lgcp (version 1.3-2)

expectation.lgcpPredict: expectation.lgcpPredict function

Description

This function requires data to have been dumped to disk: see ?dump2dir and ?setoutput. This function computes the Monte Carlo Average of a function where data from a run of lgcpPredict has been dumped to disk.

Usage

## S3 method for class 'lgcpPredict':
expectation(obj, fun,
    maxit = NULL, ...)

Arguments

obj

an object of class lgcpPredict

fun

a function accepting a single argument that returns a numeric vector, matrix or array object

maxit

Not used in ordinary circumstances. Defines subset of samples over which to compute expectation. Expectation is computed using information from iterations 1:maxit, where 1 is the first non-burn in iteration dumped to disk.

...

additional arguments

Value

• the expectated value of that function

Details

A Monte Carlo Average is computed as: $$E_{\pi(Y_{t_1:t_2}|X_{t_1:t_2})}[g(Y_{t_1:t_2})] \approx \frac1n\sum_{i=1}^n g(Y_{t_1:t_2}^{(i)})$$ where $g$ is a function of interest, $Y_{t_1:t_2}^{(i)}$ is the $i$th retained sample from the target and $n$ is the total number of retained iterations. For example, to compute the mean of $Y_{t_1:t_2}$ set, $$g(Y_{t_1:t_2}) = Y_{t_1:t_2},$$ the output from such a Monte Carlo average would be a set of $t_2-t_1$ grids, each cell of which being equal to the mean over all retained iterations of the algorithm (NOTE: this is just an example computation, in practice, there is no need to compute the mean on line explicitly, as this is already done by default in lgcpPredict).

See Also

lgcpPredict, dump2dir, setoutput