NetCourseTM 152: Advanced Stata Programming
- Content:
-
This course teaches you how to create and debug new commands that are
indistinguishable from the commands in Stata. It is assumed that you
know why and when to program and to some extent how. You will learn
how to parse both standard and nonstandard Stata syntax using the
intuitive syntax command, how to manage and process saved
results, how to process by groups, and more.
- Course leaders:
- Kerry Kammire, technical services analyst at StataCorp
- Jennifer Rolfes, technical services representative at StataCorp
- Course length:
- 7 weeks (5 lectures)
- Dates:
- January 22–March 12, 2010
- Prerequisites:
- Stata 11, installed and working
- Course content of NetCourse 151 or equivalent knowledge
- Internet web browser, installed and working
(course is platform independent)
- Price:
- $150.00
Can’t wait for the scheduled course?
Enroll in NCNow152
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Course content
Lecture 1: Parsing Stata syntax/Stata programming basics
- Review of Stata’s programming features
- Parsing
- Parsing options
- Parsing complicated syntax
- Aside on subprograms
Lecture 2: Parsing Stata syntax, continued/Quotes, returned results, and subsamples
- Quotes
- Development continues
- Temporary variables
- Development continues
- An aside concerning r()
- Programming the formulas
- Putting it together
Lecture 3: Using scalars and macros and introduction to low-level parsing
- What you must learn
- Scalars
- Binary accuracy
- Accuracy of macros versus scalars
- Converting a program from macros to scalars
- Handling by() options
- Sorting
- Low-level parsing
- Programming immediate commands
- Rewriting mytt in terms of mytti
- Parsing new variables
There is a week break between lectures 3 and 4 to allow more time for those
who may fall behind and to allow for more discussion from the participants.
Lecture 4: Returning results and writing estimation commands
- Where are we?
- Saved results
- What can be returned in r()?
- Referring to returned results in other programs
- Referring to returned results in the program that sets them
- Other types of returned values: s() and e()
- S-class returned values
- E-class returned results
- Writing postestimation commands
- Writing an estimation (e-class) command
- An alternative estimation command outline
- Writing estimation commands from first principles
- Writing estimation commands via maximum likelihood
Lecture 5: List processing, controlling program output, and naming conventions
- Restricting commands to the relevant subsample
- Which is better: marksample or mark?
- Programming by varlist
- Lists
- Creating lists
- Stepping through list elements one by one
- Deleting elements from lists
- Adding elements to lists
- Macro vectors
- Parsing revisited: gettoken
- Quietly blocks
- The relation between capture and quietly
- Capture blocks
- Naming conventions
- Program naming convention
- Calling convention
- Version control
- Conclusion
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