proflik {geoR} | R Documentation

## Computes Profile Likelihoods

### Description

Computes profile likelihoods for model parameters previously estimated using the function `likfit`.

### Usage

    proflik(obj.likfit, geodata, coords = geodata$coords,
            data = geodata$data, sill.values, range.values,
            nugget.values, nugget.rel.values, lambda.values,
            sillrange.values = TRUE, sillnugget.values = TRUE,
            rangenugget.values = TRUE, sillnugget.rel.values = FALSE,
            rangenugget.rel.values = FALSE, silllambda.values = FALSE,
            rangelambda.values = TRUE,  nuggetlambda.values = FALSE,
            nugget.rellambda.values = FALSE,
            uni.only = TRUE, bi.only = FALSE, messages, ...)

### Arguments

`obj.likfit` | an object of the class `likfit`, typically an output of the function `likfit`.
`geodata` | a list containing elements `coords` and `data` described next. Typically an object of the class `"geodata"` - a geoR data-set. If not provided the arguments `coords` and `data` must be provided instead.
`coords` | an n x 2 matrix containing in each row Euclidean coordinates of the n data locations. By default it takes the element `coords` of the argument `geodata`.
`data` | a vector with data values. By default it takes the element `data` of the argument `geodata`.
`sill.values` | set of values of the partial sill parameter sigma^2 for which the profile likelihood will be computed.
`range.values` | set of values of the range parameter phi for which the profile likelihood will be computed.
`nugget.values` | set of values of the nugget parameter tau^2 for which the profile likelihood will be computed. Only used if the model was fitted using the function `likfit` with the option `fix.nugget = FALSE`.
`nugget.rel.values` | set of values of the relative nugget parameter tauR^2 for which the profile likelihood will be computed. Only used if the model was fitted using the function `likfit` with the option `fix.nugget = FALSE`.
`lambda.values` | set of values of the Box-Cox transformation parameter lambda for which the profile likelihood will be computed. Only to be used if the model was fitted using the function `likfit` with the option `fix.lambda = FALSE`.
`sillrange.values` | logical indicating whether or not the 2-D profile likelihood should be computed. Only valid if `uni.only = FALSE`.
`sillnugget.values` | as above.
`rangenugget.values` | as above.
`sillnugget.rel.values` | as above.
`rangenugget.rel.values` | as above.
`silllambda.values` | as above.
`rangelambda.values` | as above.
`nuggetlambda.values` | as above.
`nugget.rellambda.values` | as above.
`uni.only` | as above.
`bi.only` | as above.
`messages` | logical. Indicates whether status messages should be printed on the screen (i.e. current output device) while the function is running.
`...` | additional parameters to be passed to the minimization function.

### Details

The functions `.proflik.*` are auxiliary functions used to compute the profile likelihoods. These functions are internally called by the minimization functions when estimating the model parameters.

### Value

An object of the class `"proflik"` which is a list. Each element contains values of a parameter (or a pair of parameters for 2-D profiles) and the corresponding value of the profile likelihood. The components of the output will vary according to the input options.

### Note

1. Profile likelihoods for Gaussian Random Fields are usually uni-modal. Unusual or jagged shapes can be due to the lack of the convergence in the numerical minimization for particular values of the parameter(s). If this is the case it might be necessary to pass `control` arguments to the minimization functions using the argument .... It's also advisable to try the different options for the `minimisation.function` argument. See documentation of the functions `optim` and/or `nlm` for further details.
2. 2-D profiles can be computed by setting the argument `uni.only = FALSE`. However, before computing 2-D profiles be sure they are really necessary. Their computation can be time demanding since it is performed on a grid determined by the cross-product of the values defining the 1-D profiles.
3. There is no "default strategy" to find reasonable values for the x-axis. They must be found in a "try-and-error" exercise. It's recommended to use short sequences in the initial attempts. The `EXAMPLE` section below illustrates this.

### Author(s)

Paulo Justiniano Ribeiro Jr. paulojus@leg.ufpr.br,
Peter J. Diggle p.diggle@lancaster.ac.uk.

### References

Further information on the package geoR can be found at:
http://www.leg.ufpr.br/geoR.

### See Also

`plot.proflik` for graphical output, `likfit` for the parameter estimation, `optim` and `nlm` for further details about the minimization functions.

### Examples

    op <- par(no.readonly=TRUE)
    ml <- likfit(s100, ini=c(.5, .5), fix.nug=TRUE)
    ## a first atempt to find reasonable values for the x-axis:
    prof <- proflik(ml, s100, sill.values=seq(0.5, 1.5, l=4),
                    range.val=seq(0.1, .5, l=4))
    par(mfrow=c(1,2))
    plot(prof)
    ## a nicer setting
    ## Not run:
    prof <- proflik(ml, s100, sill.values=seq(0.45, 2, l=11),
                    range.val=seq(0.1, .55, l=11))
    plot(prof)
    ## to include 2-D profiles use:
    ## (commented because this is time demanding)
    #prof <- proflik(ml, s100, sill.values=seq(0.45, 2, l=11),
    #                range.val=seq(0.1, .55, l=11), uni.only=FALSE)
    #par(mfrow=c(2,2))
    #plot(prof, nlevels=16)
    ## End(Not run)
    par(op)

[Package geoR version 1.6-19 Index]