Tutorial by Examples

Just as the random forest algorithm may be applied to regression and classification tasks, it can also be extended to survival analysis. In the example below a survival model is fit and used for prediction, scoring, and performance analysis using the package randomForestSRC from CRAN. require(rand...

survival is the most commonly used package for survival analysis in R. Using the built-in lung dataset we can get started with Survival Analysis by fitting a regression model with the survreg() function, creating a curve with survfit(), and plotting predicted survival curves by calling the predict m...

Base plot install.packages('survminer') source("https://bioconductor.org/biocLite.R") biocLite("RTCGA.clinical") # data for examples library(RTCGA.clinical) survivalTCGA(BRCA.clinical, OV.clinical, extract.cols = "admin.disease_code") -> BRCAOV.sur...