Description Usage Arguments Details Value Examples

Calculates
the WAIC of the fitted object of S4-class
stanfit whose stan file is described by only "`target += `

", which
calculates likelihoods with constant terms.

1 |

`StanS4classwithTargetFormulation` |
This is a fitted model
object built by In this package, the author made a new S4 class named |

`dig` |
The number of significant digits of WAIC. |

`summary` |
Logical: |

WAIC is an abbreviation for Widely Applicable Information Criterion (Watanabe-Akaike Information Criterion)

A real number, representing the value of
WAIC of the fitted model object `StanS4classwithTargetFormulation`

.

Revised 2020 Jan, Jul

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## Not run:
#========================================================================================
# Model selection based on WAIC
#========================================================================================
# (1) We prepare an example dataset in this package:
dat <- get(data("dataList.Chakra.1"))
# (2) Create a fitted model object;
fit1 <- fit_Bayesian_FROC(dat,
ModifiedPoisson = FALSE)
# (3) Using the fitted model object "fit", we can calculate the WAIC of it
waic(fit1)
# Fuerthermore,
# the Author provides an another model for a single reader and a single modality case.
# One is false alarm rates means "per lesion" and the other means "per image".
# The above "fit" is "per image".
# Now we shall consider to compare WAIC of these two models
# To do so, next we shall fit the "per lesion" model to the data as follows:
fit2 <- fit_Bayesian_FROC(dat,
ModifiedPoisson = TRUE)
waic(fit2)
# By compare two model's WAIC we can say which model is better.
# Note that the smaller WAIC is better.
waic(fit1) # per lesion model
waic(fit2) # per image model
# For the dataset,
# We should select one of the above two models
# by the criteria that the smaller waic is better.
# Namely, if the following inequality
waic(fit2) > waic(fit1)
# is TRUE, then we should use fit1.
# Similary, if the following inequality
waic(fit2) < waic(fit1)
# is TRUE, then we should use fit2.
# 2019.05.21 Revised.
# 2020 Feb Revised.
## End(Not run)# dottest
``` |

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