plot_importance

Importance is currently based on the PRISM filter model. For elastic net (filter_glmnet).
variables with non-zero coefficients are shown. For random forest variable importance 
(filter_ranger), variables are sorted by their p-values, and "top_n" will show only the
"top_n" most importance variables (based on p-values).

A toolkit for stratified medicine, subgroup identification, and precision medicine.
Current tools include (1) filtering models (reduce covariate space), (2) patient-level estimate
models (counterfactual patient-level quantities, such as the conditional average treatment effect),
(3) subgroup identification models (find subsets of patients with similar treatment effects),
and (4) treatment effect estimation and inference (for the overall population and discovered
subgroups). These tools can be customized and are directly used in PRISM
(patient response identifiers for stratified medicine; Jemielita and Mehrotra 2019
<doi:10.48550/arXiv.1912.03337>).

Thomas Jemielita

StratifiedMedicine

Stratified Medicine

plot_importance function

<dl><dt>object</dt>
<dd>PRISM object</dd>
<dt>top_n</dt>
<dd>Show top_n variables only, default=NULL (show all)</dd>
<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd></dl>

Arguments

Importance Plot: Visualize relative importance of variables — plot_importance

<dl>

<dt>object</dt>
<dd>PRISM object</dd>


<dt>top_n</dt>
<dd>Show top_n variables only, default=NULL (show all)</dd>


<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd>

</dl>

plot_importance: Importance Plot: Visualize relative importance of variables

Description

Usage

Value

Arguments

Examples