ContinModel: R6 class for fitting and predicting joint probability for a...

In stremr: Streamlined Estimation of Survival for Static, Dynamic and Stochastic Treatment and Monitoring Regimes

Description

This R6 class defines and fits a conditional probability model P(A[j]|W,...) for a univariate continuous summary measure A[j]. This class inherits from GenericModel class. Defines the fitting algorithm for a regression model A[j] ~ W + .... Reconstructs the likelihood P(A[j]=a[j]|W,...) afterwards. Continuous A[j] is discretized using either of the 3 interval cutoff methods, defined via RegressionClass object reg passed to this class constructor. The fitting algorithm estimates the binary regressions for hazard Bin_A[j][i] ~ W, i.e., the probability that continuous A[j] falls into bin i, Bin_A[j]_i, given that A[j] does not belong to any prior bins Bin_A[j]_1, ..., Bin_A[j]_{i-1}. The dataset of discretized summary measures (BinA[j]_1,...,BinA[j]_M) is created inside the passed data or newdata object while discretizing A[j] into M bins.

Usage

ContinModel

Format

An R6Class generator object

Details

• reg - .

• outvar - .

• intrvls - .

• intrvls.width - .

• bin_weights - .

Methods

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

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fit(data)

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predict(newdata)

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predictAeqa(newdata)

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Active Bindings

cats

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stremr documentation built on May 30, 2017, 6:35 a.m.