MachineShop (version 1.1.0)

MLModel: MLModel Class Constructor

Description

Create a model for use with the MachineShop package.

Usage

MLModel(name = "MLModel", label = name, packages = character(),
  types = character(), params = list(), grid = function(x, length,
  random, ...) NULL, design = c(NA, "model.matrix", "terms"),
  fit = function(formula, data, weights, ...) stop("no fit function"),
  predict = function(object, newdata, times, ...)
  stop("no predict function"), varimp = function(object, ...) NULL, ...)

Arguments

name

character string name for the instantiated MLModel object; same name as the object to which the model is assigned.

label

descriptive label for the model.

packages

character vector of packages whose namespaces are required by the model.

types

character vector of response variable types to which the model can be fit. Supported types are "binary", "factor", "matrix", "numeric", "ordered", and "Surv".

params

list of user-specified model parameters to be passed to the fit function.

grid

tuning grid function whose first agument x is a ModelFrame of the model fit data and formula, followed by a length to use in generating sequences of parameter values, a number of grid points to sample at random, and an ellipsis (...).

design

character string indicating whether the type of design matrix used to fit the model is a "model.matrix", a data.frame of the original predictor variable "terms", or unknown (default).

fit

model fitting function whose arguments are a formula, a data frame, case weights, and an ellipsis.

predict

model prediction function whose arguments are the object returned by fit, a newdata frame of predictor variables, optional vector of times at which to predict survival, and an ellipsis.

varimp

variable importance function whose arguments are the object returned by fit, optional arguments passed from calls to varimp, and an ellipsis.

...

arguments passed from other methods.

Value

MLModel class object.

Details

If supplied, the grid function should return a list whose elements are named after and contain values of parameters to include in a tuning grid to be constructed automatically by the package.

Values returned by the predict functions should be formatted according to the response variable types below.

factor

a vector or column matrix of probabilities for the second level of binary factors or a matrix whose columns contain the probabilities for factors with more than two levels.

matrix

a matrix of predicted responses.

numeric

a vector or column matrix of predicted responses.

Surv

a matrix whose columns contain survival probabilities at times if supplied or a vector of predicted survival means otherwise.

The varimp function should return a vector of importance values named after the predictor variables or a matrix or data frame whose rows are named after the predictors.

See Also

modelinfo, fit, resample, tune

Examples

# NOT RUN {
## Logistic regression model
LogisticModel <- MLModel(
  name = "LogisticModel",
  types = "binary",
  fit = function(formula, data, weights, ...) {
    glm(formula, data = data, weights = weights, family = binomial, ...)
  },
  predict = function(object, newdata, ...) {
    predict(object, newdata = newdata, type = "response")
  },
  varimp = function(object, ...) {
    pchisq(coef(object)^2 / diag(vcov(object)), 1)
  }
)

library(MASS)
res <- resample(type ~ ., data = Pima.tr, model = LogisticModel)
summary(res)

# }