Maximum likelihood (ML) or restricted maximum likelihood (REML) parameter estimation for (transformed) Gaussian random fields.
likfit(geodata, coords = geodata$coords, data = geodata$data,
trend = "cte", ini.cov.pars, fix.nugget = FALSE, nugget = 0,
fix.kappa = TRUE, kappa = 0.5, fix.lambda = TRUE, lambda = 1,
fix.psiA = TRUE, psiA = 0, fix.psiR = TRUE, psiR = 1,
cov.model, realisations, lik.method = "ML", components = TRUE,
nospatial = TRUE, limits = pars.limits(),
print.pars = FALSE, messages, …)# S3 method for likGRF
fitted(object, spatial = TRUE, …)
# S3 method for likGRF
resid(object, spatial = FALSE, …)
a list containing elements coords and
data as described next.
Typically an object of the class "geodata".
If not provided the arguments
coords and data must be provided instead.
an \(n \times 2\) matrix where each row has the 2-D
coordinates of the \(n\) data locations.
By default it takes the
component coords of the argument geodata, if provided.
a vector with n data values. By default it takes the
component data of the argument geodata, if provided.
specifies the mean part of the model. See documentation
of trend.spatial for further details.
Defaults to "cte".
initial values for the covariance parameters:
\(\sigma^2\) (partial sill) and \(\phi\) (range
parameter). Typically a vector with two components. However a
matrix can be used to provide several initial values. See
DETAILS below.
logical, indicating whether the parameter
\(\tau^2\) (nugget variance) should be regarded as fixed
(fix.nugget = TRUE) or should be
estimated (fix.nugget = FALSE). Defaults to
FALSE.
value of the nugget parameter.
Regarded as a fixed value if fix.nugget = TRUE otherwise
as the initial value for the minimisation algorithm. Defaults to zero.
logical, indicating whether the extra parameter
\(\kappa\) should be regarded as fixed
(fix.kappa = TRUE) or should be
estimated (fix.kappa = FALSE). Defaults to TRUE.
value of the extra parameter \(\kappa\).
Regarded as a fixed value if fix.kappa = TRUE
otherwise as the initial value for the minimisation algorithm. Defaults to
\(0.5\). This parameter is valid only if the covariance function is one
of: "matern", "powered.exponential", "cauchy" or
"gneiting.matern". For more details on covariance functions
see documentation for cov.spatial.
logical, indicating whether the Box-Cox transformation parameter
\(\lambda\) should be regarded as fixed
(fix.lambda = TRUE) or should be
be estimated (fix.lambda = FALSE). Defaults to TRUE.
value of the Box-Cox transformation parameter
\(\lambda\).
Regarded as a fixed value if fix.lambda = TRUE otherwise
as the initial value for the minimisation algorithm. Defaults to
\(1\). Two particular cases are \(\lambda = 1\)
indicating no transformation and \(\lambda = 0\) indicating log-transformation.
logical, indicating whether the anisotropy angle parameter
\(\psi_R\) should be regarded as fixed
(fix.psiA = TRUE) or should
be estimated (fix.psiA = FALSE). Defaults to
TRUE.
value (in radians) for the anisotropy angle parameter
\(\psi_A\).
Regarded as a fixed value if fix.psiA = TRUE
otherwise as the initial value for the minimisation algorithm.
Defaults to \(0\). See coords.aniso for further
details on anisotropy correction.
logical, indicating whether the anisotropy ratio parameter
\(\psi_R\) should be regarded as fixed
(fix.psiR = TRUE) or should be estimated (fix.psiR = FALSE). Defaults to
TRUE.
value, always greater than 1, for the anisotropy ratio parameter
\(\psi_R\).
Regarded as a fixed value if fix.psiR = TRUE
otherwise as the initial value for the minimisation algorithm.
Defaults to \(1\). See coords.aniso for further
details on anisotropy correction.
a string specifying the model for the correlation
function. For further details see documentation for cov.spatial.
Reads values from an variomodel object passed to ini.cov.pars if any, otherwise
defaults to the exponential model.
optional. Logical or a vector indicating the number of replication
for each datum. For further information see DETAILS below and
documentation for as.geodata.
(formely method.lik) options are "ML" for maximum likelihood and "REML" for
restricted maximum likelihood. Defaults to "ML".
an \(n \times 3\) data-frame with fitted
values for the three model components: trend, spatial and residuals.
See the section DETAILS below for the model specification.
logical. If TRUE parameter estimates for the
model without spatial component are included in the output.
values defining lower and upper limits for the model
parameters used in the numerical minimisation.
The auxiliary function pars.limits is called to set
the limits.
See also Limits in DETAILS below.
logical. If TRUE the parameters and the value
of the negative log-likelihood (up to a constant) are printed each
time the function to be minimised is called.
logical. Indicates whether status messages should be printed on the screen (or output device) while the function is running.
additional parameters to be passed to the minimisation
function. Typically arguments of the type control() which controls the
behavior of the minimisation algorithm. For further details see documentation
for the minimisation function optim.
an object with output of the function likfit.
logical, determines whether the spatial component of the model in included in the output. The geostatistical model components are: trend, spatial and residulas. See DETAILS.
An object of the classes "likGRF" and "variomodel".
The function summary.likGRF is used to print a summary
of the fitted model.
The object is a list with the following components:
a string with the name of the correlation function.
value of the nugget parameter \(\tau^2\).
