[R-sig-ME] compare two GAMM4 models using AICs

Highland Statistics Ltd highstat at highstat.com
Wed Oct 25 19:59:21 CEST 2017


**
>
> Family: poisson
> Link function: log
>
> Formula:
> y ~ s(age) + offset(expy)
>
> Parametric coefficients:
>             Estimate Std. Error z value Pr(>|z|)
> (Intercept)  -7.3922     0.1065  -69.41  <2e-16 ***
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Approximate significance of smooth terms:
>             edf Ref.df    Chi.sq p-value
> s(age)   1      1         0.13   0.719
>
> R-sq.(adj) =  -8.44e-05
> glmer.ML = 2075.4  Scale est. = 1         n = 10523
>
> *summary(mg$mer)*
>
> Generalized linear mixed model fit by maximum likelihood (Laplace 
> Approximation) ['glmerMod']
>  Family: poisson  ( log )
>
>      AIC      BIC   logLik deviance df.resid
>   5294.9   5331.2  -2642.4   5284.9    10518
>
> Scaled residuals:
>     Min      1Q  Median      3Q     Max
> -1.8449 -0.0806 -0.0786 -0.0775  4.9384
>
> Random effects:
>  Groups  Name        Variance Std.Dev.
>  g  (Intercept) 2.868    1.694
>  s (Intercept) 3.820    1.954
>  Xr      s(age) 0.000    0.000
> Number of obs: 10523, groups:  g, 1785; s, 1768; Xr, 8
>
> Fixed effects:
>                 Estimate Std. Error z value Pr(>|z|)
> X(Intercept)    -7.39218    0.19834  -37.27  <2e-16 ***
> Xs(age)Fx1  0.03669    0.08418    0.44    0.663
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Correlation of Fixed Effects:
>             X(Int)
> Xs(age)F1 0.024
>
> *
> *
> *Is this the right way to interpret it:*
> *
> *
> *1) from the gam part *I would conclude that the spline for the 
> variable age has an estimated degree of freedom of 1, indicating that 
> I might not need a spline.
Indeed. And you won't need the covariate Age as a parametric term neither.


> *
> *
> *2) from the mer part *I would conclude that the linear part of 
> the variable age is not associated with my outcome, either.
There is no need to look at the smoother info in the mer part.
>
> Based on this model, I can safely assume that age can be included in 
> my regression model as a linear term, given there is nothing to 
> suggest the association with my outcome is non-parametrical.

Keep in mind that you are using a log link function, which means that 
the effect of age on your response variable is actually exponential 
(would it have been significant).


