library(additive)
library(recipes) library(workflows)
Let's simulate a data using mgcv
package, which is automatically loaded by additive
.
set.seed(2020) dat <- gamSim(1, n = 400, dist = "normal", scale = 2)
In a first step, we use the recipes
package to prepare (a recipe for) the data.
test_recipe <- dat |> recipe() |> update_role(y, new_role = "outcome") |> update_role(x0, x1, x2, x3, new_role = "predictor") |> step_normalize(all_numeric_predictors())
print(test_recipe)
Above, we not only define the roles of the relevant variables but also
normalized all numeric predictors to facilitate model fitting later on.
In the next step, we use additive
to set up a basic model structure.
test_model <- additive( family = gaussian(), method = "REML" ) |> set_engine("mgcv") |> set_mode("regression")
print(test_model)
The additive
function is the main function of the package to initialize a
Generalized Additive Model (GAM). We can set up a lot of the information directly
within the function or update the information later on, via the update
method.
For example, if we didn't specify the family initially or set it to something else
that we now wanted to change, we could use the update
method as follows
test_model <- test_model |> update(family = gaussian())
Next, we define a workflow via the workflows
package, by combining the above
defined data processing recipe and the model plus the actual model formula to be
passed to the mgcv
engine.
test_workflow <- workflow() |> add_recipe(test_recipe) |> add_model( spec = test_model, formula = y ~ s(x0) + s(x1) + s(x2) + s(x3) )
print(test_workflow)
We are now ready to fit the model by calling the fit
method
with the data set we want to train the model on.
run_on_linux <- grepl("linux", R.Version()$os, ignore.case = TRUE)
test_workflow_fit <- test_workflow |> fit(data = dat)
print(test_workflow_fit)
To extract the parsnip model fit from the workflow
test_fit <- test_workflow_fit |> extract_fit_parsnip()
The gamObject
object can be extracted as follows
gam_fit <- test_workflow_fit |> extract_fit_engine()
class(gam_fit)
We can use the trained workflow, which includes the fitted model, to
conveniently predict
using new data without having to worry about all
the data reprocessing, which is automatically applied using the workflow
preprocessor (recipe).
newdata <- dat[1:5, ]
test_workflow_fit |> predict( new_data = newdata, type = "conf_int", level = 0.95 )
To add the standard errors on the scale of the linear predictors
test_workflow_fit |> predict( new_data = newdata, type = "conf_int", level = 0.95, std_error = TRUE )
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