.newRWL,Kernel-method {DynTxRegime} | R Documentation |
Complete a Residual Weighted Learning Analysis
Description
Complete a Residual Weighted Learning Analysis
Usage
## S4 method for signature 'Kernel'
.newRWL(
moPropen,
moMain,
responseType,
data,
response,
txName,
lambdas,
cvFolds,
surrogate,
guess,
kernel,
fSet,
suppress,
...
)
Arguments
moPropen |
modelObj for propensity modeling |
moMain |
modelObj for main effects |
responseType |
Character indicating type of response |
data |
data.frame of covariates |
response |
vector of responses |
txName |
treatment variable column header in data |
lambdas |
tuning parameter(s) |
cvFolds |
number of cross-validation folds |
surrogate |
Surrogate object |
guess |
optional numeric vector providing starting values for optimization methods |
kernel |
Kernel object |
fSet |
Function or NULL defining subsets |
suppress |
T/F indicating if prints to screen are executed |
... |
Additional inputs for optimization |
Value
An RWL object
[Package DynTxRegime version 4.15 Index]