This is an estimate if fix.nugget = FALSE otherwise, a fixed
value.
a vector with the estimates of the parameters \(\sigma^2\) and \(\phi\), respectively.
value of the smoothness parameter. Valid only if
the correlation function is one of: "matern",
"powered.exponential", "cauchy"
or "gneiting.matern".
estimate of mean parameter \(\beta\). This can be a scalar or vector depending on the trend (covariates) specified in the model.
estimated variance (or covariance matrix) for the mean parameter \(\beta\).
values of the Box-Cox transformation parameter. A fixed value if
fix.lambda = TRUE otherwise the estimate value.
fixed values or estimates of the anisotropy parameters, according to the function call.
estimation method used, "ML" (maximum likelihood)
or "REML" (restricted maximum likelihood).
the value of the maximized likelihood.
number of estimated parameters.
value of the Akaike Information Criteria, \(AIC=-2 ln(L) + 2 p\) where \(L\) is the maximised likelihood and p is the number of parameters in the model.
value of the Bayesian Information Criteria, \(BIC=-2ln(L) + p log(n)\), where \(n\) is the number of data, \(L,p\) as for AIC above.
a data-frame with all model parameters, their status (estimated or fixed) and values.
results returned by the minimisation function.
maximum distance between 2 data points. This
information relevant for other functions which use outputs from
likfit.
the trend (covariates) matrix \(X\).
numerical value of the logarithm of the Jacobian of the transformation.
estimates for the model without the spatial component.
the function call.
This function estimate the parameters of the Gaussian random field model, specified as: $$Y(x) = \mu(x) + S(x) + e$$ where
\(x\) defines a spatial location. Typically Euclidean coordinates on a plane.
\(Y\) is the variable been observed.
\(\mu(x) = X\beta\) is the mean component of the model (trend).
\(S(x)\) is a stationary Gaussian process with variance \(\sigma^2\) (partial sill) and a correlation function parametrized in its simplest form by \(\phi\) (the range parameter). Possible extra parameters for the correlation function are the smoothness parameter \(\kappa\) and the anisotropy parameters \(\phi_R\) and \(\phi_A\) (anisotropy ratio and angle, respectively).
\(e\) is the error term with variance parameter \(\tau^2\) (nugget variance).
The additional parameter \(\lambda\) allows for the Box-Cox transformation of the response variable. If used (i.e. if \(\lambda \neq 1\)) \(Y(x)\) above is replaced by \(g(Y(x))\) such that $$g(Y(x)) = \frac{Y^\lambda(x) - 1}{\lambda}.$$
Two particular cases are \(\lambda = 1\) which indicates no transformation and \(\lambda = 0\) indicating the log-transformation.
Numerical minimization
In general parameter estimation is performed numerically using the R
function optim to minimise the
negative log-likelihood computed by the function negloglik.GRF.
If the nugget, anisotropy (\(\psi_A, \psi_R\)),
smoothness (\(\kappa\)) and transformation (\(\lambda\)) parameters
are held fixed then the numerical minimisation can be reduced to
one-dimension and the function optimize is used instead
of optim. In this case initial values are irrelevant.
Limits
Lower and upper limits for parameter values can be
individually specified using the function link{pars.limits}.
For example, including the following in the function call:
limits = pars.limits(phi=c(0, 10), lambda=c(-2.5, 2.5)),
will change the limits for the parameters \(\phi\) and \(\lambda\).
Default values are used if the argument limits is not provided.
There are internal reparametrisation depending on the options for
parameters to be estimated.
For instance for the common situation when fix.nugget=FALSE the
minimisation is performed in a reduced
parameter space using
\(\tau^2_{rel} = \frac{\tau^2}{\sigma^2}\).
In this case values of \(\sigma^2\) and \(\beta\)
are then given by
analytical expressions which are function of the two parameters
remaining parameters and limits for these two parameters will be ignored.
Since parameter values are found by numerical optimization using
the function optim,
in given circunstances the algorithm may not converge to correct
parameter values when called with default options and the user may
need to pass extra options for the optimizer. For instance the
function optim takes a control argument.
The user should try different initial values and if the parameters have
different orders of magnitude may need to use options to scale the parameters.
Some possible workarounds in case of problems include:
rescale you data values (dividing by a constant, say)
rescale your coordinates (subtracting values and/or dividing by constants)
Use the mechanism to pass control() options for the
optimiser internally
Transformation
If the fix.lambda = FALSE and nospatial = FALSE the
Box-Cox parameter for the model without the spatial component is
obtained numerically, with log-likelihood computed by the function
boxcox.ns.
Multiple initial values can be specified providing a \(n
\time 2\) matrix for the argument ini.cov.pars and/or
providing a vector for the values of the remaining model parameters.
In this case the log-likelihood is computed for all combinations of
the model parameters. The parameter set which maximises the
value of the log-likelihood is then used to start the
minimisation algorithm.
Alternatively the argument ini.cov.pars can take an object of
the class eyefit or variomodel. This allows the usage
of an output of the functions eyefit, variofit or
likfit be used as initial value.
The argument realisations allows sets of data assumed to be
independent replications of the same process.
Data on different realisations may or may not be co-located.
For instance, data collected at different times
can be pooled together in the parameter estimation assuming
time independence.
The argument realisations takes a vector indicating the
replication number (e.g. the times).
If realisations = TRUE the code looks for an element
named realisations in the geodata object.
The log-likelihoods are computed for each replication and added together.
Further information on the package geoR can be found at: http://www.leg.ufpr.br/geoR.
summary.likGRF for summary of the results,
plot.variogram, lines.variogram and
lines.variomodel for graphical output,
proflik for computing profile likelihoods,
variofit and for other estimation methods,
and optim for the numerical minimisation function.
# NOT RUN {
ml <- likfit(s100, ini=c(0.5, 0.5), fix.nug = TRUE)
ml
summary(ml)
reml <- likfit(s100, ini=c(0.5, 0.5), fix.nug = TRUE, lik.met = "REML")
summary(reml)
plot(variog(s100))
lines(ml)
lines(reml, lty = 2)
# }
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