Description Usage Arguments Value Author(s) See Also
Hazard estimation using binary Super Learner
1 |
N.delta |
Discrete survival process. often created by |
n.controls |
number of randomly sampled observations from risk set using |
time.df |
degrees of freedom for the |
time |
the time variable. often |
X |
the baseline variables. Do not include the outcome nor time |
newX |
test data frame |
time.newX |
time points in the newX data frame |
SL.library |
algorithms for binary Super Learner |
V |
number of cross validation folds for the Super Learner |
shuffle |
shuffle rows in data frame before creating cross validation folds |
verbose |
more detailed output |
family |
binomial |
discreteTime |
currently not implemented |
method |
Loss function for combining prediction in the library. Currently either "NNLS" (the default) or "NNloglik". NNLS is non-negative least squares and will work for both gaussian and binomial. NNloglik is a non-negative binomial likelihood maximization |
id |
subject identification variable |
save.fit.library |
logical variable for saving the fit of each algorithm in |
trim.logit |
Only used if |
obsWeights |
observation weights |
SL.predict |
predicted probability of having an event |
cand.names |
gives a list of all algorithms in the library, including any screening algorithms |
SL.library |
gives a list of all algorithms in the library |
init.coef |
coefficient estimates from the non-negative least squares |
coef |
Coefficient estimates in the super learner |
library.predict |
predicted values from all candidates in |
cv.risk |
V-fold cross-validated risk estimates for each algorithm in the library |
newZ |
predicted values from all candidates in |
fit.library |
a list containing the fit of each model in SL.library on the full data set |
id |
cluster identification variable |
namesX |
The variable names in the X data frame |
DATA.split |
a list with the cross-validation splits |
method |
method used to estimate weight for prediction in library |
whichScreen |
a logical matrix indicating which variables were selected for each screening algorithm |
trim.logit |
Only used if |
errorsInCVLibrary |
A logical vector with length equal to the number of algorithms in the library. equal to 1 if the corresponding algorithm failed on any of the cross-validation splits |
errorsInLibrary |
A logical vector with length equal to the number of algorithms in the library. equal to 1 if the corresponding algorithm failed on full data |
Eric C Polley ecpolley@berkeley.edu
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