diff --git a/404.html b/404.html index 72ffed02..b2ef1a73 100644 --- a/404.html +++ b/404.html @@ -27,7 +27,7 @@ sdmTMB - 0.6.0.9015 + 0.6.0.9016 + + + + + +
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+

Calculate conditional AIC

+ Source: R/caic.R +
cAIC.Rd
+
+ +
+

Calculates the conditional Akaike Information criterion (cAIC).

+
+ +
+

Usage

+ 
cAIC(object, what = c("cAIC", "EDF"), ...)
+
+ +
+

Arguments

+ + +
object
+

Output from sdmTMB().

+ + +
what
+

Whether to return the cAIC or the effective degrees of freedom +(EDF) for each group of random effects.

+ + +
...
+

Other arguments for specific methods. Not used.

+ +
+
+

Value

+

Either the cAIC or the effective degrees of freedom (EDF) by group +of random effects depending on the argument what.

+
+
+

Details

+

cAIC is designed to optimize the expected out-of-sample predictive +performance for new data that share the same random effects as the in-sample +(fitted) data, e.g., spatial interpolation. In this sense, it should be a +fast approximation to optimizing the model structure based on k-fold +cross-validation.

+

By contrast, AIC() calculates the marginal Akaike Information Criterion, +which is designed to optimize expected predictive performance for new data +that have new random effects, e.g., extrapolation, or inference about +generative parameters.

+

cAIC also calculates the effective degrees of freedom (EDF) as a byproduct. +This is the number of fixed effects that would have an equivalent impact on +model flexibility as a given random effect.

+

Both cAIC and EDF are calculated using Eq. 6 of Zheng, Cadigan, and Thorson +(2024).

+

For models that include profiled fixed effects, these profiles are turned +off.

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+
+

References

+

Deriving the general approximation to cAIC used here:

+

Zheng, N., Cadigan, N., & Thorson, J. T. (2024). +A note on numerical evaluation of conditional Akaike information for +nonlinear mixed-effects models (arXiv:2411.14185). arXiv. +doi:10.48550/arXiv.2411.14185

+

The utility of EDF to diagnose hierarchical model behaviour:

+

Thorson, J. T. (2024). Measuring complexity for hierarchical +models using effective degrees of freedom. Ecology, +105(7), e4327 doi:10.1002/ecy.4327

+
+ +
+

Examples

+ 
mesh <- make_mesh(dogfish, c("X", "Y"), cutoff = 15)
+fit <- sdmTMB(catch_weight ~ s(log(depth)),
+  time_varying = ~1,
+  time_varying_type = "ar1",
+  time = "year",
+  spatiotemporal = "off",
+  mesh = mesh,
+  family = tweedie(),
+  data = dogfish,
+  offset = log(dogfish$area_swept)
+)
+#> Detected irregular time spacing with an AR(1) or random walk process.
+#> Consider filling in the missing time slices with `extra_time`.
+#> `extra_time = c(2005, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2020)`
+cAIC(fit)
+#> [1] 12089.43
+cAIC(fit, what = "EDF")
+#>    b_rw_t  b_smooth   omega_s 
+#>  9.043457  6.613376 38.730229 
+AIC(fit)
+#> [1] 12192.96
+
+
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+ + +
+ + + + + + + diff --git a/reference/coef.sdmTMB.html b/reference/coef.sdmTMB.html index 1ef7f3f8..70ca62ba 100644 --- a/reference/coef.sdmTMB.html +++ b/reference/coef.sdmTMB.html @@ -7,7 +7,7 @@ sdmTMB - 0.6.0.9015 + 0.6.0.9016