DatNet.sWsA: R6 class for storing and managing the combined summary...

Description Usage Format Details Methods Active Bindings

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

Methods

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

...

addYnode(YnodeVals, det.Y)

...

evalsubst(subsetexpr, subsetvars)

...

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

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get.outvar(rowsubset = TRUE, var)

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copy.sVar.types()

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

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

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

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

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discretize.sVar(name.sVar, intervals)

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

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

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

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

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

...

Active Bindings

dat.sWsA

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dat.bin.sVar

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emptydat.bin.sVar

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names.sWsA

...

nobs

...

noNA.Ynodevals

...

nodes

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tmlenet documentation built on May 29, 2017, 2:22 p.m.