running_weighted_mean

Computes running weighted mean of a vector or matrix, returning the values from each step.

Fast functions for effective sample size, weighted multivariate
mean, variance, and quantile computation, and weight diagnostic plot for
generic importance sampling type or other probability weighted samples.

Jouni Helske

diagis

Diagnostic Plot and Multivariate Summary Statistics of Weighted
Samples from Importance Sampling

running_weighted_mean function

<dl><dt>x</dt>
<dd>A numeric vector, matrix, three dimensional array, or an <code>mcmc</code> object from
the <code>coda</code> package. For matrix, the mean is computed for each column, and 
for array the sweep is done over the third dimension.</dd>
<dt>w</dt>
<dd>A numeric vector of non-negative weights. Will be automatically normalised to sum to one.</dd>
<dt>na.rm</dt>
<dd>If <code>TRUE</code>, <code>NA</code> values in <code>x</code> (and corresponding weights in <code>w</code>) are
omitted from the computation. Default is <code>FALSE</code>. Only used in vector methods.</dd></dl>

Arguments

Running weighted mean — running_weighted_mean

<dl>

<dt>x</dt>
<dd>A numeric vector, matrix, three dimensional array, or an <code>mcmc</code> object from
the <code>coda</code> package. For matrix, the mean is computed for each column, and 
for array the sweep is done over the third dimension.</dd>


<dt>w</dt>
<dd>A numeric vector of non-negative weights. Will be automatically normalised to sum to one.</dd>


<dt>na.rm</dt>
<dd>If <code>TRUE</code>, <code>NA</code> values in <code>x</code> (and corresponding weights in <code>w</code>) are
omitted from the computation. Default is <code>FALSE</code>. Only used in vector methods.</dd>

</dl>

running_weighted_mean: Running weighted mean

Description

Usage

Value

Arguments