如何使用log-likelihood比较两个cox回归模型的好坏？

可以使用log-likelihood比较两个cox回归模型的好坏，具体方法是计算每个模型的log-likelihood值，然后比较两个模型的log-likelihood值大小，值越大的模型拟合效果越好。

相关问题

在R中如何使用likelihood ratio test来比较两个模型的log-likelihood值，有没有示例？

可以使用lrtest()函数来进行likelihood ratio test，该函数需要输入两个模型的log-likelihood值，示例代码如下：

# 假设有两个模型，分别为model1和model2
# 分别计算两个模型的log-likelihood值
logLik_model1 <- logLik(model1)
logLik_model2 <- logLik(model2)

# 使用lrtest()函数进行likelihood ratio test
lrtest_result <- lrtest(model1, model2)

# 输出结果
print(lrtest_result)

注意：以上代码仅为示例，实际使用时需要根据具体情况进行修改。

比较least squares, logit,probit model的maximised log-likelihood

Least squares, logit, and probit models are different types of regression models. Least squares is a linear regression model, while logit and probit are nonlinear regression models used for binary classification problems.

The maximized log-likelihood is a measure of how well a model fits the data. For least squares, the maximized log-likelihood is the negative sum of squared errors. For logit and probit, the maximized log-likelihood is the sum of the log-likelihood function evaluated at the estimated parameters.

Therefore, comparing the maximized log-likelihoods of these models is not straightforward as they have different forms. However, we can say that the logit and probit models are more appropriate for binary classification problems, while the least squares model is better suited for continuous response variables.