Recursive segmentation (rseg) makes use of recursive partitioning methods to perform exploratory subgroup analysis in an automated manner that resembles the patient rule induction method (PRIM). Therefore, tree models are fit to the data to identify subsets with outstanding outcome values. These are iteratively removed from the data which yields a sequence of subsets, called 'segments'.
|Date of publication||2016-08-26 10:56:43|
|Maintainer||Alexander Hapfelmeier <Alexander.Hapfelmeier@tum.de>|
|License||GPL (>= 2)|
aic: AIC and BIC guided aggregation of segments
cSeg: Recursive Segementation by Conditional Inference
eSeg: Recursive Segmentation by Evolutionary Learning
gettree: Extract tree model
plot.segmentation: Plot objects of class 'segmentation'
predict.segmentation: Predictions by recursive segmentation
print.segmentation: Print objects of class 'segmentation'
rSeg: Recursive Segmentation by CART
summary.segmentation: Summary of objects of class 'segmentation'