[R-sig-ME] spatial correlation with nlme (Javier Moreira)

[Javier Moreira]

Thanks, i try again in this group because, until yesterday i didnt know
that exist.


El 2 ago. 2017 10:52 a. m., "Highland Statistics Ltd" <highstat at highstat.com>
escribió:

>
> ----------------------------------------------------------------------
>>
>> Message: 1
>> Date: Tue, 1 Aug 2017 22:43:30 -0300
>> From: Javier Moreira <javiermoreira at gmail.com>
>> To: r-sig-mixed-models at r-project.org
>> Subject: [R-sig-ME] spatial correlation with nlme
>> Message-ID:
>>         <CAEyHP-0=TXAbK-fpFH=otmJ0Tzkk_yJ_86K6-8L5MbxtbLQovw at mail.gm
>> ail.com>
>> Content-Type: text/plain; charset="UTF-8"
>>
>> hi,
>> im trying to generate different models to account for spatial correlation.
>> Im using nlme package, and mixed models, in order to compare two models,
>> one that doesnt include the spatial correlation and one that does.
>>
>
> As already indicated by Thierry...try R-INLA.
>
> Alain
>
>
> Its a nested design, one that has 4 leves,
>> BLOQUE/ AMBIENTE/ TRATAMIENTO/ SUBMUESTREO
>> Its a harvest set data, with multiple point of data/ treatment, so the
>> last
>> level account for another term in the error for "sub-muestreo".
>>
>> My first problem its, when i try to add de correlation term to the model,
>> i
>> cant, when the random effects are taken to the level /SUBMUESTREO, and i
>> have to leave it to the level of TRATAMIENTO.
>> When i do that, i have 2 differences between models, the term accounting
>> for sub-muestreo, and the spatial correlation.
>>
>> #MODELO 2##
>> attach(base_modelo3)
>> str(base_modelo3)
>> data1=base_modelo3
>> str(data1)
>> data1=groupedData(REND.~1|BLOQUE/AMBIENTE/TRATAMIENTO/SUBMUESTREO,
>> data=data1, units=list(y="(ton/ha)"))
>> data1$TRATAMIENTO =factor(data1$TRATAMIENTO)
>> data1$BLOQUE =factor(data1$BLOQUE)
>> data1$AMBIENTE =factor(data1$AMBIENTE)
>>
>> modelo2_MM<-lme(REND.~1+TRATAMIENTO*AMBIENTE,
>>                  random=~1|BLOQUE/AMBIENTE/TRATAMIENTO/SUBMUESTREO,
>>                  weights=varComb(varIdent(form=~1|TRATAMIENTO)),
>>                  data=data1,
>>                  control=lmeControl(niterEM=150,msMaxIter=200))
>> summary(modelo2_MM)
>> anova(modelo2_MM)
>>
>> ##MODELO 4##
>>
>> modelo4_MM<-lme(REND.~1+TRATAMIENTO*AMBIENTE,
>>                  random=~1|BLOQUE/AMBIENTE/TRATAMIENTO,
>>                  weights=varComb(varIdent(form=~1|TRATAMIENTO)),
>>                  correlation=corExp(form=~X+Y,nugget=T),
>>                  data=data1,
>>                  control=lmeControl(niterEM=150,msMaxIter=200))
>> summary(modelo4_MM)
>> anova(modelo4_MM)
>>
>>
> --
>
> Dr. Alain F. Zuur
>
>
>
> 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).
>
> Highland Statistics Ltd.
> 9 St Clair Wynd
> UK - AB41 6DZ Newburgh
> Tel:   0044 1358 788177
> Email: highstat at highstat.com
> URL:   www.highstat.com
>
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