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SAS中Proc logisitc过程提供了很完善的logistic回归的分析功能，学习R中完成此过程只是想比较一下两个软件在完成此过程的差别。虽然有很多帖子介绍如何采用R完成logistic回归过程，但是都相对过于简单，对于以下常用细节很少涉及。

1、模型筛选方法

2、如何简单设定哑变量

3、针对分类变量，如何选取特定水平作为参考水平

4、如何简单输出OR值及置信区间

5、如何构建条件logistic回归过程

6、不同模型的预测效果比较

虽然之前有很多帖子比较SAS与R的差别，但是多是基于宏观层面的。通过比较实现某一具体过程的细微差别，估计更能体会两者的功能差异。

以下是自己学习的笔记，附有一些简单说明，供参考，希望对大家有帮助

1. library(stats)

2. help(infert) # Description of data

3. infert <- data.frame(infert)

4. str(infert) # Check type of variables

5. summary(infert) # Statistical summary

6.

7. ## Model1 Develop a simple logistic regression:

1. model1 <- glm(case ~ spontaneous+induced, data = infert, family = binomial())

2.

3. ## Model output

4. summary(model1) # Output summary information

5. confint(model1) # Output 95% CI for the coefficients

6. exp(coef(model1)) # Output OR (exponentiated coefficients)

7. exp(confint(model1)) # 95% CI for exponentiated coefficients

8. predict(model1, type="risk") # predicted values

9. residuals(model1, type="deviance") # residuals

10.

11. ## Model2 Develop a logistic regression adjusted for other potential confounders:

1. summary(model1) # Output summary information

2. confint(model1) # Output 95% CI for the coefficients

3. exp(coef(model1)) # Output OR (exponentiated coefficients)

4. exp(confint(model1)) # 95% CI for exponentiated coefficients

5. predict(model1, type="risk") # predicted values

6. residuals(model1, type="deviance") # residuals

7.

8. ## Model2 Develop a logistic regression adjusted for other potential confounders:

9. summary(model4)

10.

11. ## Model5 Conditional logistic regression

12. ## Conduct a subgroup analysis

13. ## "subset" is not aviable for "clogit"

14. ## create the subset first

15.

16. str(infert$education)

17.

18. infert1 <- subset(infert,education =="12+ yrs")

19.

20. model5 <- clogit(case ~ factor(spontaneous)+ factor(induced)+ strata(stratum),

21. data = infert1)

22. summary(model5)

23.

24. ## Model6 General logistic regression