collinear (version 2.0.0)

model_formula: Generate Model Formulas

Description

Generate Model Formulas

Usage

model_formula(
  df = NULL,
  response = NULL,
  predictors = NULL,
  term_f = NULL,
  term_args = NULL,
  random_effects = NULL,
  quiet = FALSE
)

Value

list if predictors is a list or length of response is higher than one, and character vector otherwise.

Arguments

df

(optional; data frame, tibble, or sf). A data frame with responses and predictors. Required if predictors = NULL. Default: NULL.

response

(optional; character string or vector) Name/s of response variable/s in df. Used in target encoding when it names a numeric variable and there are categorical predictors, and to compute preference order. Default: NULL.

predictors

(optional, character vector, output of collinear()): predictors to include in the formula. Required if df = NULL.

term_f

(optional; string). Name of function to apply to each term in the formula, such as "s" for mgcv::s() or any other smoothing function, "poly" for stats::poly(). Default: NULL

term_args

(optional; string). Arguments of the function applied to each term. For example, for "poly" it can be "degree = 2, raw = TRUE". Default: NULL

random_effects

(optional, string or character vector). Names of variables to be used as random effects. Each element is added to the final formula as +(1 | random_effect_name). Default: NULL

quiet

(optional; logical) If FALSE, messages generated during the execution of the function are printed to the console Default: FALSE

See Also

Other modelling_tools: case_weights(), performance_score_auc(), performance_score_r2(), performance_score_v()

Examples

#using df, response, and predictors
#----------------------------------
df <- vi[1:1000, ]

#additive formulas
formulas_additive <- model_formula(
  df = df,
  response = c(
    "vi_numeric",
    "vi_categorical"
    ),
  predictors = vi_predictors_numeric[1:10]
)

formulas_additive

#using a formula in a model
#m <- stats::lm(
#  formula = formulas_additive[[1]],
#  data = df
#  )

# using output of collinear()
#----------------------------------
selection <- collinear(
  df = df,
  response = c(
    "vi_numeric",
    "vi_binomial"
  ),
  predictors = vi_predictors_numeric[1:10],
  quiet = TRUE
)

#polynomial formulas
formulas_poly <- model_formula(
  predictors = selection,
  term_f = "poly",
  term_args = "degree = 3, raw = TRUE"
)

formulas_poly

#gam formulas
formulas_gam <- model_formula(
  predictors = selection,
  term_f = "s"
)

formulas_gam

#adding a random effect
formulas_random_effect <- model_formula(
  predictors = selection,
  random_effects = "country_name"
)

formulas_random_effect