为什么是这三个自变量而不是cd40、age、wtkg、homo、race、gender？我这不是在纠错，如果你认为你是对的请阐述原因。如果你认为你错了请给出修改的代码

非常抱歉，我理解错了你的问题。我们需要对所有自变量进行log-log图的检验，以检验它们是否符合Weibull模型的假设。

下面是对所有自变量进行log-log图检验的示例代码：

# 加载survival包
library(survival)

# 导入数据
data(lung)

# 拟合Weibull模型
weibull_fit <- survreg(Surv(time, status) ~ cd40 + age + wtkg + homo + race + gender, data = lung, dist = "weibull")

# 提取残差和估计的累积分布函数
residuals <- resid(weibull_fit, type = "dfbetas")
fitted_values <- pweibull(fitted(weibull_fit), shape = weibull_fit$scale, scale = 1)

# 绘制log-log图
par(mfrow = c(2, 3))
plot(log(fitted_values), log(residuals[, 1]), xlab = "log(fitted values)", ylab = "log(residuals)", main = "cd40")
plot(log(fitted_values), log(residuals[, 2]), xlab = "log(fitted values)", ylab = "log(residuals)", main = "age")
plot(log(fitted_values), log(residuals[, 3]), xlab = "log(fitted values)", ylab = "log(residuals)", main = "wtkg")
plot(log(fitted_values), log(residuals[, 4]), xlab = "log(fitted values)", ylab = "log(residuals)", main = "homo")
plot(log(fitted_values), log(residuals[, 5]), xlab = "log(fitted values)", ylab = "log(residuals)", main = "race")
plot(log(fitted_values), log(residuals[, 6]), xlab = "log(fitted values)", ylab = "log(residuals)", main = "gender")

在这个例子中，我们对lung数据集中的cd40、age、wtkg、homo、race、gender这六个自变量进行了log-log图检验。在log-log图中，我们对每个自变量分别绘制了一张图，用来检验Weibull模型假设的符合程度。如果所有自变量的log-log图都显示出了近似直线的模式，说明这些自变量都符合Weibull模型的假设。如果出现弯曲或其他非线性的形式，则说明数据不符合Weibull模型的假设。