首页fit <- CoxBoost(train[,'month'], train[,'OS'], as.matrix(train[,-c(0:2)]), stepno=200, sf.scheme=c("sigmoid"), criterion="hscore") summary(fit) data = read.csv("val.csv",sep = ",", header = T) validation = data step.logplik = predict(fit, newdata = as.matrix(validation[,-c(1:2)]), newtime = validation[,'month'], newstatus = validation[,'OS'], as.step = 0:300, typr = "logplik") step.logplik针对这个代码如何继续求brier score

fit <- CoxBoost(train[,'month'], train[,'OS'], as.matrix(train[,-c(0:2)]), stepno=200, sf.scheme=c("sigmoid"), criterion="hscore") summary(fit) data = read.csv("val.csv",sep = ",", header = T) validation = data step.logplik = predict(fit, newdata = as.matrix(validation[,-c(1:2)]), newtime = validation[,'month'], newstatus = validation[,'OS'], as.step = 0:300, typr = "logplik") step.logplik针对这个代码如何继续求brier score

时间: 2023-11-27 14:06:19 浏览: 151

easy-fit:直接从JS轻松解析.FIT文件

注意：该库当前正在重写。容易安装直接从JS轻松解析您的.FIT文件。写在ES6中。安装 $ npm install easy-fit --save 如何使用在文件夹中查看： // Require the module var EasyFit = require ( './../dist/easy-fit.js' ) . default ; // Read a .FIT file var fs = require ( 'fs' ) ; fs . readFile ( './example.fit' , function ( err , content ) { // Create a EasyFit instance (options argument is optional) var easyFit = new EasyFit ( { force : true

在计算Brier Score之前，需要对`step.logplik`进行处理来获得模型预测的生存概率。可以使用以下代码： ``` probs <- exp(-step.logplik/365.25) ``` 这里将`step.logplik`除以365.25，是因为`step.logplik`的单位是天，而Brier Score的计算需要的是年。然后，使用以下代码计算Brier Score： ``` library(survival) brier <- survConcordance(y=validation$OS, x=probs, weights=rep(1/nrow(validation), nrow(validation)), type="brier") ``` 其中，`validation$OS`是事件发生的指示变量，`probs`是模型预测的生存概率，`rep(1/nrow(validation), nrow(validation))`是样本权重，`type="brier"`指定计算Brier Score。最终，`brier`变量的值即为Brier Score。

阅读全文