output.control {geoR} | R Documentation

## Defines output options for prediction functions

### Description

Auxiliary function defining output options for `krige.bayes` and `krige.conv`.

### 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 distributions at each of the prediction locations. If `quantile = TRUE` default values equals to `c(0.025, 0.5, 0.975)`, are assumed. A measure of uncertainty of the predictions, analogous to the kriging standard error, can be computed by (quantile0.975 - quantile0.025)/4. Only used if prediction locations are provided in the argument `locations`.
`threshold` | a (optional) numerical vector. If one or more values are provided, 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. Defaults to `NULL` and changed internally in the functions which call `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 to be returned by these functions.

The underlying model

Y(x) = mu + S(x) + e

assumes that observations Y(x) are noisy versions of a signal S(x) and Var(e)=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 e = e_{ms} + e_{me} and the nugget variance is divided into two terms: micro-scale variance and measurement error.
If `signal = TRUE` the term e_{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.

### Author(s)

Paulo J. Ribeiro Jr. paulojus@leg.ufpr.br,
Peter J. Diggle p.diggle@lancaster.ac.uk.

### See Also

The prediction functions `krige.bayes` and `krige.conv`.

[Package geoR version 1.6-19 Index]