If .data is empty an InducerRpart object will be created. If .data is
a Dataset object a rpart model will be fitted.
Usage
InducerRpart(
.data = NULL,
formula,
weights,
subset,
na.action = "na.rpart",
method,
model = FALSE,
x = FALSE,
y = TRUE,
parms,
control,
cost
)Arguments
- .data
data object of class
Dataset- formula
a formula, with a response but no interaction terms. If this is a data frame, it is taken as the model frame
- weights
optional case weights.
- subset
optional expression saying that only a subset of the rows of the data should be used in the fit.
- na.action
the default action deletes all observations for which y is missing, but keeps those in which one or more predictors are missing.
- method
one of "anova", "poisson", "class" or "exp". If method is missing then the routine tries to make an intelligent guess
- model
if logical: keep a copy of the model frame in the result? If the input value for model is a model frame
- x
keep a copy of the x matrix in the result.
- y
keep a copy of the dependent variable in the result. If missing and model is supplied this defaults to FALSE.
- parms
optional parameters for the splitting function.
- control
a list of options that control details of the rpart algorithm
- cost
a vector of non-negative costs, one for each variable in the model. Defaults to one for all variables
Examples
inducer <- InducerRpart()
inducer
#> Inducer: rpart
#> Configuration: formula = , weights = , subset = , na.action = na.rpart, method = , model = FALSE, x = FALSE, y = TRUE, parms = , control = , cost =
cars.data <- Dataset(data = cars, target = "dist")
fittedInd <- InducerRpart(.data = cars.data)
#> Error in na.rpart(structure(list(dist = c(2, 10, 4, 22, 16, 10, 18, 26, 34, 17, 28, 14, 20, 24, 28, 26, 34, 34, 46, 26, 36, 60, 80, 20, 26, 54, 32, 40, 32, 40, 50, 42, 56, 76, 84, 36, 46, 68, 32, 48, 52, 56, 64, 66, 54, 70, 92, 93, 120, 85), speed = c(4, 4, 7, 7, 8, 9, 10, 10, 10, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 15, 15, 15, 16, 16, 17, 17, 17, 18, 18, 18, 18, 19, 19, 19, 20, 20, 20, 20, 20, 22, 23, 24, 24, 24, 24, 25)), class = "data.frame", row.names = c(NA, 50L), terms = dist ~ speed)): could not find function "na.rpart"
fittedInd
#> Error in eval(expr, envir, enclos): object 'fittedInd' not found