fit() and fit_xy() take a model specification, translate the required code by substituting arguments, and execute the model fit routine.

# S3 method for model_spec
fit(object, formula, data, control = control_parsnip(), ...)

# S3 method for model_spec
fit_xy(object, x, y, control = control_parsnip(), ...)

## Arguments

object An object of class model_spec that has a chosen engine (via set_engine()). An object of class formula (or one that can be coerced to that class): a symbolic description of the model to be fitted. Optional, depending on the interface (see Details below). A data frame containing all relevant variables (e.g. outcome(s), predictors, case weights, etc). Note: when needed, a named argument should be used. A named list with elements verbosity and catch. See control_parsnip(). Not currently used; values passed here will be ignored. Other options required to fit the model should be passed using set_engine(). A matrix, sparse matrix, or data frame of predictors. Only some models have support for sparse matrix input. See parsnip::get_encoding() for details. x should have column names. A vector, matrix or data frame of outcome data.

## Value

A model_fit object that contains several elements:

• lvl: If the outcome is a factor, this contains the factor levels at the time of model fitting.

• spec: The model specification object (object in the call to fit)

• fit: when the model is executed without error, this is the model object. Otherwise, it is a try-error object with the error message.

• preproc: any objects needed to convert between a formula and non-formula interface (such as the terms object)

The return value will also have a class related to the fitted model (e.g. "_glm") before the base class of "model_fit".

## Details

fit() and fit_xy() substitute the current arguments in the model specification into the computational engine's code, check them for validity, then fit the model using the data and the engine-specific code. Different model functions have different interfaces (e.g. formula or x/y) and these functions translate between the interface used when fit() or fit_xy() was invoked and the one required by the underlying model.

When possible, these functions attempt to avoid making copies of the data. For example, if the underlying model uses a formula and fit() is invoked, the original data are references when the model is fit. However, if the underlying model uses something else, such as x/y, the formula is evaluated and the data are converted to the required format. In this case, any calls in the resulting model objects reference the temporary objects used to fit the model.

If the model engine has not been set, the model's default engine will be used (as discussed on each model page). If the verbosity option of control_parsnip() is greater than zero, a warning will be produced.

set_engine(), control_parsnip(), model_spec, model_fit

## Examples

# Although glm() only has a formula interface, different
# methods for specifying the model can be used

library(dplyr)
#>
#> Attaching package: ‘dplyr’#> The following objects are masked from ‘package:stats’:
#>
#>     filter, lag#> The following objects are masked from ‘package:base’:
#>
#>     intersect, setdiff, setequal, unionlibrary(modeldata)
data("lending_club")

lr_mod <- logistic_reg()

using_formula <-
lr_mod %>%
set_engine("glm") %>%
fit(Class ~ funded_amnt + int_rate, data = lending_club)

using_xy <-
lr_mod %>%
set_engine("glm") %>%
fit_xy(x = lending_club[, c("funded_amnt", "int_rate")],
y = lending_club\$Class)

using_formula
#> parsnip model object
#>
#> Fit time:  33ms
#>
#> Call:  stats::glm(formula = Class ~ funded_amnt + int_rate, family = stats::binomial,
#>     data = data)
#>
#> Coefficients:
#> (Intercept)  funded_amnt     int_rate
#>   5.131e+00    2.767e-06   -1.586e-01
#>
#> Degrees of Freedom: 9856 Total (i.e. Null);  9854 Residual
#> Null Deviance:	    4055
#> Residual Deviance: 3698 	AIC: 3704using_xy
#> parsnip model object
#>
#> Fit time:  31ms
#>
#> Call:  stats::glm(formula = ..y ~ ., family = stats::binomial, data = data)
#>
#> Coefficients:
#> (Intercept)  funded_amnt     int_rate
#>   5.131e+00    2.767e-06   -1.586e-01
#>
#> Degrees of Freedom: 9856 Total (i.e. Null);  9854 Residual
#> Null Deviance:	    4055
#> Residual Deviance: 3698 	AIC: 3704