tmle.Sl.dbarts2: Super Learner wrappers for modeling and prediction using...

In tmle: Targeted Maximum Likelihood Estimation

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	id

id 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

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

Author(s)

Chris Kennedy and Susan Gruber

See Also

SuperLearner

tmle documentation built on Aug. 23, 2023, 1:08 a.m.