compare two GAMM4 models using AICs
Hello Dr. Zuur, Thank you so much, this is so helpful! Best, Dani NM
From: Highland Statistics Ltd <highstat at highstat.com>
Sent: Wednesday, October 25, 2017 10:59 AM
To: dani; r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] compare two GAMM4 models using AICs
Sent: Wednesday, October 25, 2017 10:59 AM
To: dani; r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] compare two GAMM4 models using AICs
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-sig-mixed-models-bounces at r-project.org<mailto:g-mixed-models-bounces at r-project.org>> on behalf of Highland Statistics Ltd <highstat at highstat.com><mailto:highstat at highstat.com>
Sent: Tuesday, October 24, 2017 2:00 PM
To: r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] compare two GAMM4 models using AICs
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>>
>> 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
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Highland Statistics Ltd.<http://www.highstat.com/>
www.highstat.com<http://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,
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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<mailto:highstat at highstat.com>
URL: www.highstat.com<http://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).