# [R] not understanding geoR "nugget" output

Paulo Justiniano Ribeiro Jr paulojus at est.ufpr.br
Wed Sep 29 03:32:42 CEST 2004

```Melaine

When estimated phi=0 or sigmasq=0 the odel is a "pure nugget effect" and
you cannot distinguish between sigmasq and tausq
Therefore it is a convention in geoR to assign the estimated varioance to
tausq.

Regarding R^2:
forgaussian models you can compute this values using the maximised
likelihood and othe model information

Alternatively you can use likfit with the argument components=T.
This will return the estimated model components frwom which you can
compute R^2.

Since this is a package specific question feel free to contact me directly
if you have any further queries
best
P.J.

On Tue, 28 Sep 2004, Melanie A. Link-Perez wrote:

> I am having difficulty understanding the output from a likfit call,
> specifically the output for the nugget.  When the partial sill is non-zero,
> the estimated nugget that is returned is zero.  When the partial sill is zero,
> I get a non-zero nugget.  The following output may be helpful:
>
> Estimation method: maximum likelihood
>
> Parameters of the mean component (trend):
>   beta0   beta1   beta2   beta3   beta4   beta5
>  2.4299  2.5095  4.8184 -0.0084 -0.0625 -0.0057
>
> Parameters of the spatial component:
>    correlation function: spherical
>       (estimated) variance parameter sigmasq (partial sill) =  1694
>       (estimated) cor. fct. parameter phi (range parameter)  =  32.1
>    anisotropy parameters:
>       (fixed) anisotropy angle = 0  ( 0 degrees )
>       (fixed) anisotropy ratio = 1
>
> Parameter of the error component:
>       (estimated) nugget =  0
>
> Transformation parameter:
>       (fixed) Box-Cox parameter = 1 (no transformation)
>
> Maximised Likelihood:
>    log.L n.params      AIC      BIC
> "-98.92"      "9"  "215.8"  "224.8"
>
> non spatial model:
>    log.L n.params      AIC      BIC
> "-101.5"      "8"  "219.0"  "226.9"
>
> Call:
> likfit(geodata = geodataK, trend = "2nd", ini.cov.pars = c(1700,
>     50), cov.model = "sph", method.lik = "ML")
>
>
> ---
> This is the code I used:
>
> geodataK <- as.geodata(data[,c(2,3,11)], coords.col=1:2, data.col=3)
> geodataK
> bin4 <- variog(geodataK, uvec=seq(0,163.44,l=21), max.dist=50,
> estimator.type="modulus", trend="2nd"); plot(bin4, main = "(f) Potassium",
> xlab = "", ylab = "")
> mod1 <- likfit(cov.model="sph",geodataK, trend="2nd",ini=c(1700,50),
> method="ML");summary(mod1)
> lines(mod1, lty=1)
>
> ---
>
> I am also trying to figure out how to calculate R^2 values for the likfit
> models that I fit to the semivariogram.
>
> I am using R version 1.9.1 (rw1091) and geoR version  1.4-8 on a PC running MS
> Windows XP Professional version 2002.
>
>
> Many thanks,
> Miami University
>
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>
>

Paulo Justiniano Ribeiro Jr
Departamento de EstatÃ­stica
Universidade Federal do ParanÃ¡
Caixa Postal 19.081
CEP 81.531-990
Curitiba, PR  -  Brasil
Tel: (+55) 41 361 3573
Fax: (+55) 41 361 3141
e-mail: paulojus at est.ufpr.br
http://www.est.ufpr.br/~paulojus

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