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

Description

This class inherits from DatNet and extends its methods to handle a single matrix dataset of all summary measures (sA,sW) The class DatNet.sWsA is the only way to access data in the entire package. Contains methods for combining, subsetting, discretizing & binirizing summary measures (sW,sA). 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 SummariesModel functions: $fit(), $predict() and $predictAeqa().

Usage

1

Format

An R6Class generator object

Details

  • datnetW - .

  • datnetA - .

  • active.bin.sVar - Currently discretized continous sVar column in data matrix mat.sVar.

  • mat.bin.sVar - Matrix of the binary indicators for discretized continuous covariate active.bin.sVar.

  • ord.sVar - Ordinal (categorical) transformation of a continous covariate sVar.

  • YnodeVals - .

  • det.Y - .

  • p - .

Methods

new(datnetW, datnetA, YnodeVals, det.Y, ...)

...

addYnode(YnodeVals, det.Y)

...

evalsubst(subsetexpr, subsetvars)

...

get.dat.sWsA(rowsubset = TRUE, covars)

...

get.outvar(rowsubset = TRUE, var)

...

copy.sVar.types()

...

bin.nms.sVar(name.sVar, nbins)

...

pooled.bin.nm.sVar(name.sVar)

...

detect.sVar.intrvls(name.sVar, nbins, bin_bymass, bin_bydhist, max_nperbin)

...

detect.cat.sVar.levels(name.sVar)

...

discretize.sVar(name.sVar, intervals)

...

binirize.sVar(name.sVar, intervals, nbins, bin.nms)

...

binirize.cat.sVar(name.sVar, levels)

...

get.sVar.bw(name.sVar, intervals)

...

get.sVar.bwdiff(name.sVar, intervals)

...

make.dat.sWsA(p = 1, f.g_fun = NULL, sA.object = NULL)

...

Active Bindings

dat.sWsA

...

dat.bin.sVar

...

emptydat.bin.sVar

...

names.sWsA

...

nobs

...

noNA.Ynodevals

...

nodes

...

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