# 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(), ...)
An object of class
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
Not currently used; values passed here will be
ignored. Other options required to fit the model should be
A matrix, sparse matrix, or data frame of predictors. Only some
models have support for sparse matrix input. See
A vector, matrix or data frame of outcome data.
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: when the model is executed without error,
this is the model object. Otherwise, it is a
object with the error message.
preproc: any objects needed to convert between
a formula and non-formula interface (such as the
The return value will also have a class related to the fitted model (e.g.
"_glm") before the base class of
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
y) and these functions translate
between the interface used when
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
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.
# Although `glm()` only has a formula interface, different # methods for specifying the model can be used library(dplyr)#> #>#>#> #>#>#> #>library(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: 65ms #> #> 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: 39ms #> #> 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