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

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Description

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).

Usage

1

Format

An R6Class generator object

Details

  • 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.

Methods

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)

...

Active Bindings

names.sVar

...

names.c.sVar

...

ncols.sVar

...

dat.sVar

...

emptydat.sVar

...

nodes

...

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