[R-sig-ME] Negative binomial GLMM model/variables selection based in marginal R2 and conditional R2

Wed Jul 17 09:46:16 CEST 2019

Dear Alexandre,

IMHO the full model of your analysis should be based upon the design of
your study, not on any goodness-of-fit measurement. Having said that, both
random effects variables have only 4 levels. That is too few to get a
descent variance estimation. I'd recommend to consider both as fixed
effects.

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 using inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

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Op do 11 jul. 2019 om 23:47 schreef ASANTOS <alexandresantosbr using yahoo.com.br
>:

> Dear R-Mixed-Models Members,
>
>  ?????? ?????? I've like to chose my negative binomial GLMM better
> model/variables based in marginal R2 (variance explained by the fixed
> factor(s)) and conditional R2 (variance explained by both the fixed and
> random factors), but some times I have a great dissimilarities in this
> values, if I have gain in the conditional R2, my marginal R2 is poor and
> vice-versa (I make a little exercise by changes in the position on fixed
> and random effects in the models). In my example:
>
> *A) Model 1 - Inf_Leaves ~ Inf_YST + Age_months + (1 | Trat) - balance
> values between marginal and conditional R2*
>
> R2m R2c
>
> delta 0.4282151 0.5203953
>
> lognormal 0.5090799 0.6186677
>
> trigamma 0.3153259 0.3832049
>
>
> Generalized linear mixed model fit by maximum likelihood (Laplace
> Approximation) ['glmerMod']
>
> Family: Negative Binomial(0.9207)?? ( log )
>
> Formula: Inf_Leaves ~ Inf_YST + Age_months + (1 | Trat)
>
>  ???? Data: d3
>
>  ???????? AIC?????????? BIC???? logLik deviance df.resid
>
>  ????4500.6???? 4521.9?? -2245.3???? 4490.6?????????? 519
>
> Scaled residuals:
>
> Min?????????? 1Q?? Median?????????? 3Q???????? Max
>
> -0.9413 -0.7254 -0.4113?? 0.5294?? 7.2853
>
> Random effects:
>
> Groups Name?????????????? Variance Std.Dev.
>
> Trat???? (Intercept) 0.2176 ????0.4664
>
> Number of obs: 524, groups:?? Trat, 4
>
> Fixed effects:
>
>  ?????????????????????????? Estimate Std. Error z value Pr(>|z|)
>
> (Intercept)?? 0.2847245?? 0.2913635???? 0.977 0.328
>
> Inf_YST???????? -0.0016482?? 0.0003483?? -4.732 2.22e-06 ***
>
> Age_months???? 0.3144764?? 0.0183616?? 17.127?? < 2e-16 ***
>
> ---
>
> Signif. codes:?? 0 ???***??? 0.001 ???**??? 0.01 ???*??? 0.05 ???.??? 0.1
> ??? ??? 1
>
> Correlation of Fixed Effects:
>
>  ???????????????????? (Intr) In_YST
>
> Inf_YST???????? 0.171
>
> Age_months -0.558 -0.532
>
> convergence code: 0
>
> Model failed to converge with max|grad| = 0.00631137 (tol = 0.001,
> component 1)
>
> Model is nearly unidentifiable: very large eigenvalue
>
> - Rescale variables?
>
> Model is nearly unidentifiable: large eigenvalue ratio
>
> - Rescale variables?
>
>
> *B) Model 2 -?? Inf_Leaves ~ Inf_YST + Trat + (1 | Age_months) - a better
> conditional but poor marginal R2*
>
> R2m R2c
>
> delta???????? 0.1626844 0.7257397
>
> lognormal 0.1725712 0.7698453
>
> trigamma?? 0.1489258 0.6643626
>
>
> Generalized linear mixed model fit by maximum likelihood (Laplace
> Approximation) ['glmerMod']
>
> Family: Negative Binomial(1.8431)?? ( log )
>
> Formula: Inf_Leaves ~ Inf_YST + Trat + (1 | Age_months)
>
>  ???? Data: d3
>
>  ???????? AIC?????????? BIC logLik deviance df.resid
>
>  ????4121.5???? 4151.4 -2053.8???? 4107.5?????????? 517
>
> Scaled residuals:
>
> Min?????????? 1Q?? Median?????????? 3Q???????? Max
>
> -1.2776 -0.6703 -0.1486?? 0.3279?? 5.4019
>
> Random effects:
>
> Groups Name?????????????? Variance Std.Dev.
>
> Age_months (Intercept) 1.172?????? 1.083
>
> Number of obs: 524, groups:?? Age_months, 4
>
> Fixed effects:
>
> Estimate Std. Error z value Pr(>|z|)
>
> (Intercept)???????????????? 3.4859551 0.5492043???? 6.347 2.19e-10 ***
>
> Inf_YST???????????????????????? 0.0005702 0.0002864???? 1.991???? 0.0465 *
>
> TratC1-Insecticide -1.1081610 0.1012478 -10.945?? < 2e-16 ***
>
> TratC2-Control???????? -0.7859302 0.1058146?? -7.427 1.11e-13 ***
>
> TratC2-Insecticide -1.3833545 0.1041882 -13.277?? < 2e-16 ***
>
> ---
>
> Signif. codes:?? 0 ???***??? 0.001 ???**??? 0.01 ???*??? 0.05 ???.??? 0.1
> ??? ??? 1
>
> Correlation of Fixed Effects:
>
>  ?????????????????????? (Intr) In_YST TrC1-I TrC2-C
>
> Inf_YST???????? -0.122
>
> TrtC1-Insct -0.103 0.189
>
> TrtC2-Cntrl -0.104 0.265?? 0.436
>
> TrtC2-Insct -0.097 0.221?? 0.424?? 0.504
>
> convergence code: 0
>
> Model failed to converge with max|grad| = 0.00398879 (tol = 0.001,
> component 1)
>
> Model is nearly unidentifiable: very large eigenvalue
>
> - Rescale variables?
>
> Model is nearly unidentifiable: large eigenvalue ratio
>
> - Rescale variables?
>
>
> And my questions are:
>
> 1) Marginal R2 is a good metric for identify a bad fixed effect choose
> in my models B? Despite a better conditional R2 comparing of conditional
> R2 in my model A.
>
> 2) If I'm sure about my fixed and random effects, it is better a final
> model with high values in both R2 or I choose based in the high value in
> conditional R2?
>
>
> Thanks in advanced,
>
>
> Alexandre
>
>
> --
> ======================================================================
> Alexandre dos Santos
> Prote????o Florestal
> IFMT - Instituto Federal de Educa????o, Ci??ncia e Tecnologia de Mato
> Grosso
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>
>          alexandre.santos using 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
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> ======================================================================
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