MachineShop (version 3.8.0)

ModelFrame: ModelFrame Class

Description

Class for storing data, formulas, and other attributes for MachineShop model fitting.

Usage

ModelFrame(...)
# S3 method for formula
ModelFrame(
  formula,
  data,
  groups = NULL,
  strata = NULL,
  weights = NULL,
  na.rm = TRUE,
  ...
)
# S3 method for matrix
ModelFrame(
  x,
  y = NULL,
  offsets = NULL,
  groups = NULL,
  strata = NULL,
  weights = NULL,
  na.rm = TRUE,
  ...
)

Value

ModelFrame class object that inherits from data.frame.

Arguments

...

arguments passed from the generic function to its methods. The first argument of each ModelFrame 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. In the associated method, arguments groups, strata, and weights will be evaluated as expressions, whose objects are searched for first in the accompanying data environment and, if not found there, next in the calling environment.

groups

vector of values defining groupings of case observations, such as repeated measurements, to keep together during resampling [default: none].

strata

vector of values to use in conducting stratified resample estimation of model performance [default: none].

weights

numeric vector of non-negative case weights for the y response variable [default: equal weights].

na.rm

character string or logical specifying removal of "all" (TRUE) cases with missing values, "none" (FALSE), or only those whose missing values are in the "response" variable.

x, y

matrix and object containing predictor and response variables.

offsets

numeric vector, matrix, or data frame of values to be added with a fixed coefficient of 1 to linear predictors in compatible regression models.

See Also

fit, resample, response, SelectedInput

Examples

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

mf <- ModelFrame(ncases / (ncases + ncontrols) ~ agegp + tobgp + alcgp,
                 data = esoph, weights = ncases + ncontrols)
gbm_fit <- fit(mf, model = GBMModel)
varimp(gbm_fit)
# }