DatNet


Class for evaluating and storing arbitrary summary measures sVar.
 The summary measures are evaluated based on the user-specified sVar expressions in sVar.object (sW or sA),
 in the environment of the input data.frame (Odata).
 The evaluated summary measures from sVar.object are stored as a matrix (self$mat.sVar).
 Contains methods for replacing missing values with default in gvars$misXreplace.
 Also contains method for detecting / setting sVar variable type (binary, categor, contin).


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 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(netind_cl, nodes, nFnode, ...)</code></dt><dd>...</dd> <dt><code>make.sVar(Odata, sVar.object = NULL, type.sVar = NULL, norm.c.sVars = FALSE)</code></dt><dd>...</dd> <dt><code>def_types_sVar(type.sVar = NULL)</code></dt><dd>...</dd> <dt><code>norm_c_sVars()</code></dt><dd>...</dd> <dt><code>fixmiss_sVar()</code></dt><dd>...</dd> <dt><code>norm.sVar(name.sVar)</code></dt><dd>...</dd> <dt><code>set.sVar(name.sVar, new.sVar)</code></dt><dd>...</dd> <dt><code>get.sVar(name.sVar)</code></dt><dd>...</dd> <dt><code>set.sVar.type(name.sVar, new.type)</code></dt><dd>...</dd> <dt><code>get.sVar.type(name.sVar)</code></dt><dd>...</dd> </dl> 

Methods

 <dl>
   <dt><code>names.sVar</code></dt><dd>...</dd> <dt><code>names.c.sVar</code></dt><dd>...</dd> <dt><code>ncols.sVar</code></dt><dd>...</dd> <dt><code>dat.sVar</code></dt><dd>...</dd> <dt><code>emptydat.sVar</code></dt><dd>...</dd> <dt><code>nodes</code></dt><dd>...</dd> </dl> 

DatNet: R6 class for storing and managing already evaluated summary measures `sW` or `sA` (but not both at the same time).

Description

Usage

Arguments

Format

Methods

Active Bindings

Details