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]