MachineShop (version 3.5.0)

fit: Model Fitting

Description

Fit a model to estimate its parameters from a data set.

Usage

fit(...)
# S3 method for formula
fit(formula, data, model, ...)
# S3 method for matrix
fit(x, y, model, ...)
# S3 method for ModelFrame
fit(input, model = NULL, ...)
# S3 method for recipe
fit(input, model = NULL, ...)
# S3 method for ModelSpecification
fit(object, ...)
# S3 method for MLModel
fit(model, ...)
# S3 method for MLModelFunction
fit(model, ...)

Value

MLModelFit class object.

Arguments

...

arguments passed from the generic function to its methods and from the MLModel and MLModelFunction methods to others. The first argument of each fit method is positional and, as such, must be given first in calls to them.

formula, data

formula defining the model predictor and response variables and a data frame containing them.

model

model function, function name, or object; or another object that can be coerced to a model. A model can be given first followed by any of the variable specifications, and the argument can be omitted altogether in the case of modeled inputs.

x, y

matrix and object containing predictor and response variables.

input

input object defining and containing the model predictor and response variables.

object

model specification.

Details

User-specified case weights may be specified for ModelFrames upon creation with the weights argument in its constructor.

Variables in recipe specifications may be designated as case weights with the role_case function.

See Also

as.MLModel, response, predict, varimp

Examples

# \donttest{
## Requires prior installation of suggested package gbm to run

## Survival response example
library(survival)

gbm_fit <- fit(Surv(time, status) ~ ., data = veteran, model = GBMModel)
varimp(gbm_fit)
# }