- update(m1, . ~ . - Days + junk2)
          cand.set <- list(m1, m2, m3)
          aictab(cand.set, modnames=names, second.ord=TRUE, nobs=NULL, sort=TRUE)



          Error in aictab.default(cand.set, modnames = names, second.ord = TRUE, :
          Function not yet defined for this object class




          The bbmle::AICctab() function is a little bit more robust because it relies only on the logLik method being defined for a class (by default it gives a table with only the delta-AIC and df)



          library(bbmle)
          AICctab(m1, m2, m3, mnames=names, base=TRUE, weights=TRUE, logLik=TRUE)
          ## logLik AICc dLogLik dAICc df weight
          ## Days -897.0 1802.3 58.2 0.0 4 1
          ## junk2 -955.1 1918.5 0.1 116.2 4 <0.001
          ## junk1 -955.2 1918.7 0.0 116.4 4 <0.001





          share|improve this answer























          • Thank you so much for the response. Detaching the lmerTest package worked perfectly.

            – user7898623
            Mar 22 at 0:59











          • if this answered your question you're encouraged to click the check-mark to accept it ...

            – Ben Bolker
            Mar 22 at 1:03













          2












          2








          2







          tl;dr you loaded the lmerTest package, so your models have a different class, which is confusing aictab(). You could either make sure you have lme4 and not lmerTest loaded when you fit the models, or use bbmle::AICctab(), which seems slightly more robust. (It might be worth contacting the package maintainer of AICcmodavg about this ...)



          Set up with lme4:



          library(lme4)
          library(AICcmodavg)

          ss <- transform(sleepstudy, junk1=rnorm(nrow(sleepstudy)),
          junk2=rnorm(nrow(sleepstudy)))
          m1 <- lmer(Reaction ~ Days + (1|Subject), data=ss, REML=FALSE)
          m2 <- update(m1, . ~ . - Days + junk1)
          m3 <- update(m1, . ~ . - Days + junk2)
          cand.set <- list(m1, m2, m3)
          names <- c("Days", "junk1", "junk2") ## this should be a vector, not a list ...


          This works fine:



          aictab(cand.set, modnames=names, second.ord=TRUE, nobs=NULL, sort=TRUE)
          ## K AICc Delta_AICc AICcWt Cum.Wt LL
          ## Days 4 1802.31 0.00 1 1 -897.04
          ## junk2 4 1918.47 116.16 0 1 -955.12
          ## junk1 4 1918.71 116.40 0 1 -955.24


          Now load lmerTest and refit the models (we could just do e.g. m1 <- as(m1, "lmerModLmerTest") but it's easy enough to refit).



          library(lmerTest)
          m1 <- lmer(Reaction ~ Days + (1|Subject), data=ss, REML=FALSE)
          m2 <- update(m1, . ~ . - Days + junk1)
          m3 <- update(m1, . ~ . - Days + junk2)
          cand.set <- list(m1, m2, m3)
          aictab(cand.set, modnames=names, second.ord=TRUE, nobs=NULL, sort=TRUE)



          Error in aictab.default(cand.set, modnames = names, second.ord = TRUE, :
          Function not yet defined for this object class




          The bbmle::AICctab() function is a little bit more robust because it relies only on the logLik method being defined for a class (by default it gives a table with only the delta-AIC and df)



          library(bbmle)
          AICctab(m1, m2, m3, mnames=names, base=TRUE, weights=TRUE, logLik=TRUE)
          ## logLik AICc dLogLik dAICc df weight
          ## Days -897.0 1802.3 58.2 0.0 4 1
          ## junk2 -955.1 1918.5 0.1 116.2 4 <0.001
          ## junk1 -955.2 1918.7 0.0 116.4 4 <0.001





          share|improve this answer













          tl;dr you loaded the lmerTest package, so your models have a different class, which is confusing aictab(). You could either make sure you have lme4 and not lmerTest loaded when you fit the models, or use bbmle::AICctab(), which seems slightly more robust. (It might be worth contacting the package maintainer of AICcmodavg about this ...)



          Set up with lme4:



          library(lme4)
          library(AICcmodavg)

          ss <- transform(sleepstudy, junk1=rnorm(nrow(sleepstudy)),
          junk2=rnorm(nrow(sleepstudy)))
          m1 <- lmer(Reaction ~ Days + (1|Subject), data=ss, REML=FALSE)
          m2 <- update(m1, . ~ . - Days + junk1)
          m3 <- update(m1, . ~ . - Days + junk2)
          cand.set <- list(m1, m2, m3)
          names <- c("Days", "junk1", "junk2") ## this should be a vector, not a list ...


          This works fine:



          aictab(cand.set, modnames=names, second.ord=TRUE, nobs=NULL, sort=TRUE)
          ## K AICc Delta_AICc AICcWt Cum.Wt LL
          ## Days 4 1802.31 0.00 1 1 -897.04
          ## junk2 4 1918.47 116.16 0 1 -955.12
          ## junk1 4 1918.71 116.40 0 1 -955.24


          Now load lmerTest and refit the models (we could just do e.g. m1 <- as(m1, "lmerModLmerTest") but it's easy enough to refit).



          library(lmerTest)
          m1 <- lmer(Reaction ~ Days + (1|Subject), data=ss, REML=FALSE)
          m2 <- update(m1, . ~ . - Days + junk1)
          m3 <- update(m1, . ~ . - Days + junk2)
          cand.set <- list(m1, m2, m3)
          aictab(cand.set, modnames=names, second.ord=TRUE, nobs=NULL, sort=TRUE)



          Error in aictab.default(cand.set, modnames = names, second.ord = TRUE, :
          Function not yet defined for this object class




          The bbmle::AICctab() function is a little bit more robust because it relies only on the logLik method being defined for a class (by default it gives a table with only the delta-AIC and df)



          library(bbmle)
          AICctab(m1, m2, m3, mnames=names, base=TRUE, weights=TRUE, logLik=TRUE)
          ## logLik AICc dLogLik dAICc df weight
          ## Days -897.0 1802.3 58.2 0.0 4 1
          ## junk2 -955.1 1918.5 0.1 116.2 4 <0.001
          ## junk1 -955.2 1918.7 0.0 116.4 4 <0.001






          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Mar 22 at 0:37









          Ben BolkerBen Bolker

          136k13229322




          136k13229322












          • Thank you so much for the response. Detaching the lmerTest package worked perfectly.

            – user7898623
            Mar 22 at 0:59











          • if this answered your question you're encouraged to click the check-mark to accept it ...

            – Ben Bolker
            Mar 22 at 1:03

















          • Thank you so much for the response. Detaching the lmerTest package worked perfectly.

            – user7898623
            Mar 22 at 0:59











          • if this answered your question you're encouraged to click the check-mark to accept it ...

            – Ben Bolker
            Mar 22 at 1:03
















          Thank you so much for the response. Detaching the lmerTest package worked perfectly.

          – user7898623
          Mar 22 at 0:59





          Thank you so much for the response. Detaching the lmerTest package worked perfectly.

          – user7898623
          Mar 22 at 0:59













          if this answered your question you're encouraged to click the check-mark to accept it ...

          – Ben Bolker
          Mar 22 at 1:03





          if this answered your question you're encouraged to click the check-mark to accept it ...

          – Ben Bolker
          Mar 22 at 1:03



















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