# expectation.lgcpPredict: expectation.lgcpPredict function In lgcp: Log-Gaussian Cox Process

## 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

 1 2 ## 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

## Details

A Monte Carlo Average is computed as:

E_{π(Y_{t_1:t_2}|X_{t_1:t_2})}[g(Y_{t_1:t_2})] \approx \frac1n∑_{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 ith 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).

## Value

the expectated value of that function