psrf {EDISON} | R Documentation |
Calculates the potential scale reduction factor.
Description
This function calculates the potential scale reduction factor of parameters or hyperparameters over several MCMC simulations (or one simulation split up). This can serve as a convergence diagnostic.
Usage
psrf(parameters)
Arguments
parameters |
A list of MCMC trajectories, where each trajectory is a matrix with NumParams rows and NumIterations columns, where NumParams is the number of parameters and NumIterations is the number of samples. |
Value
A vectors of length NumParams, containing the PSRF values for each parameter.
Author(s)
Sophie Lebre
Frank Dondelinger
References
Gelman and Rubin (1992) Inference from iterative simulation using multiple sequences, Statistical Science.
See Also
Examples
# Generate 5 'runs' of random samples from Gaussian N(0,1)
samples = list()
for(run in 1:5) {
samples[[run]] = matrix(rnorm(1000), 1, 1000)
}
# Check potential scale reduction factor
# (Will be very close to 1 due to the samples being from
# the same distribution)
psrf.val = psrf(samples)
# Now use slightly different Gaussian distributions for each 'run'.
for(run in 1:5) {
mean = runif(1, 0, 2)
samples[[run]] = matrix(rnorm(1000, mean, 1), 1, 1000)
}
# Check potential scale reduction factor
# (Should be > 1.1)
psrf.val = psrf(samples)
[Package EDISON version 1.1.1 Index]