tmle.SL.dbarts2 {tmle}

Super Learner wrappers for modeling and prediction using bart in the dbarts package

Description

These functions are used internally, not typically called by the user

Usage

tmle.SL.dbarts2(Y, X, newX, family, obsWeights, id, sigest = NA, sigdf = 3, 
	sigquant = 0.90, k = 2, power = 2.0, base = 0.95, binaryOffset = 0.0, 
	ntree = 200, ndpost = 1000, nskip = 100, printevery = 100,  keepevery = 1,  
	keeptrainfits = TRUE, usequants = FALSE, numcut = 100,printcutoffs = 0,  
	nthread = 1,   keepcall = TRUE,verbose = FALSE, ...)
tmle.SL.dbarts.k.5(Y, X, newX, family, obsWeights, id, sigest = NA, sigdf = 3, 
	sigquant = 0.90, k = 0.5, power = 2.0, base = 0.95, binaryOffset = 0.0, 
	ntree = 200, ndpost = 1000, nskip = 100, printevery = 100,  keepevery = 1,  
	keeptrainfits = TRUE, usequants = FALSE, numcut = 100,printcutoffs = 0,  
	nthread = 1,   keepcall = TRUE,verbose = FALSE, ...)
## S3 method for class 'tmle.SL.dbarts2'
predict(object, newdata, family, ...)

Arguments

Y

Dependent variable

X

Predictor covariate matrix or data frame used as training set

newX

Predictor covariate matrix or data frame for which predictions should be made

family

Regression family, 'gaussian' or 'binomial'

obsWeights

observation-level weights

id

identifier to group observations, not used

sigest

An estimate of error variance. See bart documentation

sigdf

Degrees of freedom for error variance prior. See bart documentation

sigquant

Quantile of error variance prior. See bart documentation

k

Tuning parameter that controls smoothing. Larger values are more conservative, see Details

power

Power parameter for tree prior

base

Base parameter for tree prior

binaryOffset

Allows fits with probabilities shrunk towards values other than 0.5. See bart documentation

ntree

Number of trees in the sum-of-trees formulation

ndpost

Number of posterior draws after burn in

nskip

Number of MCMC iterations treated as burn in

printevery

How often to print messages

keepevery

Every keepevery draw is kept to be returned to the user

keeptrainfits

If TRUE the draws of f(x) for x corresponding to the rows of x.train are returned

usequants

Controls how tree decisions rules are determined. See bart documentation

numcut

Maximum number of possible values used in decision rules

printcutoffs

Number of cutoff rules to print to screen. 0 prints nothing

nthread

Integer specifying how many threads to use

keepcall

Returns the call to BART when TRUE

verbose

Ignored for now

...

Additional arguments passed on to plot or control functions

object

Object of type tmle.SL.dbarts2

newdata

Matrix or dataframe used to get predictions from the fitted model

Details

tmle.SL.dbarts2 is in the default library for estimating Q. It uses the default setting in the dbarts package, k=2. tmle.SL.dbarts.k.5 is used to estimate the components of g. It sets k=0.5, to avoid shrinking predicted values too far from (0,1). See bart documentation for more information.

Value

an object of type tmle.SL.dbarts2 used internally by Super Learner

Author(s)

Chris Kennedy and Susan Gruber

See Also

SuperLearner