CategorModel


This R6 class defines and fits a conditional probability model <code>P(A[j]|W,...)</code> for a univariate
 categorical summary measure <code>A[j]</code>. This class inherits from <code><a rd-options="" href="/link/GenericModel?package=stremr&version=0.4" data-mini-rdoc="stremr::GenericModel">GenericModel</a></code> class.
 Defines the fitting algorithm for a regression model <code>A[j] ~ W + ...</code>.
 Reconstructs the likelihood <code>P(A[j]=a[j]|W,...)</code> afterwards.
 Categorical <code>A[j]</code> is first redefined into <code>length(levels)</code> bin indicator variables, where
 <code>levels</code> is a numeric vector of all unique categories in <code>A[j]</code>.
 The fitting algorithm estimates the binary regressions for hazard for each bin indicator, <code>Bin_A[j][i] ~ W</code>,
 i.e., the probability that categorical <code>A[j]</code> falls into bin <code>i</code>, <code>Bin_A[j]_i</code>,
 given that <code>A[j]</code> does not fall in any prior bins <code>Bin_A[j]_1, ..., Bin_A[j]_{i-1}</code>.
 The dataset of bin indicators (<code>BinA[j]_1,...,BinA[j]_M</code>) is created
 inside the passed <code>data</code> or <code>newdata</code> object when defining <code>length(levels)</code> bins for <code>A[j]</code>.


class

Analysis of longitudinal time-to-event or time-to-failure data.
Estimates the counterfactual discrete survival curve under static, dynamic and
stochastic interventions on treatment (exposure) and monitoring events over time.
Estimators (IPW, MSM-IPW, GCOMP, longitudinal TMLE) adjust for measured time-varying
confounding and informative right-censoring. Model fitting can be performed either
with GLM or H2O-3 machine learning libraries.
The exposure, monitoring and censoring variables can be coded as either binary,
categorical or continuous. Each can be multivariate (e.g., can use more than one
column of dummy indicators for different censoring events).
The input data needs to be in long format.

Oleg Sofrygin

stremr

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

CategorModel function

An <code><a rd-options="" href="/link/R6Class?package=stremr&version=0.4" data-mini-rdoc="stremr::R6Class">R6Class</a></code> generator object

Format

Methods

Active Bindings


This R6 class defines and fits a conditional probability model <code>P(A[j]|W,...)</code> for a univariate
 categorical summary measure <code>A[j]</code>. This class inherits from <code><a rd-options='' href='GenericModel'>GenericModel</a></code> class.
 Defines the fitting algorithm for a regression model <code>A[j] ~ W + ...</code>.
 Reconstructs the likelihood <code>P(A[j]=a[j]|W,...)</code> afterwards.
 Categorical <code>A[j]</code> is first redefined into <code>length(levels)</code> bin indicator variables, where
 <code>levels</code> is a numeric vector of all unique categories in <code>A[j]</code>.
 The fitting algorithm estimates the binary regressions for hazard for each bin indicator, <code>Bin_A[j][i] ~ W</code>,
 i.e., the probability that categorical <code>A[j]</code> falls into bin <code>i</code>, <code>Bin_A[j]_i</code>,
 given that <code>A[j]</code> does not fall in any prior bins <code>Bin_A[j]_1, ..., Bin_A[j]_{i-1}</code>.
 The dataset of bin indicators (<code>BinA[j]_1,...,BinA[j]_M</code>) is created
 inside the passed <code>data</code> or <code>newdata</code> object when defining <code>length(levels)</code> bins for <code>A[j]</code>.


An <code><a rd-options='' href='R6Class'>R6Class</a></code> generator object

CategorModel: R6 class for fitting and predicting joint probability for a univariate categorical summary A[j]

Description

Usage

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Format

Methods

Active Bindings

Details