DatNet.sWsA: R6 class for storing and managing the combined summary...
In tmlenet: Targeted Maximum Likelihood Estimation for Network Data

Description Usage Format Details Methods Active Bindings

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().

1	DatNet.sWsA

An R6Class generator object

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 - .

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): ...

dat.sWsA: ...
dat.bin.sVar: ...
emptydat.bin.sVar: ...
names.sWsA: ...
nobs: ...
noNA.Ynodevals: ...
nodes: ...