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
`n.posterior`

.

moments

logical. Indicates whether the moments of the
predictive distribution are returned. If `lambda = 1`

there is no transformation/back-transformation.
If `lambda = 0`

or `lambda = 0.5`

the moments are
back-transformed by analytical expressions. For other cases the
back-transformation is done by simulation. Defaults to `TRUE`

.

n.back.moments

number of sample to back-transform 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 `locations`

of the main functions.
Defaults to `FALSE`

but changed to
`TRUE`

if an integer greater then zero is provided in the
argument `n.predictive`

and/or simulations are required in
order to compute quantities required by other arguments such as
threshold, quantiles and some values of the transformation parameter.

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
\([0,1]\) is provided, the output includes the object
`quantiles`

, which contains values of corresponding
estimated quantiles. For
example, if `quantile = c(0.25, 0.50, 0.75)`

the function
returns the quartiles of the predictive distributions at each of the
prediction locations.
If `quantile = TRUE`

default values ```
c(0.025, 0.5,
0.975)
```

are assumed.
A measure of uncertainty of the predictions, an alternative to the kriging
standard error, computed by \((quantile_0.975 - quantile_0.025)/4\).
Only used if prediction locations are provided in the argument
`locations`

.

threshold

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

is included in the output.
This object contains, for each
prediction location, the probability that the variable is less
than or equal than the threshold provided by the user.
Defaults to `FALSE`

.

sim.means

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

, if simulations from the predictive are required.

sim.vars

logical (optional). Indicates whether variance
of each of the conditional simulations of the predictive
distribution should be computed and returned. Defaults to `FALSE`

.

signal

logical indicating whether the signal or the variable is
to be predicted. Different defaults are set internally by
functions calling `output.control`

. See `DETAILS`

below.

messages

logical. Indicates
whether or not status messages are printed on the output device
while the function is running. Defaults to `TRUE`

.

A list with processed arguments to be passed to the main function.

**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: *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.

The prediction functions `krige.bayes`

and `krige.conv`

.