我正在研究电信流失问题，这是我的数据集。
http://www.sgi.com/tech/mlc/db/churn.data
名称 - http://www.sgi.com/tech/mlc/db/churn.names
我是生存分析的新手。鉴于训练数据，我的想法是建立一个生存模型来估计生存时间，并根据独立因素预测测试数据的流失/非流失。谁能帮我提供代码或指针关于如何解决这个问题。
准确地说，假设我的火车数据有
客户电话使用详情、计划详情、他的帐户使用期限等以及他是否流失。
使用通用分类模型，我可以预测测试数据的流失率。现在使用生存分析，我想预测测试数据的生存期。
谢谢，麦迪
If you're still interested (or for the benefit of those coming later), I've written a few guides specifically for conducting survival analysis on customer churn data using R. They cover a bunch of different analytical techniques, all with sample data and R code.
Basic survival analysis: http://daynebatten.com/2015/02/customer-churn-survival-analysis/
Basic cox regression: http://daynebatten.com/2015/02/customer-churn-cox-regression/
Time-dependent covariates in cox regression: http://daynebatten.com/2015/12/survival-analysis-customer-churn-time-varying-covariates/
Time-dependent coefficients in cox regression: http://daynebatten.com/2016/01/customer-churn-time-dependent-coefficients/
Restricted mean survival time (quantify the impact of churn in dollar terms): http://daynebatten.com/2015/03/customer-churn-restricted-mean-survival-time/
Pseudo-observations (quantify dollar gain/loss associated with the churn effects of variables): http://daynebatten.com/2015/03/customer-churn-pseudo-observations/
Please forgive the goofy images.
以下是一些帮助您入门的代码：
一、读取数据
nm <- read.csv("http://www.sgi.com/tech/mlc/db/churn.names",
skip=4, colClasses=c("character", "NULL"), header=FALSE, sep=":")[[1]]
dat <- read.csv("http://www.sgi.com/tech/mlc/db/churn.data", header=FALSE, col.names=c(nm, "Churn"))
用于Surv()设置生存对象进行建模
library(survival)
s <- with(dat, Surv(account.length, as.numeric(Churn)))
拟合 cox 比例风险模型并绘制结果
model <- coxph(s ~ total.day.charge + number.customer.service.calls, data=dat[, -4])
summary(model)
plot(survfit(model))
添加层：
model <- coxph(s ~ total.day.charge + strata(number.customer.service.calls <= 3), data=dat[, -4])
summary(model)
plot(survfit(model), col=c("blue", "red"))
