sim_outnode

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

Estimation and display of various types of population attributable fraction and impact fractions.  As well as the usual calculations of attributable fractions and impact fractions, functions are provided for attributable fraction nomograms and fan plots, continuous exposures, for pathway specific population attributable fractions, and for joint, average and sequential population attributable fractions.

John Ferguson

graphPAF

Estimating and Displaying Population Attributable Fractions

sim_outnode function

<dl><dt>data</dt>
<dd>Data frame. A dataframe containing the original variables used for fitting the models. Must contain all variables used in fitting</dd>
<dt>col_num</dt>
<dd>The indicator for the risk factor that is being eliminated</dd>
<dt>current_mat</dt>
<dd>The current value of the data frame</dd>
<dt>parent_list</dt>
<dd>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)</dd>
<dt>col_list</dt>
<dd>Column indicators for the variables in node_vec (note that node_vec is ordered from root to leaves)</dd>
<dt>model_list</dt>
<dd>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</dd></dl>

Arguments

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

<dl>

<dt>data</dt>
<dd>Data frame. A dataframe containing the original variables used for fitting the models. Must contain all variables used in fitting</dd>


<dt>col_num</dt>
<dd>The indicator for the risk factor that is being eliminated</dd>


<dt>current_mat</dt>
<dd>The current value of the data frame</dd>


<dt>parent_list</dt>
<dd>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)</dd>


<dt>col_list</dt>
<dd>Column indicators for the variables in node_vec (note that node_vec is ordered from root to leaves)</dd>


<dt>model_list</dt>
<dd>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</dd>

</dl>

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

Description

Usage

Value

Arguments