In DongqiangZeng0808/Blasso: Using LASSO and Bootstrapping algorithm to evaluate predictive value of model

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures",
  out.width = "100%"
)

Blasso： Integrating LASSO regression and bootstrapping algorithm to find best prognostic or predictive feature


The package is not yet on CRAN. You can install from Github:

if (!requireNamespace("devtools", quietly = TRUE)) 
  install.packages("devtools")
if (!requireNamespace("Blasso", quietly = TRUE)) 
  devtools::install_github("DongqiangZeng0808/Blasso")

Loading packages and main function in the package:

library(Blasso) 
help("best_predictor_cox")
help("best_predictor_binomial")

Supplementary data

data("target")
head(target)

data("features")
features[1:5,1:5]

Usage-1: Cox-regression model

res<-best_predictor_cox(target_data   = target, 
                        features      = features, 
                        status        = "status",
                        time          = "time",
                        nfolds        = 10,
                        permutation   = 300,
                        show_progress = FALSE)
head(res$res, n = 10)

Usage-2: Binomial model

res<-best_predictor_binomial(target_data   = target, 
                             features      = features,
                             response      = "status",
                             nfolds        = 10,
                             permutation   = 300,
                             show_progress = FALSE)
head(res$res, n = 10)

Session Info

sessionInfo()

References

Zeng D, Ye Z, Wu J, Zhou R, Fan X, Wang G, Huang Y, Wu J, Sun H, Wang M, Bin J, Liao Y, Li N, Shi M, Liao W. Macrophage correlates with immunophenotype and predicts anti-PD-L1 response of urothelial cancer. Theranostics 2020; 10(15):7002-7014. doi:10.7150/thno.46176

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Contact: E-mail any questions to [e-mail]

DongqiangZeng0808/Blasso documentation built on Oct. 12, 2022, 9:44 p.m.