Complete a Residual Weighted Learning Analysis
Complete a Residual Weighted Learning Analysis
## S4 method for signature 'Kernel' .newRWL( moPropen, moMain, responseType, data, response, txName, lambdas, cvFolds, surrogate, guess, kernel, fSet, suppress, ... )
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 |
txVec |
treatment vector recast as +/- 1 |
An RWL object
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