stgam (version 1.2.0)

calculate_vcs: Extracts varying coefficient estimates (for SVC, TVC and STVC models).

Description

Extracts varying coefficient estimates (for SVC, TVC and STVC models).

Usage

calculate_vcs(input_data, mgcv_model, terms = NULL)

Value

A data.frame of the input data, the coefficient estimates, the standard errors and the t-values estimates for each covariate. It can be used to generate coefficient estimates for specific time slices and over gridded surfaces as described in the package vignette.

Arguments

input_data

the data used to create the GAM model in data.frame, tibble or sf format. This can be the original data used to create the model or another surface with location and time attributes.

mgcv_model

a GAM model with smooths created using the mgcv package

terms

a vector of names starting with "Intercept" plus the names of the covariates used in the GAM model (these are the names of the variables in the input_data used to construct the model).

Examples

require(dplyr)
require(doParallel)
# define input data
data("chaco")
input_data <-
  chaco |>
  # create Intercept as an addressable term
  mutate(Intercept = 1)
# create a model for example as result of running `evaluate_models`
gam.m = gam(ndvi ~ 0 + s(X, Y, by = Intercept) +
 s(X, Y, by = tmax) + s(X, Y, by = pr), data = input_data)
# calculate the Varying Coefficients
terms = c("Intercept", "tmax", "pr")
vcs = calculate_vcs(input_data, gam.m, terms)
vcs |> select(ndvi, X, Y, starts_with(c("b_", "se_", "t_")), yhat)