pgf: A parametric g formula prediction function

In ainaimi/pgf: Package for Fitting the Parametric G Formula

Description

This function allows you simulate the follow-up of a complex longitudinal dataset under different intervention regimes.

Usage

pgf(ii, mc_data, length, randomization = NULL, exposure = NULL,
  censoring = NULL)

Arguments

ii	A numeric sequence (e.g., 1:5000) indicating monte carlo resample size.
mc_data	The original re-sampled baseline data.
length	A numeric length of follow-up to be simulated.
randomization	A numeric randomization. "NULL" indicates natural course, "1" indicates set randomization to treatment, "0" indicates set randomization to placebo. Defaults to "NULL".
exposure	A numeric exposure. "NULL" indicates natural course, "1" indicates set to exposed, "0" indicates set to unexposed.
censoring	A numeric censoring. "NULL" indicates no censoring, and "natural" indicates censoring as in empirical data. Defaults to "NULL".

Examples

pgf()

ainaimi/pgf documentation built on May 10, 2019, 8 a.m.