stgam (version 1.0.2)

evaluate_models: Evaluates multiple models with each predictor variable specified in different ways in order to determining model form

Description

Evaluates multiple models with each predictor variable specified in different ways in order to determining model form

Usage

evaluate_models(
  input_data,
  target_var,
  vars,
  coords_x,
  coords_y,
  VC_type = "SVC",
  time_var = NULL,
  ncores = 2
)

Value

a data.frame with indices for each predictor variable, a GCV score (gcv) for each model and the associated formula (f), which should be passed to the gam_model_rank function.

Arguments

input_data

he data to be used used to create the GAM model in (data.frame or tibble format), containing an Intercept column to allow it be treated as an addressable term in the model.

target_var

the name of the target variable.

vars

a vector of the predictor variable names (without the Intercept).

coords_x

the name of the X, Easting or Longitude variable in input_data.

coords_y

the name of the Y, Northing or Latitude variable in input_data.

VC_type

the type of varying coefficient model: options are "TVC" for temporally varying, "SVC" for spatially varying and "STVC" for space-time .

time_var

the name of the time variable if undertaking STVC model evaluations.

ncores

the number of cores to use in parallelised approaches (default is 2 to overcome CRAN package checks). This can be determined for your computer by running parallel::detectCores()-1. Parallel approaches are only undertaken if the number of models to evaluate is greater than 30.

Examples

require(dplyr)
require(doParallel)
# define input data
data("hp_data")
input_data <-
  hp_data |>
  # create Intercept as an addressable term
  mutate(Intercept = 1)
# evaluate different model forms
svc_mods <-
  evaluate_models(
    input_data = input_data,
    target_var = "priceper",
    vars = c("pef"),
    coords_x = "X",
    coords_y = "Y",
    VC_type = "SVC",
    time_var = NULL,
    ncores = 2
  )
head(svc_mods)