# output.control

##### Defines output options for prediction functions

Auxiliary function defining output options for
`krige.bayes`

and `krige.conv`

.

- Keywords
- spatial

##### Usage

```
output.control(n.posterior, n.predictive, moments, n.back.moments,
simulations.predictive, mean.var, quantile,
threshold, sim.means, sim.vars, signal, messages)
```

##### Arguments

- 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`

.

##### Details

**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.

##### Value

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

##### See Also

The prediction functions `krige.bayes`

and `krige.conv`

.

*Documentation reproduced from package geoR, version 1.8-1, License: GPL (>= 2)*