stgam (version 1.2.0)

gam_model_rank: Ranks models by AIC, giving the model form for each predictor variable.

Description

Ranks models by AIC, giving the model form for each predictor variable.

Usage

gam_model_rank(res_tab, n = 10)

Value

a tibble of the 'n' best models, ranked by AIC, with the form of each predictor variable where '---' indicates the absence of a predictor, 'Fixed' that a parametric form was specified, 's(S)' a spatial smooth, 's(T)' a temporal smooth and 'te(ST)' a combined space-time smooth. Model AIC is reported as are the knots in each smooth (ks) and the formula of each model (f).

Arguments

res_tab

a data.frame returned from the evaluate_models() function.

n

the number of ranked models to return.

Examples

require(dplyr)
require(stringr)
require(purrr)
require(doParallel)
require(sf)

# define input data
data("chaco")
input_data <-
  chaco |>
  # create Intercept as an addressable term
  mutate(Intercept = 1) |>
  # remove the geometry
  st_drop_geometry()

# evaluate different model forms
# example 1 with 6 models and no `k` adjustment
svc_mods <-
  evaluate_models(
    input_data = input_data,
    target_var = "ndvi",
    vars = c("tmax"),
    coords_x = "X",
    coords_y = "Y",
    VC_type = "SVC"
  )
# rank the models
gam_model_rank(svc_mods)