collinear (version 3.0.0)

model_formula: Build model formulas from response and predictors

Description

Generates model formulas from a dataframe, a response name, and a vector of predictors that can be the output of a multicollinearity management function such as collinear_select() and the likes. Intended to help fit exploratory models from the result of a multicollinearity analysis.

The types of formulas it can generate are:

• additive: y ~ x + z

• polynomial: y ~ poly(x, ...) + poly(z, ...)

• GAM: y ~ s(x) + s(z)

• random effect: y ~ x + (1 \ z)

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

(required; dataframe, tibble, or sf) A dataframe with responses (optional) and predictors. Must have at least 10 rows for pairwise correlation analysis, and 10 * (length(predictors) - 1) for VIF. Default: NULL.

response

(optional, character string) Name of a response variable in df. Default: NULL.

predictors

(optional; character vector or NULL) Names of the predictors in df. If NULL, all columns except responses and constant/near-zero-variance columns are used. Default: 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 are printed. Default: FALSE.

...

(optional) Internal args (e.g. function_name for validate_arg_function_name, a precomputed correlation matrix m, or cross-validation args for preference_order).

See Also

Other modelling_tools: case_weights(), score_auc(), score_cramer(), score_r2()

Examples

data(
  vi_smol,
  vi_predictors_numeric
  )

#reduce collinearity
x <- collinear_select(
  df = vi_smol,
  predictors = vi_predictors_numeric
)

#additive formula
y <- model_formula(
  df = vi_smol,
  response = "vi_numeric",
  predictors = x
)

y

#using a formula in a model
m <- stats::lm(
 formula = y,
 data = vi_smol
 )

summary(m)

#classification formula (character response)
y <- model_formula(
  df = vi_smol,
  response = "vi_categorical",
  predictors = x
)

y


#polynomial formula (3rd degree)
y <- model_formula(
  df = vi_smol,
  response = "vi_numeric",
  predictors = x,
  term_f = "poly",
  term_args = "degree = 3, raw = TRUE"
)

y

#gam formula
y <- model_formula(
  df = vi_smol,
  response = "vi_numeric",
  predictors = x,
  term_f = "s"
)

y

#random effect
y <- model_formula(
  df = vi_smol,
  response = "vi_numeric",
  predictors = x,
  random_effects = "country_name" #from vi_smol$country_name
)

y