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.
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)
Sorry, the error is
Error in corFactor.corSpatial(object) :
NA/NaN/Inf in foreign function call (arg 1)
In addition: Warning messages:
1: In min(unlist(attr(object, "covariate"))) :
no non-missing arguments to min; returning Inf
2: In min(unlist(attr(object, "covariate"))) :
no non-missing arguments to min; returning Inf
?
My second problem is, that i need to get the specific parameter for the
error term that belongs to the spatial correlation, in order to map it. For
what i watch, what lme does is send it to the general error, and so, what i
could do is make the differences between the residuals of these two models.
so, its essetial to get the exact same model, except for the correlation
structure.
If anybody knows how to get the specific term of error accounting for the
correlation, it would be wonderful.
E24=residuals(modelo24_3,type = "response")
E40=residuals(modelo4_MM,type = "response")
EE=E24-E40
post=data.frame(E24,E40,EE,data1$X,data1$Y)
coordinates(post)<-c("data1.X","data1.Y")
coor_post<-coordinates(post)
bubble(post,"E24",main="residuos modelo 2")
bubble(post,"E40",main="residuos modelo 4")
bubble(post,"EE",main="Est.espacial removida por Modelo 4")
here its the data set
?
base_modelo3.csv
<https://drive.google.com/file/d/0B6YImu-ZATe4bEFKTlAxci1PNW8/view?usp=
drive_web>
?
thanks!
--
Javier Moreira de Souza
Ingeniero Agr?nomo
099 406 006
[[alternative HTML version deleted]]