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MID Regression

Source: R/mid_reg.R
mid_reg.Rd

mid_reg() defines a model that can predict numeric values from predictors using a set of functions each with up to two predictors. This function fits a regression model based on the Maximum Interpretation Decomposition.

Usage

mid_reg(
  mode = "regression",
  engine = "midr",
  model = NULL,
  params_main = NULL,
  params_inter = NULL,
  penalty = NULL,
  terms = NULL
)

fit_mid_reg(
  formula,
  data,
  weights = NULL,
  model = NULL,
  params_main = NULL,
  params_inter = NULL,
  penalty = NULL,
  terms = NULL,
  ...
)

Arguments

mode

a single character string for the type of model. Currently, only "regression" is supported.

engine

a single character string specifying the computational engine to use. The default is "midr".

model

an optional fitted model object (black-box model) to be interpreted. Default is NULL.

params_main

an integer specifying the maximum number of sample points (knots) to model main effects. Corresponds to the argument k[1] in midr::interpret().

params_inter

an integer specifying the maximum number of sample points (knots) to model interaction effects. Corresponds to the argument k[2] or k2 in midr::interpret().

penalty

a non-negative number representing the total amount of regularization. Corresponds to the argument lambda in midr::interpret().

terms

a character vector or formula of term labels to be included in the fitting process.

formula

an object of class formula: a symbolic description of the model to be fitted.

data

a data frame containing the variables in the model.

weights

an optional vector of case weights.

...

other arguments to be passed on to midr::interpret().

Value

a "model_spec" object with class "mid_reg".

Details

This function is the main specification for the parsnip model. The underlying fitting is performed by fit_mid_reg(), which is a helper function that connects fit() to the midr::interpret() function.

See also

interpret