[R-sig-ME] account for spatial autocorrelation in MCMCglmm modeling
Jarrod Hadfield
j.hadfield at ed.ac.uk
Sun Jun 5 10:37:33 CEST 2011
Hi,
Fitting lon*lat in the random argument will not deal with
autocorrelation per se. A set of factors will be created for each
unique combination of lon/lat and any similarity of plants in these
'grid squares' will be accounted for. Similarity of plants in
neighbouring 'grid squares' will not be modelled. If the lat/long
combinations are unique to plants then the effects are confounded with
MCMCglmm has no functionality for spatial autocorrelation structures,
although it seems the sir function can be used to fit some types of
spatial models, as indicated in the thread you cite. I implemented
sir models for an entirely different purpose so I am not very familiar
with their application in spatial modelling. Malcolm may have better
advice, and also recommended Bivand (2009) "Fitting and interpreting
spatial regression models: an applied survey", which can be found
on-line and is an accessible read.
Cheers,
Jarrod
Quoting Mao Jianfeng <jianfeng.mao at gmail.com> on Sun, 5 Jun 2011
09:51:53 +0200:
> Dear all,
>
> I would like to hear your advice/directions on how to account to spatial
> autocorrelation in MCMCglmm modeling.
>
> I have found a really good reference on that in a former R-sig-mixed thread:
> (Another MCMCglmm question - fixing correlations)
> https://stat.ethz.ch/pipermail/r-sig-mixed-models/2010q4/004940.html
> But, I still can not implement it in my real study.
>
> I am now asking a question of how additive genetic effect, population
> structure, and environment can affect the phenotypic variations, by the data
> measured/observed in >50 wild pine populations. You know, pine is plant, not
> animal, they can not move by themselves. There should be some variance come
> from geological autocorrelation (individuals are more near in geological
> scale, they expressed more similar phenotype).
>
> In the first run, I just expect the autocorrelation defined by longitude and
> latitude. And, it may extent to be defined by longitude, latitude and
> altitude. I do not know how to fix a MCMCglmm model accounting for such a
> autocorrelation. Can it just be fixed as random effect of interaction
> between longitude and latitude. Or is it necessary to use sir() function?
>
> Could you please give any directions? I think this question may have general
> interests for those who want to asking ecological/genetic question using
> data from field.
>
> Thanks in advance.
>
> Best regards,
> Jian-Feng,
>
>
> Please see my problem in more detail as followings:
>
> ########################
> # my data (in dataframe, nypd.data),
> response - conlen (cone length)
> predictor - popcode (population code)
> tree (tree code, there are 5 replicates for each tree)
> Ped (pedigree, now I have not prepared it)
>
> ########################
> # the model without autocorrelation
> prior1.1 <- list(G = list(G1 = list(V = 1,n=0.002),
> G2=list(V=1, n=0.002)
> #G3=list(V= matrix(con.var/4),n= 1)
> #G4 = list(V = 1, n=0.002)
> ),
> R=list(V=1,n= 0.002))
> cone.model1.1<-MCMCglmm(fix=conlen ~ 1,
> random = ~ popcode + tree,
> #pedigree = Ped,
> data = nypd.data,
> nitt=53000000,
> thin=50000,
> burnin=13000000,
> prior = prior1.1, verbose = FALSE
> )
>
> ########################
> # the model with autocorrelation be defined as random effect
> prior1.2 <- (I am not clear how to specific this prior)
>
> cone.model1.2<-MCMCglmm(fix=conlen ~ 1,
> random = ~ popcode + tree + lon*lat,
> #pedigree = Ped,
> data = nypd.data,
> nitt=53000000,
> thin=50000,
> burnin=13000000,
> prior = prior1.4, verbose = FALSE
> )
>
> ########################
> # the model with autocorrelation be defined by sir()
>
> # I got error for this model:
>
> Error in sir(~W, ~units) :
> sir formulae invalid: factor levels of intersecting variables have
> to be the same
>
> ### the model
> W <- as.matrix(cbind(nypd.data$lon, nypd.data$lat))
>
> prior1.9 <- list(G = list(G1 = list(V = 1,n=0.002),
> G2=list(V=1, n=0.002)
> #G3=list(V= matrix(con.var/4),n= 1)
> #G4 = list(V = 1, n=0.002)
> ),
> R=list(V=1,n= 0.002))
> cone.model1.9<-MCMCglmm(fix=conlen ~ 1 + sir(~W, ~units),
> random = ~ popcode + tree,
> #pedigree = Ped,
> data = nypd.data,
> nitt=53000000,
> thin=50000,
> burnin=13000000,
> prior = prior1.9, verbose = FALSE
> )
>
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>
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>
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