Stratified and Personalised Models Based on Model-Based Trees and Forests
Plot for a given logistic regression model (glm with binomial family) ...
Table of coefficients for survreg model
Survival plot for a given coxph model with one binary covariate.
Add model information to a personalised-model-ctree
Fit function when model object is given
Prepare input for ctree/cforest from input of pmtree/pmforest
Density plot for a given lm model with one binary covariate.
Extract log-Likelihood
Panel-Generator for Visualization of pmtrees
Objective function of personalised models
Objective function of a given pmtree
Objective function
Check if model has only one factor covariate.
Compute model-based forest from model.
Personalised model
Test if personalised models improve upon base model.
Compute model-based tree from model.
pmtree predictions
Methods for pmtree
Residual sum of squares
Survival plot for a given survreg model with one binary covariate.
Variable Importance for pmforest
Model-based trees for subgroup analyses in clinical trials and model-based forests for the estimation and prediction of personalised treatment effects (personalised models). Currently partitioning of linear models, lm(), generalised linear models, glm(), and Weibull models, survreg(), is supported. Advanced plotting functionality is supported for the trees and a test for parameter heterogeneity is provided for the personalised models. For details on model-based trees for subgroup analyses see Seibold, Zeileis and Hothorn (2016) <doi:10.1515/ijb-2015-0032>; for details on model-based forests for estimation of individual treatment effects see Seibold, Zeileis and Hothorn (2017) <doi:10.1177/0962280217693034>.