Dear group, Please allow me to ask a naive question and pardon if it is qualified as stupid question.
I am using party package to classify covariates and predict distribution of survival times for the classified variables. Typically I have a matrix of covariates (columns) including outcome data (overall survival in months, censor status) and other covariates I want to split in tree (such as treatment dose etc. ) . Rows are patients (~1000 patients). Now similarly I have many such matrices (4K) with completely different set of covariates but identical outcome data and patients (in rows). i cannot combine all data into a giant matrix,because these covariates are totally independent. Currently I am running this model in a loop and storing the tree and parsing the tree structure. My question is, is there some testing method to choose or rank these 4K trees such that I can select each tree from top to bottom. I know each tree is important in its own way. If selection based on significance is required, then is there any other way instead of conditional inference tree , that partitions data but will also carry some significance to choose from. Thanks! . ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
