[R-sig-ME] question about a GAM model (dani)
dani
orchidn @ending from live@com
Wed Aug 29 21:42:26 CEST 2018
Hello Dr. Zuur,
Thank you so much for your message!
I am only using this model for educational purposes, I am just playing with a dataset of 500 observations. Variables x1 and x2 are covariates and they are both displaying non-parametric associations with the outcome. The x3 variable is the variable of interest.
I noticed the value of 1 for edfs for the covariate and for the variable of interest so I asked myself if I should not remove the parametric term and re-run the model is situations like these.
If this happens when I conduct an analysis for a study, do I present such results or I re-run the model without smoothers on x2 and x3, even though in bivariate associations with the outcome, x2 and x3 showed non-parametric associations.
Thank you so much for your suggestions, I will definitely look at the two books again, they are always useful!
Best,
Dani
<http://aka.ms/weboutlook>
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Sent: Wednesday, August 29, 2018 12:30 PM
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Subject: Re: [R-sig-ME] question about a GAM model (dani)
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Message: 4
Date: Wed, 29 Aug 2018 17:38:44 +0000
From: dani <orchidn using live.com>
To: Ben Bolker <bbolker using gmail.com>, "r-sig-mixed-models using r-project.org"
<r-sig-mixed-models using r-project.org>
Subject: Re: [R-sig-ME] question about a GAM model
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Thank you very much Udita and Dr. Bolker for your responses.
It is still not clear to me how should I proceed. Would anyone else be able help with this issue,?
Dani,
GAMs are useful if you use them with care, but confusing if you just apply them because someone else is doing it as well.
Perhaps you should first ask yourself the question why you are applying a GAM. Then focus on the question
whether the output makes sense.
Based on your output it seems that nothing is important (not even as parametric terms). But I am not familiar with your
data; things like collinearity can mess up the shape of smoothers. And you don't mention the size of your data set neither.
I suggest that you have a go at Wood (2017), or if I may be bold enough to self-cite....try our Beginner's Guide to GAM (2012).
Kind regards,
Alain Zuur
Best regards,
Dani
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From: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> on behalf of Ben Bolker <bbolker using gmail.com>
Sent: Tuesday, August 28, 2018 6:19 AM
To: r-sig-mixed-models using r-project.org
Subject: Re: [R-sig-ME] question about a GAM model
Don't forget to run k.check() on your model to see if you specified a
large enough basis dimension to start with ...
On 2018-08-28 05:51 AM, Bansal, Udita wrote:
> Hi Dani,
>
> I don�t know much about GAM but I know you can look at the plots for fitted model results to check if there is any curvature. You can use the following code:
>
> par(mfrow = c(1,3))
> plot(GAMmodel)
>
> Bests
> Udita
>
> On 28/08/18, 1:58 AM, "R-sig-mixed-models on behalf of dani" <r-sig-mixed-models-bounces using r-project.org on behalf of orchidn using live.com> wrote:
>
> Hi everyone,
>
>
> I have a question about a GAM model where I included three non-parametric terms. I obtained the results below. can I conclude that the associations were in fact linear and run a final GLM model without including splines? To me it seems unnecessary to include splines in the final model. How should I report these results?
>
>
> # Approximate significance of smooth terms:
> # edf Ref.df Chi.sq p-value
> # s(x1) 1.61 2.01 1.17 0.550
> # s(x2) 1.00 1.00 0.00 0.955
> # s(x3) 1.00 1.00 4.61 0.032 *
>
> Thank you very much,
> Dani
>
>
>
>
>
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>
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End of R-sig-mixed-models Digest, Vol 140, Issue 34
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--
Dr. Alain F. Zuur
Highland Statistics Ltd.
9 St Clair Wynd
AB41 6DZ Newburgh, UK
Email: highstat using 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).
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