deviance_explained {tinyVAST} | R Documentation |
Calculate deviance explained
Description
deviance_explained
fits a null model, calculates the deviance relative to
a saturated model for both the original and the null model, and uses these
to calculate the proportion of deviance explained.
This implementation conditions upon the maximum likelihood estimate of fixed effects and the empirical Bayes ("plug-in") prediction of random effects. It can be described as "conditional deviance explained". A state-space model that estimates measurement error variance approaching zero (i.e., collapses to a process-error-only model) will have a conditional deviance explained that approaches 1.0
Usage
deviance_explained(x, null_formula, null_delta_formula = ~1)
Arguments
x |
output from |
null_formula |
formula for the null model. If missing, it uses
|
null_delta_formula |
formula for the null model for the delta component.
If missing, it uses
|
Value
the proportion of conditional deviance explained.