samplesStats {BRugs} | R Documentation

## Calculate summary statistics

### Description

This function produces summary statistics for a variable, pooling over the chains selected.

### Usage

    samplesStats(node, beg = samplesGetBeg(), end = samplesGetEnd(),
        firstChain = samplesGetFirstChain(),
        lastChain = samplesGetLastChain(), thin = samplesGetThin())

### Arguments

`node` | Character vector containing names of variables in the model.
`beg, end` | Arguments to select a slice of monitored values corresponding to iterations `beg:end`.
`firstChain, lastChain` | Arguments to select a sub group of chains to calculate summary statistics for.
`thin` | to only use every `thin`-th value of the stored sample for statistics.

### Details

If the variable of interest is an array, slices of the array can be selected using the notation `variable[lower0:upper0, lower1:upper1, ...]`. A star ‘`*`’ can be entered as shorthand for all the stored samples.

If the arguments are left at their defaults the whole sample for all chains will be used for calculation.

### Value

`samples.stats` returns a data frame with columns:

`mean` | means
`sd` | standard deviations
`MC_error` | Estimate of s/sqrt(N), the Monte Carlo standard error of the mean. The batch means method outlined by Roberts (1996; p.50) is used to estimate s.
`val2.5pc` | 0.025 quantiles
`median` | medians
`val97.5pc` | 0.975 quantiles
`start` | `beg` + 1
`sample` | sample sizes

### Note

If the MCMC simulation has an adaptive phase it will not be possible to make inference using values sampled before the end of this phase.

### References

Roberts, G.O. (1996): Markov Chain Concepts Related to Sampling Algorithms. In: W.R. Gilks, S. Richardson and D.J. Spiegelhalter (Eds.): Markov Chain Monte Carlo in Practice. Chapman and Hall, London, UK.

### See Also

`BRugs`, `help.WinBUGS`

[Package BRugs version 0.3-2 Index]