Examples of how Stata fails when trying to catch up
Tools in statistics improve constantly. In some of these areas, Stata is good. In others, Stata is simply not catching up, but still pretending to do.
I recently maintained that Stata's functions for literate programming are of little use once we compare it with what is available for free in R with the knitr package. (And one can use R with RStudio and knitr as an excellent platform for running Stata functions. Please note that some programmers are improving Stata's functions in this field, though the gap to R's knitr is still wide.)
But take something relevant for more people: SEM. Stata is extremely slow and is frankly speaking unusable for serious SEM. Again, the free R is better. I see some users suggest Stata should buy Mplus (the best in the field), which I believe is unrealistic, but it does indicate that Stata fails in SEM.
Moreover, StataCorp as a publisher allows a book to present incorrect claims about model fit in SEM. A recently published book presents a SEM model that clearly fails, but the authors maintain the model fits the data. (If I remember correctly, the authors maintained that a RMSEA = .10 indicates good fit. It certainly doesn't and it easy to see what is missing in the model.) I'm not criticising the authors, they are obviously unfamiliar with SEM and SEM occupies only a chapter in the book. But StataCorp is the publisher, what happened during review and quality control?
Finally, StataCorp recently gave a video-based lecture in Bayes. Again, the free R is much better, and again, Mplus may for many users be the best. But my concern is another. Originally, Stata allowed users only one Bayesian chain. Never run Bayes with one chain only. Use at least two (I use four). But in the video, Stata is back to lecturing in Bayes with only one chain. Again, where is the quality control in StataCorp? I don't think we can claim that since this was an introduction only, we can skip one of the most essential parts of Bayesian modelling.
I love Stata for what it does best. None of the above, and not graphics (the free R is again much better). I love Stata for its language and its data management. And whenever I code in R I miss Stata's simplicity and the local macros.
I think Stata is in trouble. I think Stata has the wrong approach when Bayes is introduced by programmers who do not first learn modern Bayes. I think the inabilities of Stata to run SEM speaks volumes. And I think there is too little quality control in Stata for books and maybe for videos on modern techniques.
It's one of those unhappy love affairs. It's not that I haven't tried. I fell in love with Stata; Stata is easy to learn, with a beautiful, human-like language. But that love is not mutual. Stata doesn't care about what I need. Anyone wanting to use SEM, Bayes, or literate programming should look elsewhere.
Or, could that change?
http://staffblogs.le.ac.uk/bayeswith...13/stata-vs-r/
Tools in statistics improve constantly. In some of these areas, Stata is good. In others, Stata is simply not catching up, but still pretending to do.
I recently maintained that Stata's functions for literate programming are of little use once we compare it with what is available for free in R with the knitr package. (And one can use R with RStudio and knitr as an excellent platform for running Stata functions. Please note that some programmers are improving Stata's functions in this field, though the gap to R's knitr is still wide.)
But take something relevant for more people: SEM. Stata is extremely slow and is frankly speaking unusable for serious SEM. Again, the free R is better. I see some users suggest Stata should buy Mplus (the best in the field), which I believe is unrealistic, but it does indicate that Stata fails in SEM.
Moreover, StataCorp as a publisher allows a book to present incorrect claims about model fit in SEM. A recently published book presents a SEM model that clearly fails, but the authors maintain the model fits the data. (If I remember correctly, the authors maintained that a RMSEA = .10 indicates good fit. It certainly doesn't and it easy to see what is missing in the model.) I'm not criticising the authors, they are obviously unfamiliar with SEM and SEM occupies only a chapter in the book. But StataCorp is the publisher, what happened during review and quality control?
Finally, StataCorp recently gave a video-based lecture in Bayes. Again, the free R is much better, and again, Mplus may for many users be the best. But my concern is another. Originally, Stata allowed users only one Bayesian chain. Never run Bayes with one chain only. Use at least two (I use four). But in the video, Stata is back to lecturing in Bayes with only one chain. Again, where is the quality control in StataCorp? I don't think we can claim that since this was an introduction only, we can skip one of the most essential parts of Bayesian modelling.
I love Stata for what it does best. None of the above, and not graphics (the free R is again much better). I love Stata for its language and its data management. And whenever I code in R I miss Stata's simplicity and the local macros.
I think Stata is in trouble. I think Stata has the wrong approach when Bayes is introduced by programmers who do not first learn modern Bayes. I think the inabilities of Stata to run SEM speaks volumes. And I think there is too little quality control in Stata for books and maybe for videos on modern techniques.
It's one of those unhappy love affairs. It's not that I haven't tried. I fell in love with Stata; Stata is easy to learn, with a beautiful, human-like language. But that love is not mutual. Stata doesn't care about what I need. Anyone wanting to use SEM, Bayes, or literate programming should look elsewhere.
Or, could that change?
http://staffblogs.le.ac.uk/bayeswith...13/stata-vs-r/
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