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)

## Arguments

gam_tbl Output tibble from the model_gam function. Output tibble from the model_gamm function.

## Value

select_model returns the same model output tibble as model_gamm but with only one final GAMM for each filtered IND~pressure pair.

## Details

The best error structure is chosen here based on the Akaikes 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

## References

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
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)`