predict.ITR: Treatment prediction for rcDT and rcRF models
In kdoub5ha/rcITR: Risk Controlled ITR Discovery

Description Usage Arguments Value Examples

Returns treatment predictions for a rcDT and rcRF models given a new data set. If the input is rcRF (forest), then the proportion of trees voting for treatment ('trt=1') is returned. If the input is rcDT (single tree), then the function returns the vote (0 / 1) for the model.

1 2	## S3 method for class 'ITR' predict(fit, new.data, split.var, ctgs = NULL)

`fit`	tree or forest object from 'rcDT' or 'rcRF'.
`new.data`	data.frame of new observations
`split.var`	numeric vector indicating columns of covariates
`ctgs`	numeric vector of columns of categorical covariates.

A list of prediction summaries

`SummaryTreat`	proportion of trees voting for treatment (trt=1). If input is rcDT (single tree) then SummaryTreat is a single number. If input is rcRF (forest) then SummaryTreat is a vector equal to the length of the number of trees.
`trt.pred`	vector of treatment assignments 0, 1 based on the tree vote (single tree) or majority of tree votes (forest). This vector has length equal to the number of rows in 'new.data'.
`n.trees`	number of tree in 'fit'
`tree.votes`	matrix of votes for each tree for each subject in 'new.data'. Rows correspond to trees in 'fit' and columns correspond to subjects in 'new.dat'.
`data`	input data frame 'new.data'
`NA.trees`	number of trees returning no votes. In a forest, this is the number of null trees.

# Generate simulated data
set.seed(123)
dat <- generateData()

# Generates rcDT using simualated data with splitting variables located in columns 1-10.
rcDT.fit <- rcDT(data = dat, 
                 split.var = 1:10, 
                 risk.threshold = 2.75, 
                 lambda = 1)
# Predict treatment assignments for 1000 observations in `dat` using the rcDT model
preds.rcDT <- predict.ITR(fit = rcDT.fit, new.data = dat, split.var = 1:10)

# Generates rcRF using simualated data with splitting variables located in columns 1-10.
set.seed(2)
rcRF.fit <- rcRF(data = dat, 
                 split.var = 1:10, 
                 ntree = 200,
                 risk.threshold = 2.75, 
                 lambda = 1)
# Predict treatment assignments for 1000 observations in `dat` using the rcRF model
preds.rcRF <- predict.ITR(fit = rcRF.fit, new.data = dat, split.var = 1:10)