retrieving fitted values from geoR model

knussear

Wed, Nov 7, 2007 4:39 AM

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")