ContinModel: R6 class for modeling (fitting and predicting) joint...

In chizhangucb/tmleCommunity: Targeted Maximum Likelihood Estimation for Hierarchical Data

Description

ContinModel inherits from GenericModel class, defining and modeling a joint conditional density P(A[m]|W,E,...) where A[m] is univariate and continuous. By calling self$new(), A[m] will be discretized into nbins bins via one of the 3 bin cutoff approaches (See Details for tmleCommunity). By calling self$fit(), it fits hazard regressoin Bin_A[m][k] ~ W + E on data (a DatKeepClass class), which is the hazard probaility of the the observation of A[m] belongs to bin Bin_A[m][k], given covariates (W, E) and that observation doesn't belong to any precedent bins Bin_A[m][1], Bin_A[m][2], ..., Bin_A[m][k-1].

Usage

ContinModel

Format

An R6Class generator object

Details

• reg - .

• outvar - .

• nbins -

• bin_nms - Character vector of column names of bin indicators.

• intrvls -

• intrvls.width -

• bin_weights - .

Methods

new(reg, DataStorageClass.g0, DataStorageClass.gstar, ...)

Instantiate an new instance of ContinModel for a univariate continuous outcome A[m]

fit(data, savespace = TRUE)

...

predict(newdata, savespace = TRUE)

...

predictAeqa(newdata, savespace = TRUE, wipeProb = TRUE)

...

Active Bindings

cats

...

See Also

DatKeepClass, RegressionClass, GenericModel, BinaryOutModel

chizhangucb/tmleCommunity documentation built on May 20, 2019, 3:34 p.m.