fit()
and fit_xy()
take a model specification, translate the required
code by substituting arguments, and execute the model fit
routine.
Usage
# S3 method for class 'model_spec'
fit(
object,
formula,
data,
case_weights = NULL,
control = control_parsnip(),
...
)
# S3 method for class 'model_spec'
fit_xy(object, x, y, case_weights = NULL, control = control_parsnip(), ...)
Arguments
- object
An object of class
model_spec
that has a chosen engine (viaset_engine()
).- formula
An object of class
formula
(or one that can be coerced to that class): a symbolic description of the model to be fitted.- data
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.
- case_weights
An optional classed vector of numeric case weights. This must return
TRUE
whenhardhat::is_case_weights()
is run on it. Seehardhat::frequency_weights()
andhardhat::importance_weights()
for examples.- control
A named list with elements
verbosity
andcatch
. Seecontrol_parsnip()
.- ...
Not currently used; values passed here will be ignored. Other options required to fit the model should be passed using
set_engine()
.- x
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.- y
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.ordered
: If the outcome is a factor, was it an ordered factor?spec
: The model specification object (object
in the call tofit
)fit
: when the model is executed without error, this is the model object. Otherwise, it is atry-error
object with the error message.preproc
: any objects needed to convert between a formula and non-formula interface (such as theterms
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.
If you would like to use an alternative method for generating contrasts when
supplying a formula to fit()
, set the global option contrasts
to your
preferred method. For example, you might set it to:
options(contrasts = c(unordered = "contr.helmert", ordered = "contr.poly"))
.
See the help page for stats::contr.treatment()
for more possible contrast
types.
For models with "censored regression"
modes, an additional computation is
executed and saved in the parsnip object. The censor_probs
element contains
a "reverse Kaplan-Meier" curve that models the probability of censoring. This
may be used later to compute inverse probability censoring weights for
performance measures.
Sparse data is supported, with the use of the x
argument in fit_xy()
. See
allow_sparse_x
column of get_encoding()
for sparse input
compatibility.
See also
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)
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
#>
#>
#> 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: 3704
using_xy
#> parsnip model object
#>
#>
#> 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