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.

*
*
*2) from the mer part *I would conclude that the linear part of 
the?variable age is not associated with my outcome, either.

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.

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).