sim_outnode: Internal: Simulate from the post intervention distribution...

In graphPAF: Estimating and Displaying Population Attributable Fractions

Internal: Simulate from the post intervention distribution corresponding to eliminating a risk factor

Description

Internal: Simulate from the post intervention distribution corresponding to eliminating a risk factor

Usage

sim_outnode(data, col_num, current_mat, parent_list, col_list, model_list)

Arguments

data	Data frame. A dataframe containing the original variables used for fitting the models. Must contain all variables used in fitting
col_num	The indicator for the risk factor that is being eliminated
current_mat	The current value of the data frame
parent_list	A list. The ith element is the vector of variable names that are direct causes of ith variable in node_vec (Note that the variable names should be columns in data)
col_list	Column indicators for the variables in node_vec (note that node_vec is ordered from root to leaves)
model_list	List. A list of fitted models corresponding for the outcome variables in node_vec, with parents as described in parent_vec. This list must be in the same order as node_vec and parent_list. Models can be linear (lm), logistic (glm) or ordinal logistic (polr). Non-linear effects of variables (if necessary) should be specified via ns(x, df=y), where ns is the natural spline function from the splines library

Value

An updated data frame (a new version of current_mat) with new columns simulated for variables that the risk factor causally effects.

graphPAF documentation built on May 29, 2024, 10:21 a.m.