AMMBR II

1. AMMBR II Gerrit Rooks

2. Today • Introduction to Stata • Files / directories • Stata syntax • Useful commands / functions • Logistic regression analysis with Stata • Estimation • GOF • Coefficients • Checking assumptions

3. Stata file types • .ado • programs that add commands to Stata • .do • Batch files that execute a set of Stata commands • .dta • Data file in Stata’s format • .log • Output saved as plain text by thelog using command

4. The working directory • The working directory is the default directory for any file operations such as using & saving data, or logging output • cd “d:\my work\”

5. Saving output to log files • Syntax for the log command • log using filename [, append replace [smcl|text]] • To close a log file • log close

6. Using and saving datasets • Load a Stata dataset • use d:\myproject\data.dta, clear • Save • save d:\myproject\data, replace • Using change directory • cd d:\myproject • Use data, clear • save data, replace

7. Entering data • Data in other formats • You can use SPSS to convert data • You can use the infile and insheet commands to import data in ASCII format • Entering data by hand • Type edit or just click on the data-editor button

8. Do-files • You can create a text file that contains a series of commands • Use the do-editor to work with do-files • Example I

9. Adding comments • // or * denote comments stata should ignore • Stata ignores whatever follows after /// and treats the next line as a continuation • Example II

10. A recommended structure //if a log file is open, close it capture log close //dont'pause when output scrolls off the page set more off //change directory to your working directory cd d:\myproject //log results to file myfile.log log using myfile, replace text // * myfile.do-written 7 feb 2010 to illustrate do-files // your commands here //close the log file log close

11. Serious data analysis • Ensure replicability use do+log files • Document your do-files • What is obvious today, is baffling in six months • Keep a research log • Diary that includes a description of every program you run • Develop a system for naming files

12. Serious data analysis • New variables should be given new names • Use labels and notes • Double check every new variable • ARCHIVE

13. The Stata syntax • Regress y x1 x2 if x3 <20, cluster(x4) • Regress = Command • Whataction do you want to performed • y x1 x2 = Names of variables, files orotherobjects • Onwhatthings is the commandperformed • if x3 <20 = Qualifieronobservations • Onwhichobservationsshould the commandbeperformed • , cluster(x4) = Options • What special thingsshouldbedone in executing the command

14. Examples • tabulate smoking race if agemother > 30, row • Example of the if qualifier • sum agemother if smoking == 1 & weightmother < 100

15. Elements used for logical statements

16. Missing values • Automatically excluded when Stata fits models, they are stored as the largest positive values • Beware • The expression ‘age > 65’ can thus also include missing values • To be sure type: ‘age > 65 & age != .’

17. Selecting observations • drop variable list • Keep variable list • drop if age < 65

18. Creating new variables • generate command • generate age2 = age * age • generate • see help function • !!sometimes the command egen is a useful alternative, f.i. • egen meanage = mean(age)

19. Useful functions

20. Replace command • replace has the same syntax as generate but is used to change values of a variable that already exists • gen age_dum = . • replace age = 0 if age < 5 • replace age = 1 if age >=5

21. Recode • Change values of exisiting variables • Change 1 to 2 and 3 to 4: recode origvar (1=2)(3=4), gen(myvar1) • Change missings to 1: recode origvar (.=1), gen(origvar)

22. Logistic regression • Lets use a set of data collected by the state of California from 1200 high schools measuring academic achievement. • Our dependent variable is called hiqual. • Our predictor variable will be a continuous variable called avg_ed, which is a continuous measure of the average education (ranging from 1 to 5) of the parents of the students in the participating high schools.

23. OLS in Stata

25. Logistic regression in Stata

27. Multiple predictors

28. Model fit: the likelihood ratio test

29. Model fit: LR test

30. Pseudo R2: proportionalchange in LL

31. Classification Table

32. Classification Table

33. Interpreting coefficients: significance

34. Comparing models

35. After the full model and storage, estimate nested model

36. Likelihood ratio test

37. Interpretation of coefficients: direction

38. Interpretation of coefficients: direction

39. Interpretation of coefficients: Magnitude

40. Interpretation of coefficients: Magnitude

41. the assumptions of logistic regression • The true conditional probabilities are a logistic function of the independent variables. • No important variables are omitted. • No extraneous variables are included. • The independent variables are measured without error. • The observations are independent. • The independent variables are not linear combinations of each other.

42. Hosmer & Lemeshow Test divides sample in subgroups, checks whether difference between observed and predicted is about equal in these groups Test should not be significant (indicating no difference)

43. Hosmer & Lemeshow Average Probability In j th group

44. First logistic regression

45. Then postestimation command

46. Specification error

47. Including interaction term helps

48. Ok now

49. Ok now

50. Multicollinearity