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.lambda = 1
there is no transformation/back-transformation.
If lambda = 0 or lambda = 0.5 the moments are
back-tralocations of the main functions.
Defaults to FALSE but changed toprobabilities is included in the output.
This object contains, for each
prediction location, the probability that the variable is less
than orTRUE if simulations from the predictive are required.FALSE.NULL and changed internally in
the functions which call output.control. See DETAILS
below.TRUE. This function is typically called by the krige.bayes and krige.conv
defining the output to be returned by these functions.
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. at data locations predictions will coincide with the data.
For unsampled locations, when there is no transformation of the data,
the predicted values will be the same
regardless whether signal = TRUE or FALSE but the
predictions variances will differ.
By default krige.bayes sets signal = TRUE
and krige.conv sets signal = FALSE.
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: micro-scale variance
and measurement error.
If signal = TRUE the term $\epsilon_{ms}$ is
regarded as part of the signal and consequently the micro-scale variance is added to
the prediction variance.
If signal = FALSE the total error variance $\tau^2$
is added to the prediction variance.
krige.bayes and krige.conv.