Alain

>
> I would then check the residuals, as you suggested, and if there is 
> not any pattern, I should be ok without including a spline then.
>
> Does this sound like a reasonable explanation for not including a 
> spline term in my regression model?
>
> Thank you so much. I apologize for these silly questions. I am just 
> trying to assimilate way too much at once and I second guess 
> everything I do since these are all difficult concepts to grasp for a 
> beginner.
>
> Best,
> DaniNM
>
> _
>
> _
> _
> ------------------------------------------------------------------------
> _
> _*From:* R-sig-mixed-models 
> <r-si_g-mixed-models-bounces at r-project.org> on behalf of Highland 
> Statistics Ltd <highstat at highstat.com>
> *Sent:* Tuesday, October 24, 2017 2:00 PM
> *To:* r-sig-mixed-models at r-project.org
> *Subject:* Re: [R-sig-ME] compare two GAMM4 models using AICs
>
>
> >> ----------------------------------------------------------------------
> >>
> >> Message: 1
> >> Date: Tue, 24 Oct 2017 19:49:50 +0000
> >> From: dani <orchidn at live.com>
> >> To: "r-sig-mixed-models at r-project.org"
> >>     <r-sig-mixed-models at r-project.org>
> >> Subject: [R-sig-ME] compare two GAMM4 models using AICs
> >> Message-ID:
> >> 
>     <MWHPR1201MB0029CE3D9C5EC1956640DB70D6470 at MWHPR1201MB0029.namprd12.prod.outlook.com>
> >>
> >>
> >> Content-Type: text/plain; charset="UTF-8"
> >>
> >> Hello everyone,
> >>
> >>
> >> I am fitting two gamm4 models because I would like to see whether
> >> there is justification for including a spline term for x1. Can this
> >> be done by comparing the AICs for the underlying mixed models (i.e.,
> >> the "mer" part) of the two models?
> >
> >
>
> Technically it won't crash..."so it can be done"..but I am not sure
> whether you want to do this. Internally, the smoother is written as a
> mixed model (X * b + Z * u)....and those random effects (which is part
> of the smoother) don't count towards the number of parameters.
>
> >> b1 <- gamm4(y~x1+offset(e),data=dat,random=~(1|fac))
> >
> >> b2 <- gamm4(y~x1+s(x1)+offset(e),data=dat,random=~(1|fac))
> >
> >
> >
>
>
> I am confused about your use of an offset in a Gaussian model, and I am
> confused why you would use x1 and s(x1) in the same model. The s(x1)
> already contains the linear part of the smoother.
>
> Why not fit the first model and inspect residuals for any patterns? If
> there are, then using a smoother is an option.
>
> Kind regards,
>
> Alain Zuur
>
>
> >
> >>
> >> summary(b1$gam)
> >>
> >> summary(b1$mer)
> >>
> >>
> >> summary(b2$gam)
> >>
> >> summary(b2$mer)
> >>
> >>
> >> AIC(b1$mer)
> >>
> >> AIC(b2$mer)
> >>
> >>
> >> Thank you very much!
> >>
> >> Best,
> >>
> >> DaniNM
> >>
> >> <http://aka.ms/weboutlook>
> >>
> >>     [[alternative HTML version deleted]]
> >>
> >>
> >
>
> -- 
>
> Dr. Alain F. Zuur
> Highland Statistics Ltd.
> 9 St Clair Wynd
> AB41 6DZ Newburgh, UK
> Email: highstat at highstat.com
> URL: www.highstat.com <http://www.highstat.com>
> Highland Statistics Ltd. <http://www.highstat.com/>
> www.highstat.com
> Statistical consultancy, data analysis and software development. 
> Specialized in time series analysis. Located in Scotland.
>
>
>
>
> And:
> NIOZ Royal Netherlands Institute for Sea Research,
> Department of Coastal Systems, and Utrecht University,
> P.O. Box 59, 1790 AB Den Burg,
> Texel, The Netherlands
>
>
>
> Author of:
> 1. Beginner's Guide to Spatial, Temporal and Spatial-Temporal 
> Ecological Data Analysis with R-INLA. (2017).
> 2. Beginner's Guide to Zero-Inflated Models with R (2016).
> 3. Beginner's Guide to Data Exploration and Visualisation with R (2015).
> 4. Beginner's Guide to GAMM with R (2014).
> 5. Beginner's Guide to GLM and GLMM with R (2013).
> 6. Beginner's Guide to GAM with R (2012).
> 7. Zero Inflated Models and GLMM with R (2012).
> 8. A Beginner's Guide to R (2009).
> 9. Mixed effects models and extensions in ecology with R (2009).
> 10. Analysing Ecological Data (2007).
>
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-- 

Dr. Alain F. Zuur
Highland Statistics Ltd.
9 St Clair Wynd
AB41 6DZ Newburgh, UK
Email: highstat at highstat.com
URL:   www.highstat.com

And:
NIOZ Royal Netherlands Institute for Sea Research,
Department of Coastal Systems, and Utrecht University,
P.O. Box 59, 1790 AB Den Burg,
Texel, The Netherlands



Author of:
1. Beginner's Guide to Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA. (2017).
2. Beginner's Guide to Zero-Inflated Models with R (2016).
3. Beginner's Guide to Data Exploration and Visualisation with R (2015).
4. Beginner's Guide to GAMM with R (2014).
5. Beginner's Guide to GLM and GLMM with R (2013).
6. Beginner's Guide to GAM with R (2012).
7. Zero Inflated Models and GLMM with R (2012).
8. A Beginner's Guide to R (2009).
9. Mixed effects models and extensions in ecology with R (2009).
10. Analysing Ecological Data (2007).


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