[R-sig-ME] R^2 for linear mixed effects models with glmer()

ASANTOS alexandresantosbr at yahoo.com.br
Wed Feb 7 11:40:17 CET 2018 

Thanks again Thierry,

          I will test a possible collinearity of my explained variables.

Best wishes,

Alexandre

-- 
======================================================================
Alexandre dos Santos
Proteção Florestal
IFMT - Instituto Federal de Educação, Ciência e Tecnologia de Mato Grosso
Campus Cáceres
Caixa Postal 244
Avenida dos Ramires, s/n
Bairro: Distrito Industrial
Cáceres - MT                      CEP: 78.200-000
Fone: (+55) 65 99686-6970 (VIVO) (+55) 65 3221-2674 (FIXO)

         alexandre.santos at cas.ifmt.edu.br
Lattes: http://lattes.cnpq.br/1360403201088680
OrcID: orcid.org/0000-0001-8232-6722
Researchgate: www.researchgate.net/profile/Alexandre_Santos10
LinkedIn: br.linkedin.com/in/alexandre-dos-santos-87961635
Mendeley:www.mendeley.com/profiles/alexandre-dos-santos6/
======================================================================

Em 07/02/2018 05:38, Thierry Onkelinx escreveu:
> Dear Alexandre,
>
> The str() is not on the same dataset that is used in the model. One
> has 288 observations, the other 72.
>
> Furthermore look at the correlation among the covariates. I expect
> that the issues are due to strong collinearity.
>
> Best regards,
>
>
> ir. Thierry Onkelinx
> Statisticus / Statistician
>
> Vlaamse Overheid / Government of Flanders
> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
> AND FOREST
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx at 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
> ///////////////////////////////////////////////////////////////////////////////////////////
>
>
>
>

>> Thanks Thierry,
>>
>>         Sorry about my question, I start to use the mixed models approach
>> recently. My structure of data was:
>>
>> 'data.frame':	288 obs. of  13 variables:
>>   $ Transecto        : int  2 2 2 2 2 2 3 3 3 3 ...
>>   $ Ponto            : int  1 2 3 4 5 6 1 2 3 4 ...
>>   $ Distancia        : int  160 120 80 40 20 0 160 120 80 40 ...
>>   $ tipo_trat        : Factor w/ 4 levels "","controle",..: 3 3 3 3 3 3 4 4 4
>> 4 ...
>>   $ umid_inici       : num  81.3 84.1 81.3 83.9 81.9 ...
>>   $ umid_final       : num  63.7 68 66.2 66.8 66.4 ...
>>   $ temp_inici       : num  19.1 19.5 19.5 19.1 19.1 ...
>>   $ temp_final       : num  29.1 27.8 27.6 28 28.6 ...
>>   $ abertu_dossel    : num  35.6 20.8 28.9 30.6 27.1 ...
>>   $ delta.umidade    : num  -17.6 -16.1 -15.2 -17.1 -15.5 ...
>>   $ delta.temperatura: num  9.95 8.3 8.1 8.91 9.5 ...
>>   $ remocao          : num  0.02 0 0.1 0 0.08 0 1 0.04 0.08 0.42 ...
>>   $ riqueza          : int  3 9 3 3 4 5 4 3 2 5 ...
>>
>>
>> And the summary of model below. In my case is a adjustment problem or in
>> r.squaredGLMM() function?
>>
>>> summary(mT)
>> Generalized linear mixed model fit by maximum likelihood (Laplace
>> Approximation) [
>> glmerMod]
>>   Family: poisson  ( log )
>> Formula: riqueza ~ tipo_trat + temp_final + temp_inici + umid_inici +
>>      umid_final + (1 | Ponto)
>>     Data: d1
>> Control:
>> glmerControl(check.conv.singular = "warning", optCtrl = list(maxfun =
>> 1e+05))
>>
>>       AIC      BIC   logLik deviance df.resid
>>     326.1    344.3   -155.0    310.1       64
>>
>> Scaled residuals:
>>      Min      1Q  Median      3Q     Max
>> -1.8416 -0.7947 -0.2221  0.7253  2.4622
>>
>> Random effects:
>>   Groups Name        Variance  Std.Dev.
>>   Ponto  (Intercept) 5.293e-17 7.275e-09
>> Number of obs: 72, groups:  Ponto, 6
>>
>> Fixed effects:
>>                        Estimate Std. Error z value Pr(>|z|)
>> (Intercept)           -5.18941    3.18615  -1.629   0.1034
>> tipo_trattrat_euc     -0.51209    0.29360  -1.744   0.0811 .
>> tipo_trattrat_mat_euc -0.43877    0.25986  -1.688   0.0913 .
>> temp_final             0.06914    0.04144   1.669   0.0952 .
>> temp_inici             0.07176    0.05479   1.310   0.1903
>> umid_inici             0.03270    0.02543   1.286   0.1985
>> umid_final             0.01774    0.01660   1.069   0.2850
>> ---
>> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>>
>> Correlation of Fixed Effects:
>>              (Intr) tp_tr_ tp_t__ tmp_fn tmp_nc umd_nc
>> tp_trttrt_c  0.457
>> tp_trttrt__  0.525  0.358
>> temp_final  -0.546 -0.393 -0.143
>> temp_inici  -0.750 -0.557 -0.696  0.037
>> umid_inici  -0.755  0.028 -0.522 -0.020  0.667
>> umid_final  -0.287 -0.661  0.236  0.651  0.023 -0.352
>> convergence code: 0
>> Model failed to converge with max|grad| = 0.00894145 (tol = 0.001, component
>> 1)
>> singular fit
>> Model is nearly unidentifiable: very large eigenvalue
>>   - Rescale variables?
>> Model is nearly unidentifiable: large eigenvalue ratio
>>   - Rescale variables?
>>
>> --
>> ======================================================================
>> Alexandre dos Santos
>> Proteção Florestal
>> IFMT - Instituto Federal de Educação, Ciência e Tecnologia de Mato Grosso
>> Campus Cáceres
>> Caixa Postal 244
>> Avenida dos Ramires, s/n
>> Bairro: Distrito Industrial
>> Cáceres - MT                      CEP: 78.200-000
>> Fone: (+55) 65 99686-6970 (VIVO) (+55) 65 3221-2674 (FIXO)

