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model4you (version 0.9-5)

Stratified and Personalised Models Based on Model-Based Trees and Forests

Description

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) ; for details on model-based forests for estimation of individual treatment effects see Seibold, Zeileis and Hothorn (2017) .

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Version

Install

install.packages('model4you')

Monthly Downloads

556

Version

0.9-5

License

GPL-2 | GPL-3

Maintainer

Heidi Seibold

Last Published

February 19th, 2020

Functions in model4you (0.9-5)

binomial_glm_plot

Plot for a given logistic regression model (glm with binomial family) with one binary covariate.
logLik.pmtree

Extract log-Likelihood
coxph_plot

Survival plot for a given coxph model with one binary covariate.
lm_plot

Density plot for a given lm model with one binary covariate.
.prepare_args

Prepare input for ctree/cforest from input of pmtree/pmforest
.modelfit

Fit function when model object is given
node_pmterminal

Panel-Generator for Visualization of pmtrees
objfun

Objective function
.add_modelinfo

Add model information to a personalised-model-ctree
coeftable.survreg

Table of coefficients for survreg model
pmtree

Compute model-based tree from model.
one_factor

Check if model has only one factor covariate.
pmforest

Compute model-based forest from model.
predict.pmtree

pmtree predictions
objfun.pmodel_identity

Objective function of personalised models
print.pmtree

Methods for pmtree
survreg_plot

Survival plot for a given survreg model with one binary covariate.
pmodel

Personalised model
pmtest

Test if personalised models improve upon base model.
varimp.pmforest

Variable Importance for pmforest
objfun.pmtree

Objective function of a given pmtree
rss

Residual sum of squares