SummariesModel: R6 class for fitting and predicting model P(sA|sW) under...
In tmlenet: Targeted Maximum Likelihood Estimation for Network Data

Description Usage Format Details Methods Active Bindings

This R6 class Class for defining, fitting and predicting the probability model P(sA|sW) under g_star or under g_0 for summary measures (sW,sA). Defines and manages the factorization of the multivariate conditional probability model P(sA=sa|...) into univariate regression models sA[j] ~ sA[j-1] + ... + sA[1] + sW. The class self$new method automatically figures out the correct joint probability factorization into univariate conditional probabilities based on name ordering provided by (sA_nms, sW_nms). When the outcome variable sA[j] is binary, this class will automatically call a new instance of BinOutModel class. Provide self$fit() function argument data as a DatNet.sWsA class object. This data will be used for fitting the model P(sA|sW). Provide self$fit() function argument newdata (also as DatNet.sWsA class) for predictions of the type P(sA=1|sW=sw), where sw values are coming from newdata object. Finally, provide self$predictAeqa function newdata argument (also DatNet.sWsA class object) for getting the likelihood predictions P(sA=sa|sW=sw), where both, sa and sw values are coming from newdata object.