The return value depends of the type argument. If type is response the function
will return a matrix with n rows and the number of columns equal to the level of treatment.
If type is ITE then it returns a matrix with n rows and a number of columns equal to
one minus the levels of treatment. And if type is opT then it returns a matrix with n
rows and two columns. With the first column denoting the optimal treatment and
the second column denoting the optimal response.
Arguments
object
A fitted CERFIT object
newdata
New data to make predictions from. IF not provided will make predictions
on training data
gridval
For continuous treatment. Controls for what values of treatment to predict
prediction
Return prediction using all trees ("overall") or using first i trees ("by iter")
type
Choose what value you wish to predict. Response will predict the response.
ITE will predict the Individualized treatment effect. Node will predict the node. And opT
will predict the optimal treatment for each observation.
alpha
For continuous treatment it is the mixing parameter for the elastic
net regularization in each node. When equal to 0 it is ridge regression and
when equal to 1 it is lasso regression.
fit <- CERFIT(Result_of_Treatment ~ sex + age + Number_of_Warts + Area + Time + Type | treatment,
data = warts,
ntrees = 30,
method = "RCT",
mtry = 2)
ite <- predict(fit,type = "ITE")