Complete a Learning Analysis
Performs a weighted learning analysis.
.newLearning(fSet, kernel, ...) ## S4 method for signature ''NULL',Kernel' .newLearning( fSet, kernel, ..., moPropen, moMain, moCont, data, response, txName, lambdas, cvFolds, iter, surrogate, suppress, guess, createObj, prodPi = 1, index = NULL ) ## S4 method for signature ''function',Kernel' .newLearning( fSet, kernel, ..., moPropen, moMain, moCont, data, response, txName, lambdas, cvFolds, iter, surrogate, suppress, guess, createObj, prodPi = 1, index = NULL ) ## S4 method for signature ''function',SubsetList' .newLearning( fSet, kernel, moPropen, moMain, moCont, data, response, txName, lambdas, cvFolds, iter, surrogate, suppress, guess, createObj, prodPi = 1, index = NULL, ... )
fSet |
NULL or function defining subset rules |
kernel |
Kernel object or SubsetList |
... |
Additional inputs for optimization |
moPropen |
modelObj for propensity model |
moMain |
modelObj for main effects of outcome model |
moCont |
modelObj for contrasts of outcome model |
data |
data.frame of covariates |
response |
Vector of responses |
txName |
Tx variable column header in data |
lambdas |
Tuning parameter(s) |
cvFolds |
Number of cross-validation folds |
iter |
Maximum number of iterations for outcome regression |
surrogate |
Surrogate object |
suppress |
T/F indicating if prints to screen are executed |
guess |
optional numeric vector providing starting values for optimization methods |
createObj |
A function name defining the method object for a specific learning algorithm |
prodPi |
A vector of propensity weights |
index |
The subset of individuals to be included in learning |
A Learning object
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