The function selects and returns the best GAMM out of the six GAMMs computed in
`model_gamm`

. In the case that the GAMM without any correlation
structure performs best, the output tibble contains the information from
the original `model_gam`

output tibble (therefore needed as input).

select_model(gam_tbl, gamm_tbl)

gam_tbl | Output tibble from the |
---|---|

gamm_tbl | Output tibble from the |

`select_model`

returns the same model output tibble as `model_gamm`

but with only **one** final GAMM for each filtered IND~pressure pair.

The best error structure is chosen here based on the Akaike`s Information Criterion (AIC). The GAMM with the lowest AIC value is selected, but only if the AIC difference to the GAMMs with a less complex error structure is greater than 2 (or respectively 4 or 6 depending on the level of nested complexity) (Burnham and Anderson, 2002). Otherwise the less complex GAMM is chosen. The following hierarchy of complexity is considered:

no structure < AR1 < AR2 and ARMA1,1 < ARMA2,1 and ARMA1,2

Burnham, K.P., Anderson, D.R. (2002) Model Selection and Multimodel Inference - A Practical Information-Theoretic Approach. Springer, New York.

Other IND~pressure modeling functions:
`find_id()`

,
`ind_init()`

,
`model_gamm()`

,
`model_gam()`

,
`plot_diagnostics()`

,
`plot_model()`

,
`scoring()`

,
`test_interaction()`

# Using some models of the Baltic Sea demo data test_ids <- c(67:70) gam_tbl <- model_gam_ex[model_gam_ex$id %in% test_ids,] gamm_tbl <- model_gamm(ind_init_ex[test_ids,], filter = gam_tbl$tac) best_gamm <- select_model(gam_tbl, gamm_tbl)