BinOutModel: R6 class for fitting and making predictions for a single...
In tmlenet: Targeted Maximum Likelihood Estimation for Network Data

Description Usage Format Details Methods Active Bindings

This R6 class can request, store and manage the design matrix Xmat, as well as the binary outcome Bin for the logistic regression P(Bin|Xmat). Can also be used for converting data in wide format to long when requested, e.g., when pooling across binary indicators (fitting one pooled logistic regression model for several indicators) The class has methods that perform queries to data storage R6 class DatNet.sWsA to get appropriate data columns & row subsets

1	BinOutModel

An R6Class generator object

cont.sVar.flag - Is the original outcome variable continuous?
bw.j - Bin width (interval length) for an outcome that is a bin indicator of a discretized continous outcome.
glmfitclass - Controls which package will be used for performing model fits (glm or speedglm).
bindat - Pointer to an instance of BinDat class that contains the data.

new(reg): Uses reg R6 RegressionClass object to instantiate a new model for a logistic regression with binary outcome.
show(): Print information on outcome and predictor names used in this regression model
fit(): ...
copy.fit(): ...
predict(): ...
copy.predict(): ...
predictAeqa(): ...

getoutvarnm: ...
getoutvarval: ...
getsubset: ...
getprobA1: ...
getfit: ...
wipe.alldat: ...