loo: Leave-one-out cross validation for Hickory models
In kholsinger/Hickory: Analysis of within-population inbreeding and population differentiation
Description
Usage
Arguments
Value
Description
Use leave-one-out cross validation to estimate the expected log pointwise
predictive density (elpd_loo), the effective number of parameters,
(p_loo), and the LOO information criterion (looic). See the
documentation for loo() in the loo package for details.
Usage
1
Arguments
fit
A stanfit object returned from analyze_codominant() or
analyze_dominant()
...
Optional parameters passed to loo::loo()
Value
A named list from loo::loo().
kholsinger/Hickory documentation built on Jan. 9, 2022, 6:30 p.m.
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Description Usage Arguments Value
Description
Use leave-one-out cross validation to estimate the expected log pointwise
predictive density (elpd_loo), the effective number of parameters,
(p_loo), and the LOO information criterion (looic). See the
documentation for loo() in the loo package for details.
Usage
1 |
Arguments
fit |
A stanfit object returned from |
... |
Optional parameters passed to |
Value
A named list from loo::loo().
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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