>>          alexandre.santos at cas.ifmt.edu.br
>> Lattes: http://lattes.cnpq.br/1360403201088680
>> OrcID: orcid.org/0000-0001-8232-6722
>> Researchgate: www.researchgate.net/profile/Alexandre_Santos10
>> LinkedIn: br.linkedin.com/in/alexandre-dos-santos-87961635
>> Mendeley:www.mendeley.com/profiles/alexandre-dos-santos6/
>> ======================================================================
>>
>> Em 06/02/2018 06:15, Thierry Onkelinx escreveu:
>>
>> Dear Alexandre,
>>
>> First of all you need to get a stable model. Otherwise any number you
>> get from it is meaningless. Can you provide more detail on your model.
>> E.g. summary(mT), str(d1), ...
>>
>> Best regards,
>>
>> ir. Thierry Onkelinx
>> Statisticus / Statistician
>>
>> Vlaamse Overheid / Government of Flanders
>> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
>> AND FOREST
>> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
>> thierry.onkelinx at 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
>> ///////////////////////////////////////////////////////////////////////////////////////////
>>
>>
>>
>>
>> 2018-02-05 20:15 GMT+01:00 ASANTOS via R-sig-mixed-models
>> <r-sig-mixed-models at r-project.org>:
>>
>> Dear Mix Models Members,
>>
>>          I try to extract R^2 for linear mixed effects models with
>> glmer() function with poisson distribution using r.squaredGLMM() in
>> MuMIn package, but doesn't work. My output always show:
>>
>> #Model ajusted > mT <-glmer(riqueza ~tipo_trat+(1|Ponto),data=d1, +
>> family=poisson, control = glmerControl(check.conv.singular =
>> "warning",optCtrl = list(maxfun=100000))) Warning messages: 1: In
>> checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :Model
>> failed to converge with max|grad| = 0.00894145 (tol = 0.001, component
>> 1) 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl =
>> control$checkConv, :singular fit 3: In checkConv(attr(opt, "derivs"),
>> opt$par, ctrl = control$checkConv, :Model is nearly unidentifiable: very
>> large eigenvalue - Rescale variables?;Model is nearly unidentifiable:
>> large eigenvalue ratio - Rescale variables? #R^2 conditional and
>> marginal > r.squaredGLMM(mT) Error in glmer(formula = riqueza ~
>> tipo_trat + temp_final + temp_inici + : fitting model with the
>> observation-level random effect term failed. Add the term manually In
>> addition: Warning message: In value[[3L]](cond) :(p <- ncol(X)) ==
>> ncol(Y) is not TRUE
>>
>>        I change almost all parameters indicating by web posts like
>> glmerControl, maxfun, etc. There are other approaches to calculate the
>> conditional and marginal R^2 for my model with lme4 package?
>>
>> Thanks in advance,
>>
>> Alexandre
>>
>> --
>> ======================================================================
>> Alexandre dos Santos
>> Proteção Florestal
>> IFMT - Instituto Federal de Educação, Ciência e Tecnologia de Mato Grosso
>> Campus Cáceres
>> Caixa Postal 244
>> Avenida dos Ramires, s/n
>> Bairro: Distrito Industrial
>> Cáceres - MT                      CEP: 78.200-000
>> Fone: (+55) 65 99686-6970 (VIVO) (+55) 65 3221-2674 (FIXO)
>>
>>           alexandre.santos at cas.ifmt.edu.br
>> Lattes:http://lattes.cnpq.br/1360403201088680
>> OrcID: orcid.org/0000-0001-8232-6722
>> Researchgate:www.researchgate.net/profile/Alexandre_Santos10
>> LinkedIn: br.linkedin.com/in/alexandre-dos-santos-87961635
>> Mendeley:www.mendeley.com/profiles/alexandre-dos-santos6/
>> ======================================================================
>>
>>
>>          [[alternative HTML version deleted]]
>>
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>>

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