Description Usage Arguments Details Value Author(s) See Also
Fit a generalized covariance model to simulation data
1 2 3 |
qldata |
object of class |
set.var |
logical vector of length one or equal to the number of covariance models;
for values |
var.type |
name of variance matrix approximation type (see |
var.opts |
list of arguments passed to |
intrinsic |
logical vector, |
... |
arguments passed to |
controls |
list of control parameters passed to |
cl |
cluster object, |
verbose |
if |
The function estimates the parameters of a covariance model using the REML method for kriging
the sample means of the statistics and kriging the variance matrix of statistics unless 'var.type
'
equals "const
". By default it uses the covariance model derived from a (self-similar) intrinsic random function, that is,
the 'sirfk
' function of order k (see, e.g. [1]) with k=1,2, for all statistics (including a default quadratic drift term
k=2). The user can also define different covariance models for each statistic separately (see below). Other covariance models can be used by their
name 'model
' which is passed to the function setCovModel
. Kriging the variance matrix always uses the 'sirfk
' covariance model.
Argument 'var.opts
' only sets the options for the covariance models for kriging the variance matrix if this is the users prefered
type of approximation. Other optional arguments, e.g., 'var.sim
' for the statistics, 'var.opts$var.sim
' for kriging the variance matrix,
specify the local or global nugget values for each sample point depending on whether or not 'set.var
' (used for kriging the statistics)
equals TRUE
. Both are passed to setCovModel
and must be data frames of lengths (number of columns) corresponding to the number of covariance
models of statistics and, respectively, to the number of Cholesky decomposed terms in case of kriging the variance matrix.
If 'set.var
' equals TRUE
(default), then local nugget variances are estimated by the variance of the sample average of the statistics.
Otherwise the values given in 'var.sim
' are used as fixed 'nugget' variances and replicated to match the number of sample points if required.
The same principle applies in case of kriging the variance matrix. If 'intrinsic
' equals TRUE
, then local nugget variances
for each of the variance-covariances of the of the statistics are estimated by a bootstrapping procedure. Otherwise the values given by 'var.opts$var.sim
'
(of length one or equal to the number of corresponding sample points) are used directly as local estimates (which then must correspond to
the other Cholesky decomposed terms). A global nugget value can be also estimated during the REML estimation which is the default option for both
cases unless this parameter is excluded from the covariance parameter estimation (see setCovModel
). The default optimization algorithm for
estimating the covariance parameters is the random starting point method mlsl
followed by a final local search by the same local algorithm.
Note that in this case the estimated parameters may vary when starting the REML procedure several times since starting points are chosen as random. All
options for the optimization can be modified by the argument 'controls
'.
Note that the returned object can also be constructed manually and passed as an input argument to
QLmodel
in case the user prefers to set up each covariance model separately. In this case, first use setCovModel
to construct
the covariance model, then estimate the parameters by fitCov
and pass a list of fitted covariance models to function QLmodel
.
Please see function QLmodel
for an example.
A list of fitted covariance models for kriging the sample means of statistics named 'covT
' and optionally
the variance matrix of statistics, 'covL
'. The object also stores the reml optimization parameters 'controls
'.
M. Baaske
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