ContinSummaryModel: R6 class for fitting and predicting joint probability for a...
In tmlenet: Targeted Maximum Likelihood Estimation for Network Data

Description Usage Format Details Methods Active Bindings

This R6 class defines and fits a conditional probability model P(sA[j]|sW,...) for a univariate continuous summary measure sA[j]. This class inherits from SummariesModel class. Defines the fitting algorithm for a regression model sA[j] ~ sW + .... Reconstructs the likelihood P(sA[j]=sa[j]|sW,...) afterwards. Continuous sA[j] is discretized using either of the 3 interval cutoff methods, defined via RegressionClass object reg passed to this class constructor. The fitting algorithm estimates the binary regressions for hazard Bin_sA[j][i] ~ sW, i.e., the probability that continuous sA[j] falls into bin i, Bin_sA[j]_i, given that sA[j] does not belong to any prior bins Bin_sA[j]_1, ..., Bin_sA[j]_{i-1}. The dataset of discretized summary measures (BinsA[j]_1,...,BinsA[j]_M) is created inside the passed data or newdata object while discretizing sA[j] into M bins.