DatNet.sWsA


This class inherits from <code>DatNet</code> and extends its methods to handle a single matrix dataset of
 all summary measures <code>(sA,sW)</code>
 The class <code>DatNet.sWsA</code> is the only way to access data in the entire package.
 Contains methods for combining, subsetting, discretizing & binirizing summary measures <code>(sW,sA)</code>.
 For continous sVar this class provides methods for detecting / setting bin intervals,
 normalization, disretization and construction of bin indicators.
 The pointers to this class get passed on to <code>SummariesModel</code> functions: <code>$fit()</code>,
 <code>$predict()</code> and <code>$predictAeqa()</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

DatNet.sWsA 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(datnetW, datnetA, YnodeVals, det.Y, ...)</code></dt><dd>...</dd> <dt><code>addYnode(YnodeVals, det.Y)</code></dt><dd>...</dd> <dt><code>evalsubst(subsetexpr, subsetvars)</code></dt><dd>...</dd> <dt><code>get.dat.sWsA(rowsubset = TRUE, covars)</code></dt><dd>...</dd> <dt><code>get.outvar(rowsubset = TRUE, var)</code></dt><dd>...</dd> <dt><code>copy.sVar.types()</code></dt><dd>...</dd> <dt><code>bin.nms.sVar(name.sVar, nbins)</code></dt><dd>...</dd> <dt><code>pooled.bin.nm.sVar(name.sVar)</code></dt><dd>...</dd> <dt><code>detect.sVar.intrvls(name.sVar, nbins, bin_bymass, bin_bydhist, max_nperbin)</code></dt><dd>...</dd> <dt><code>detect.cat.sVar.levels(name.sVar)</code></dt><dd>...</dd> <dt><code>discretize.sVar(name.sVar, intervals)</code></dt><dd>...</dd> <dt><code>binirize.sVar(name.sVar, intervals, nbins, bin.nms)</code></dt><dd>...</dd> <dt><code>binirize.cat.sVar(name.sVar, levels)</code></dt><dd>...</dd> <dt><code>get.sVar.bw(name.sVar, intervals)</code></dt><dd>...</dd> <dt><code>get.sVar.bwdiff(name.sVar, intervals)</code></dt><dd>...</dd> <dt><code>make.dat.sWsA(p = 1, f.g_fun = NULL, sA.object = NULL)</code></dt><dd>...</dd> </dl> 

Methods

 <dl>
   <dt><code>dat.sWsA</code></dt><dd>...</dd> <dt><code>dat.bin.sVar</code></dt><dd>...</dd> <dt><code>emptydat.bin.sVar</code></dt><dd>...</dd> <dt><code>names.sWsA</code></dt><dd>...</dd> <dt><code>nobs</code></dt><dd>...</dd> <dt><code>noNA.Ynodevals</code></dt><dd>...</dd> <dt><code>nodes</code></dt><dd>...</dd> </dl> 

DatNet.sWsA: R6 class for storing and managing the combined summary measures `sW` & `sA` from DatNet classes.

Description

Usage

Arguments

Format

Methods

Active Bindings

Details