plot_dependence

object

Variables to visualize (ex: c("var1", "var2", "var3)). If no grid.data 
provided, defaults to using seq(min(var), max(var)) for each continuous variables. 
For categorical, uses all categories.

vars

Input grid of values for 2-3 covariates (if 3, last variable cannot
be continuous). This is required for type="heatmap". Default=NULL.

grid.data

Threshold for PLE, ex: I(PLE&gt;thres). Used to estimate P(PLE&gt;thres) for
type="heatmap". Default is "&gt;0". Direction can be reversed and can include equality
sign (ex: "&lt;=").

grid.thres

Estimand for which to generate dependendence or heat map plots.

estimand

Additional arguments (currently ignored).

Partial dependence plots: Single Variable (marginal effect) or heat map (2 to 
3 variables).

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, for example the individual treatment effect),
(3) subgroup identification models (find subsets of patients with similar treatment effects),
and (4) parameter estimation and inference (for the overall population and discovered subgroups).
These tools can directly feed into stratified medicine algorithms including PRISM
(patient response identifiers for stratified medicine; Jemielita and Mehrotra (2019)
<arXiv:1912.03337>. PRISM is a flexible and general framework which accepts
user-created models/functions. This package is in beta and will be continually updated.

plot_dependence: Partial dependence plots: Single Variable (marginal effect) or heat map (2 to 3 variables).

Description

Usage

Arguments

Value

References