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).