BinOutModel


This R6 class can request, store and manage the design matrix Xmat, as well as the binary outcome Bin for the
 logistic regression P(Bin|Xmat).
 Can also be used for converting data in wide format to long when requested,
 e.g., when pooling across binary indicators (fitting one pooled logistic regression model for several indicators)
 The class has methods that perform queries to data storage R6 class DatNet.sWsA to get appropriate data columns & row subsets


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

BinOutModel 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)</code></dt><dd>Uses <code>reg</code> R6 <code><a rd-options="" href="/link/RegressionClass?package=tmlenet&version=0.1.0" data-mini-rdoc="tmlenet::RegressionClass">RegressionClass</a></code> object to instantiate a new model for a
  logistic regression with binary outcome.</dd> <dt><code>show()</code></dt><dd>Print information on outcome and predictor names used in this regression model</dd> <dt><code>fit()</code></dt><dd>...</dd> <dt><code>copy.fit()</code></dt><dd>...</dd> <dt><code>predict()</code></dt><dd>...</dd> <dt><code>copy.predict()</code></dt><dd>...</dd> <dt><code>predictAeqa()</code></dt><dd>...</dd> </dl> 

Methods

 <dl>
  <dt><code>getoutvarnm</code></dt><dd>...</dd> <dt><code>getoutvarval</code></dt><dd>...</dd> <dt><code>getsubset</code></dt><dd>...</dd> <dt><code>getprobA1</code></dt><dd>...</dd> <dt><code>getfit</code></dt><dd>...</dd> <dt><code>wipe.alldat</code></dt><dd>...</dd> </dl> 

BinOutModel: R6 class for fitting and making predictions for a single logistic regression with binary outcome B, P(B | PredVars)

Description

Usage

Arguments

Format

Methods

Active Bindings

Details