[R-sig-ME] AIC Comparison for MLM with Different Distributions

Kate R kr@g|tcode @end|ng |rom gm@||@com
Wed Mar 4 17:38:59 CET 2020

Hi Thierry,

Thank you for your response!

We are running different models - some have ordered factors as the response
variable and others have continuous or count data as the response variable, and
so I would still be curious to learn how to compare the AIC for models 1-4.

One post suggested that in order to compare normal with log-normal, you
would transform the AIC for the log-normal model with the following code: AIC
+ 2*sum(log(anxious)). I am still unsure how to compare the lmer/normal
models with the glmer/gamma models, as well as between glmer/gamma models
with different link functions.

For the ordered factor, I'd prefer to use the clmm for this, but it's
unfortunately common practice in the journals we publish in to use
continuous models (for ease of interpretation and convention), and so I'd
like to be able to show that the model fit is best with the clmm. In Burnham
& Anderson's book, they compare continuous models with count models, so I
hope it's possible to compare continuous with ordinal?

For the models with count data (frequency of use) as the response variable,
I suppose that we might also want to be able to compare poisson and
negative binomial distributions...

Overall, I'd like to learn how to compare models with different
distributions and/or links for my general knowledge and future use with
different research questions.

Many thanks again for your help!

On Wed, Mar 4, 2020 at 6:25 AM Thierry Onkelinx <thierry.onkelinx using inbo.be>

> Dear Kate,
> If your response variable is an ordered factor, then use the clmm model as
> that is one with the most appropriate distribution. All other models are
> workarounds. Hence the AIC comparison is not relevant.
> Best regards,
> ir. Thierry Onkelinx
> Statisticus / Statistician
> Vlaamse Overheid / Government of Flanders
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx using inbo.be
> Havenlaan 88 bus 73, 1000 Brussel
> www.inbo.be
> ///////////////////////////////////////////////////////////////////////////////////////////
> To call in the statistician after the experiment is done may be no more
> than asking him to perform a post-mortem examination: he may be able to say
> what the experiment died of. ~ Sir Ronald Aylmer Fisher
> The plural of anecdote is not data. ~ Roger Brinner
> The combination of some data and an aching desire for an answer does not
> ensure that a reasonable answer can be extracted from a given body of data.
> ~ John Tukey
> ///////////////////////////////////////////////////////////////////////////////////////////
> <https://www.inbo.be>
> Op di 3 mrt. 2020 om 23:30 schreef Kate R <kr.gitcode using gmail.com>:
>> Hi all,
>> Thank you in advance for your time and consideration! I am a
>> non-mathematically-inclined graduate student in communication just
>> learning
>> multilevel modeling.
>> I am trying to compare the AIC for 5 different models:
>>    1. model.mn5 <- lmer(anxious ~ num.cm + num.pmc + (1|userid), data =
>> df,
>>    REML = F)
>>    2. model.mn5.log <- lmer(log(anxious) ~ num.cm + num.pmc + (1|userid),
>>    data = df, REML = F)
>>    3. model.mn5.gamma.log <- glmer(anxious ~ num.cm + num.pmc +
>> (1|userid),
>>    data = df, family = Gamma(link="log"))
>>    4. model.mn5.gamma.id <- glmer(anxious ~ num.cm + num.pmc +
>> (1|userid),
>>    data = df, family = Gamma(link="identity"))
>>    5. model.ord5 <- clmm(anxious ~ num.cm + num.pmc + (1|userid), data =
>>    df, na.action = na.omit)
>> (num.cm is the group mean and num.pmc is the group-mean-centered score of
>> the predictor)
>> Despite many posts on various help forums, I understand that it's possible
>> to compare non-nested models with different distributions as long as all
>> terms, including constants, are retained (i.e. see Burnham & Anderson, Ch
>> 6.7 <https://www.springer.com/gp/book/9780387953649>), but that
>> different R
>> packages or model classes might handle constants differently or use
>> different algorithms (see point 7 <https://robjhyndman.com/hyndsight/aic/
>> >),
>> thus making it difficult to directly compare AIC values. To avoid
>> this non-comparability pitfall, it was suggested in one post to calculate
>> your own log-likelihood (though I'm having trouble finding this post
>> again).
>> Please could you help with the following:
>>    - What is the best practice for comparing the AICs for these 5 models?
>>    - What is the R-code for manually calculating the log-likelihood and/or
>>    the AIC to retain all terms, including constants?
>>    - Can you compare ordinal models (clmm) with the continuous models?
>>    - Do you recommend any other methods and/or packages for comparing
>>    models with different distributions and/or links?
>> Many thanks in advance for your time and consideration! I greatly
>> appreciate any suggestions.
>> Kind regards,
>> K
>>         [[alternative HTML version deleted]]
>> _______________________________________________
>> R-sig-mixed-models using r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

	[[alternative HTML version deleted]]

More information about the R-sig-mixed-models mailing list