Package rpst. June 6, 2017
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1 Type Package Title Recursive Partitioning Survival Trees Version Date Author Maintainer Package rpst June 6, 2017 An implementation of Recursive Partitioning Survival Trees via a nodesplitting rule that builds decision tree models that reflected within-node and within-treatment responses. The algorithm aims to find the maximal difference in survival time among different treatments. License GPL-3 Depends R (>= 2.0.0) Imports plotrix, survival NeedsCompilation no Repository CRAN Date/Publication :41:08 UTC R topics documented: gen.data plot.rpst predict.rpst print.rpst rpst rpst.km Index 8 1
2 2 gen.data gen.data Generate simulated data Generate survival data for simulations. gen.data(n=100, p=10, c = 4, beta = NULL) n The number of observations. p The number of predictors of interest. c The censoring paramter to control the censoring rate. Default is 4. beta The coefficient values in the underlying regression model. Value A list with the following components: data, beta. data beta The simulated dataset. The coefficients. rpst, predict.rpst, plot.rpst.
3 plot.rpst 3 plot.rpst Produces a tree plot of a "rpst" object Produces a tree plot of a "rpst" object. ## S3 method for class 'rpst' plot(x, xlength = 1, ylength = 3, xshift = 0.5, ysegment = 6,...) x a "rpst" project. xlength xlength of the plot. ylength ylength of the plot. xshift xshift of the plot. ysegment ysegment of the plot.... additional summary arguments. rpst, print.rpst, predict.rpst. plot.rpst(tree)
4 4 predict.rpst predict.rpst Make predictions from a "rpst" object Similar to other predict methods, which returns predictions from a fitted "rpst" object. ## S3 method for class 'rpst' predict(object, newdata,...) object Output from the rpst function. newdata New data used for prediction.... additional summary arguments. Value predict.rpst returns a dataframe that contain a new column "class", which contains the class labels of each sample. print.rpst, predict.rpst, plot.rpst. ##not run## #data <- gen.data(n = 100, p = 10, c = 2) # #class = predict.rpst(tree,data$data)$class class = predict.rpst(tree,data$data)$class
5 print.rpst 5 print.rpst Print out a "rpst" object Print node and node information details of "rpst". ## S3 method for class 'rpst' print(x,...) x a "rpst" project.... additional summary arguments. rpst, predict.rpst, plot.rpst. rpst Recursive Partitioning Survival Trees Build recursive partitioning survival trees via a node-splitting rule that builds decision tree models that reflected within-node and within-treatment responses. rpst(data, datapath, maxlay = 12, minsize = 5)
6 6 rpst data datapath maxlay minsize Input dataframe, it should be set in the particular way showed in detail. The file path of the input dataframe. The maximum layer of the survival tree. The minimum sample size in the each leaf node of the survival tree. Details The input dataframe should be set in the following way: (i) The variable names are specified in the first line in the data file. (ii) The variable types are specified in the second line in the data file. The variable types can include treatment (1), survival time and censor status (2), ordered (3), binary (4), nominal (5), and unwanted (6) variables. For potential predictors, they are categorized in three types, including ordered, binary, and nominal ones. Specifically, ordered covariates include continuous and ordinal data types, binary covariates must have only two factors (they are typically coded as 0 and 1), and nominal covariates must have more than two factors. The number in the parenthesis represents each variable type. print.rpst, predict.rpst, plot.rpst.
7 rpst.km 7 rpst.km Produces KM-curve plot of a "rpst" object Produces a KM-curve plot of a "rpst" object. rpst.km(object, newdata,...) object newdata a "rpst" project. New data used for prediction and plot.... additional summary arguments. rpst, print.rpst, predict.rpst. rpst.km(tree,data$data) ##not run## #data <- gen.data(n = 100, p = 10, c = 2) # # #rpst.km(tree,data$data)
8 Index gen.data, 2 plot.rpst, 2, 3, 4 6 predict.rpst, 2 4, 4, 5 7 print.rpst, 3, 4, 5, 6, 7 rpst, 2, 3, 5, 5, 7 rpst.km, 7 8
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