[R-sig-Geo] retrieving fitted values from geoR model

[Ken Nussear]

Just in case anyone needs this in the future

Looks like I am able to get them using this call

fitted.likGRF(m4r)

Ken
>
>
> Message: 2
> Date: Tue, 6 Nov 2007 16:52:05 -0800
> From: Ken Nussear <knussear at usgs.gov>
> Subject: [R-sig-Geo] retrieving fitted values from geoR model
> To: r-sig-geo at stat.math.ethz.ch
> Message-ID: <AAC0BCC3-D58E-4082-AFD1-3FBEC440ED8E at usgs.gov>
> Content-Type: text/plain; charset=US-ASCII; format=flowed; delsp=yes
>
> I'm trying to obtain the fitted values for a spatial model using
> likfit under geoR. When I use the statement
>
> fitted(m4r, spatial = TRUE), per the instructions under help(likfit)
>
> I get the following error
>
>> fitted(m4r, spatial = TRUE)
> be patient ... this function currently require calling likfit again
> Error in eval(object.call) : object "object.call" not found
>
> If I ask the object for the call I get
>
>> m4r$call
> likfit(geodata = spUta, trend = ~TransectLength + all.road +
>     Urban, ini.cov.pars = c(0.82, 1949), nugget = 0.55, cov.model =
> "exponential",
>     method.lik = "REML")
>
>
>
> Can anyone provide help?
>
> Thanks
>
> Ken
>
>
>
> The model summary is:
>
>> summary(m4r)
> Summary of the parameter estimation
> -----------------------------------
> Estimation method: restricted maximum likelihood
>
> Parameters of the mean component (trend):
>   beta0   beta1   beta2   beta3
>  1.1742  0.0008 -0.3758 -0.0001
>
> Parameters of the spatial component:
>    correlation function: exponential
>       (estimated) variance parameter sigmasq (partial sill) =  1.167
>       (estimated) cor. fct. parameter phi (range parameter)  =  1949
>    anisotropy parameters:
>       (fixed) anisotropy angle = 0  ( 0 degrees )
>       (fixed) anisotropy ratio = 1
>
> Parameter of the error component:
>       (estimated) nugget =  0.2518
>
> Transformation parameter:
>       (fixed) Box-Cox parameter = 1 (no transformation)
>
> Maximised Likelihood:
>    log.L n.params      AIC      BIC
> "-75.92"      "7"  "165.8"  "180.3"
>
> non spatial model:
>    log.L n.params      AIC      BIC
> "-81.95"      "5"  "173.9"  "184.2"
>
> Call:
> likfit(geodata = spUta, trend = ~TransectLength + all.road +
>     Urban, ini.cov.pars = c(0.82, 1949), nugget = 0.55, cov.model =
> "exponential",
>     method.lik = "REML")