fit.cluster_spec  R Documentation 
fit()
and fit_xy()
take a model specification, translate_celery the
required code by substituting arguments, and execute the model fit routine.
## S3 method for class 'cluster_spec' fit(object, formula, data, control = control_celery(), ...) ## S3 method for class 'cluster_spec' fit_xy(object, x, control = control_celery(), ...)
object 
An object of class 
formula 
An object of class 
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. 
control 
A named list with elements 
... 
Not currently used; values passed here will be ignored. Other
options required to fit the model should be passed using

x 
A matrix, sparse matrix, or data frame of predictors. Only some
models have support for sparse matrix input. See

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 enginespecific code.
Different model functions have different interfaces (e.g. formula or
x
/y
) and these functions translate_celery 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_celery()
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.
A cluster_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 tryerror
object with the error message.
preproc
: any objects needed to convert between a formula and
nonformula interface
(such as the terms
object)
The return value will also have a class related to the fitted model (e.g.
"_kmeans"
) before the base class of "cluster_fit"
.
set_engine_celery()
, control_celery()
, cluster_spec
,
cluster_fit
library(dplyr) kmeans_mod < k_means(k = 5) using_formula < kmeans_mod %>% set_engine_celery("stats") %>% fit(~., data = mtcars) using_x < kmeans_mod %>% set_engine_celery("stats") %>% fit_xy(x = mtcars) using_formula using_x
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.