output.control  R Documentation 
Auxiliary function defining output options for
krige.bayes
and krige.conv
.
output.control(n.posterior, n.predictive, moments, n.back.moments,
simulations.predictive, mean.var, quantile,
threshold, sim.means, sim.vars, signal, messages)
n.posterior 
number of samples to be taken from the posterior distribution. Defaults to 1000. 
n.predictive 
number of samples to be taken from the
predictive distribution. Default equals to

moments 
logical. Indicates whether the moments of the
predictive distribution are returned. If 
n.back.moments 
number of sample to backtransform moments by simulation. Defaults to 1000. 
simulations.predictive 
logical. Defines whether to draw simulations
from the predictive distribution.
Only considered if prediction
locations are provided in the argument 
mean.var 
logical (optional). Indicates whether mean and variances of the simulations of the predictive distributions are computed and returned. 
quantile 
a (optional) numeric vector.
If provided indicates whether quantiles of the
simulations from the
predictive distribution are computed and returned.
If a vector with numbers in the interval

threshold 
Optional. A numerical vector.
If one or more values are provided, an object named

sim.means 
logical (optional). Indicates whether mean
of each of the conditional simulations of the predictive
distribution should be computed and returned. Defaults to

sim.vars 
logical (optional). Indicates whether variance
of each of the conditional simulations of the predictive
distribution should be computed and returned. Defaults to 
signal 
logical indicating whether the signal or the variable is
to be predicted. Different defaults are set internally by
functions calling 
messages 
logical. Indicates
whether or not status messages are printed on the output device
while the function is running. Defaults to 
SIGNAL
This function is typically called by the geoR's prediction functions
krige.bayes
and krige.conv
defining the output.
By default, krige.bayes
sets signal = TRUE
and krige.conv
sets signal = FALSE
.
The underlying model
Y(x) = \mu + S(x) + \epsilon
assumes that observations Y(x)
are noisy
versions of a signal S(x)
and
Var(\epsilon)=\tau^2
is the nugget variance.
If \tau^2 = 0
the Y
and S
are
indistiguishable.
If \tau^2 > 0
and regarded as measurement error, the
option signal
defines whether the S
(signal =
TRUE
) or the variable Y
(signal = FALSE
) is to be
predicted.
For the latter the predictions will "honor" the data,
i.e. predicted values will coincide with the data, at data locations.
For unsampled locations and untransformed data,
the predicted values equals data
regardless signal = TRUE
or FALSE
, however
predictions variances will differ.
The function krige.conv
has an argument
micro.scale
. If micro.scale > 0
the error term is
divided as \epsilon = \epsilon_{ms} + \epsilon_{me}
and the nugget variance is divided into two terms: microscale variance
and measurement error.
If signal = TRUE
the term \epsilon_{ms}
is
regarded as part of the signal and consequently the microscale variance is added to
the prediction variance.
If signal = FALSE
the total error variance \tau^2
is added to the prediction variance.
A list with processed arguments to be passed to the main function.
Paulo J. Ribeiro Jr. paulojus@leg.ufpr.br,
Peter J. Diggle p.diggle@lancaster.ac.uk.
The prediction functions krige.bayes
and krige.conv
.
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