View source: R/model_selection.R
WAIC | R Documentation |
This function computes the WAIC value of an RprobitB_fit
object.
WAIC(x)
x |
An object of class |
WAIC is short for Widely Applicable (or Watanabe-Akaike) Information Criterion. As for AIC and BIC, the smaller the WAIC value the better the model. Its definition is
WAIC = -2 \cdot lppd + 2 \cdot p_{WAIC},
where lppd stands for log pointwise predictive density and p_{WAIC} is a penalty term proportional to the variance in the posterior distribution that is sometimes called effective number of parameters. The lppd is approximated as follows. Let
p_{is} = \Pr(y_i\mid θ_s)
be the probability of observation y_i given the sth set θ_s of parameter samples from the posterior. Then
lppd = ∑_i \log S^{-1} ∑_s p_{si}.
The penalty term is computed as the sum over the variances in log-probability for each observation:
p_{WAIC} = ∑_i V_{θ} ≤ft[ \log p_{si} \right].
A numeric, the WAIC value, with the following attributes:
se_waic
, the standard error of the WAIC value,
lppd
, the log pointwise predictive density,
p_waic
, the effective number of parameters,
p_waic_vec
, the vector of summands of p_waic
,
p_si
, the output of compute_p_si
.
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