loocv_thresh_gam: Leave-One-Out Cross-Validation (LOOCV) procedure for...
In INDperform: Evaluation of Indicator Performances for Assessing Ecosystem States

Description Usage Arguments Value See Also Examples

loocv_thresh_gam applies a LOOCV on a threshold-GAM and its corresponding GAM and returns TRUE if the threshold-GAM has a lower estimate, else FALSE (see for more infos on the LOOCV procedure the details section in test_interaction).

loocv_thresh_gam(
  model,
  ind_vec,
  press_vec,
  t_var_vec,
  name_t_var,
  k,
  a,
  b,
  time
)

`model`	A single GAM object from the model tibble needed to extract the family and the link function.
`ind_vec`	A vector with the IND training observations (including or excluding defined outliers).
`press_vec`	A vector with the training observations (including or excluding defined outliers) of pressure 1 (i.e. the original significant pressure in the GAM(M)).
`name_t_var`	The name of the threshold variable (pressure 2). t_var will be named after this string in the model formula.
`k`	Choice of knots (for the smoothing function `s`); the default is 4 to avoid over-parameterization.
`a`	The lower quantile value of the selected threshold variable, which the estimated threshold is not allowed to exceed; the default is 0.2.
`b`	The upper quantile value of the selected threshold variable, which the estimated threshold is not allowed to exceed; the default is 0.8.
`time`	A vector containing the actual time series.

The function returns a list with the following 2 sublists:

result: logical; if TRUE, at least one thresh_gam performs better than its corresponding gam based on LOOCV value.
error: A string capturing potential error messages that occurred as side effects when fitting the threshold GAM for the LOOCV.

thresh_gam which creates a threshold-GAM object and test_interaction which applies thresh_gam and loocv_thresh_gam

# Using the first model in the Baltic Sea demo data
loocv_thresh_gam(model = model_gam_ex$model[[1]],
  ind_vec = ind_init_ex$ind_train[[1]],
  press_vec = ind_init_ex$press_train[[1]],
  t_var_vec = ind_init_ex$press_train[[2]],
  name_t_var = "Swin",
  k = 4, a = 0.2, b = 0.8,
  time = ind_init_ex$time_train[[1]])