CategorSummaryModel


This R6 class defines and fits a conditional probability model <code>P(sA[j]|sW,...)</code> for a univariate
 categorical summary measure <code>sA[j]</code>. This class inherits from <code><a rd-options="" href="/link/SummariesModel?package=tmlenet&version=0.1.0" data-mini-rdoc="tmlenet::SummariesModel">SummariesModel</a></code> class.
 Defines the fitting algorithm for a regression model <code>sA[j] ~ sW + ...</code>.
 Reconstructs the likelihood <code>P(sA[j]=sa[j]|sW,...)</code> afterwards.
 Categorical <code>sA[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>sA[j]</code>.
 The fitting algorithm estimates the binary regressions for hazard for each bin indicator, <code>Bin_sA[j][i] ~ sW</code>,
 i.e., the probability that categorical <code>sA[j]</code> falls into bin <code>i</code>, <code>Bin_sA[j]_i</code>,
 given that <code>sA[j]</code> does not fall in any prior bins <code>Bin_sA[j]_1, ..., Bin_sA[j]_{i-1}</code>.
 The dataset of bin indicators (<code>BinsA[j]_1,...,BinsA[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>sA[j]</code>.


class

Estimation of average causal effects for single time point
interventions in network-dependent data (e.g., in the presence of spillover
and/or interference). Supports arbitrary interventions (static or
stochastic). Implemented estimation algorithms are the targeted maximum
likelihood estimation (TMLE), the inverse-probability-of-treatment (IPTW)
estimator and the parametric G-computation formula estimator. Asymptotically
correct influence-curve-based confidence intervals are constructed for the
TMLE and IPTW. The data are assumed to consist of rows of unit-specific
observations, each row i represented by variables (F.i,W.i,A.i,Y.i), where
F.i is a vector of friend IDs of unit i (i's network), W.i is a vector of
i's baseline covariates, A.i is i's exposure (can be binary, categorical or
continuous) and Y.i is i's binary outcome. Exposure A.i depends on
(multivariate) user-specified baseline summary measure(s) sW.i, where sW.i
is any function of i's baseline covariates W.i and the baseline covariates
of i's friends in F.i. Outcome Y.i depends on sW.i and (multivariate)
user-specified summary measure(s) sA.i, where sA.i is any function of i's
baseline covariates and exposure (W.i,A.i) and the baseline covariates and
exposures of i's friends. The summary measures are defined with functions
def.sW and def.sA. See ?'tmlenet-package' for a general overview.

Oleg Sofrygin

tmlenet

Targeted Maximum Likelihood Estimation for Network Data

CategorSummaryModel function

An <code><a rd-options="" href="/link/R6Class?package=tmlenet&version=0.1.0" data-mini-rdoc="tmlenet::R6Class">R6Class</a></code> generator object

Format

 <dl>
  <dt><code>new(reg, DatNet.sWsA.g0, ...)</code></dt><dd>...</dd> <dt><code>fit(data)</code></dt><dd>...</dd> <dt><code>predict(newdata)</code></dt><dd>...</dd> <dt><code>predictAeqa(newdata)</code></dt><dd>...</dd> </dl> 

Methods

CategorSummaryModel: R6 class for fitting and predicting joint probability for a univariate categorical summary measure sA[j]

Description

Usage

Arguments

Format

Methods

Active Bindings

Details