DatNet: R6 class for storing and managing already evaluated summary...
In tmlenet: Targeted Maximum Likelihood Estimation for Network Data

Description Usage Format Details Methods Active Bindings

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).

DatNet

An R6Class generator object

Kmax - Maximum number of friends for any observation.
nFnode - Name of the vector that stores the number of friends for each observation (always set to 'nF').
netind_cl - Pointer to a network instance of class simcausal::NetIndClass.
Odata - Pointer to the input (observed) data frame.
mat.sVar - The evaluated matrix of summary measures for sW or sA.
sVar.object - Instance of the DefineSummariesClass class which contains the summary measure expressions for sW or sA.
type.sVar - named list of length ncol(mat.sVar) with sVar variable types: "binary"/"categor"/"contin".
norm.c.sVars - flag = TRUE if continous covariates need to be normalized.
nOdata - number of observations in the observed data frame.

new(netind_cl, nodes, nFnode, ...): ...
make.sVar(Odata, sVar.object = NULL, type.sVar = NULL, norm.c.sVars = FALSE): ...
def_types_sVar(type.sVar = NULL): ...
norm_c_sVars(): ...
fixmiss_sVar(): ...
norm.sVar(name.sVar): ...
set.sVar(name.sVar, new.sVar): ...
get.sVar(name.sVar): ...
set.sVar.type(name.sVar, new.type): ...
get.sVar.type(name.sVar): ...

names.sVar: ...
names.c.sVar: ...
ncols.sVar: ...
dat.sVar: ...
emptydat.sVar: ...
nodes: ...