Information Criteria For Each Participant — compare

Returns the BPIC/DIC based model weights for each participant in a list of samples objects

Usage

compare_subject(
  sList,
  stage = "sample",
  filter = 0,
  use_best_fit = TRUE,
  print_summary = TRUE,
  digits = 3
)

Arguments

sList: List of samples objects
stage: A string. Specifies which stage the samples are to be taken from "preburn", "burn", "adapt", or "sample"
filter: An integer or vector. If it's an integer, iterations up until the value set by filter will be excluded. If a vector is supplied, only the iterations in the vector will be considered.
use_best_fit: Boolean, defaults to TRUE, use minimal likelihood or mean likelihood (whichever is better) in the calculation, otherwise always uses the mean likelihood.
print_summary: Boolean (defaults to TRUE) print table of results
digits: Integer, significant digits in printed table

Value

List of matrices for each subject of effective number of parameters, mean deviance, deviance of mean, DIC, BPIC and associated weights.

Examples

# For a broader illustration see `compare`.
# Here we just take two times the same model, but normally one would compare
# different models
compare_subject(list(m0 = samples_LNR, m1 = samples_LNR))
#> Warning: subject-by-subject comparison is best done with models of type `single`
#>      wDIC_m0 wDIC_m1 wBPIC_m0 wBPIC_m1
#> as1t     0.5     0.5      0.5      0.5
#> bd6t     0.5     0.5      0.5      0.5
#> bl1t     0.5     0.5      0.5      0.5
#> 
#> Winners
#>      m0
#> DIC   3
#> BPIC  3