使用curve()绘制Survreg的生存率和危险函数

本文介绍了使用curve()绘制Survreg的生存率和危险函数的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我有以下survreg模型:

I have the following survreg model:

Call:
survreg(formula = Surv(time = (ev.time), event = ev) ~ age,
    data = my.data, dist = "weib")
             Value Std. Error    z        p
(Intercept) 4.0961     0.5566 7.36 1.86e-13
age         0.0388     0.0133 2.91 3.60e-03
Log(scale)  0.1421     0.1208 1.18 2.39e-01
Scale= 1.15

Weibull distribution

我想根据上述估算值绘制危险函数和生存率函数.
我不想使用predict()或pweibull()(如此处参数生存或此处.

I would like to plot the hazard function and the survival function based on the above estimates.
I don't want to use predict() or pweibull() (as presented here Parametric Survival or here SO question.

我想使用curve()函数.有什么想法我可以做到这一点吗? survreg的Weibull函数似乎使用了比平常其他的比例和形状定义(例如rweibull不同).

I would like to use the curve() function. Any ideas how I can accomplish this? It seems the Weibull function of the survreg uses other definitions of scale and shape than the usual (and different that for example rweibull).

更新:我想我真正需要它来表达危险/生存的函数是估计值Intercept，age (+ other potential covariates)，Scale的函数，而不使用任何现成的*weilbull函数.

UPDATE: I guess what I really require it to express hazard / survival as a function of the estimates Intercept, age (+ other potential covariates), Scale without using any ready made *weilbull function.

推荐答案

您的参数是:

scale=exp(Intercept+beta*x)，假设年龄为40岁

scale=283.7

您的形状参数是模型输出的比例的倒数

your shape parameter is the reciprocal of the scale that the model outputs

shape=1/1.15

那么危险是:

curve((shape/scale)*(x/scale)^(shape-1), from=0,to=12,ylab=expression(hat(h)(t)), col="darkblue",xlab="t", lwd=5)

累积危害函数为:

curve((x/scale)^(shape), from=0,to=12,ylab=expression(hat(F)(t)), col="darkgreen",xlab="t", lwd=5)

生存函数是1-累积危害函数，因此:

The Survival function is 1-the cumulative hazard function, so:

curve(1-((x/scale)^(shape)), from=0,to=12,ylab=expression(hat(S)(t)), col="darkred",xlab="t", lwd=5, ylim=c(0,1))

还要检出eha软件包以及功能hweibull和Hweibull

Also check out the eha package, and the function hweibull and Hweibull

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intercept