Specify a working model for a study variable y

Declares the working model that step_model_calibration() fits for one study variable: a formula, a learner (a linear/glm model or a machine-learning method such as a regression tree, a random forest or gradient boosting) and, for glm, a family. It builds no model and touches no data – it records the specification that the calibration step will fit on the sample and predict over the population.

Usage

y_model(formula, engine = c("glm", "tree", "forest", "boost"), family = NULL)

Arguments

formula: full formula, e.g. income ~ sex + age_g.
engine: "glm", "tree" (rpart), "forest" (ranger) or "boost" (xgboost). The flexible learners run with fixed default settings (hyperparameters are not currently exposed): "tree"/"forest" use the 'rpart'/'ranger' defaults, and "boost" uses xgboost with nrounds = 150, max_depth = 4 and eta = 0.1.
family: for engine = "glm": "gaussian", "binomial" or "poisson". For tree/forest, regression vs classification is inferred from y.

Value

a model specification list.

Examples

y_model(income ~ age + sex, engine = "glm")
#> $formula
#> income ~ age + sex
#> <environment: 0x557a35809848>
#> 
#> $engine
#> [1] "glm"
#> 
#> $family
#> NULL
#> 
#> attr(,"class")
#> [1] "wf_y_model"