11/6/2022 0 Comments Stata mp version ic version 的区别Now there is an official StataCorp suite as well. Stata is known for its community-contributed meta-analysis. Here’s a link to themanualso you can judge for yourself. We tell you and we show you, with examples and workflows. The real problem was that we never told you how to use the reporting features. I think what’s important is another aspect of what we did. I don’t want to downplay the additions, but neither do I want to discuss them. Stata has always been strong on both, and we have added more features. The inelegant title above is trying to say (1) reports that reproduce themselves just as they were originally and (2) reports that, when run again, update themselves by running the analysis on the latest data. I specified one variable of special interest in the example, but you can specify however many you wish.Ģ.Reproducible and automatically updating reports Reported will be the coefficient and its standard error forx1. Anyway, the inference calculations are robust to those errors. Another way to think about selection is thatlassoestimatesthe variables to be selected and, as with all estimation, that is subject to error. I said earlier that they are correlated with the true variables, and they are. That’s not how the calculation is made because the variables lasso selects are not identical to the true variables that belong in the model. Then,conceptually but not actually,ywill be fit onx1and the variableslassoselects fromx2-x999. Anyway, thelassocommand is for prediction, and standard errors for the covariates it selects are not reported because they would be misleading.Ĭoncerning inference, we provide four lasso-based methods: double selection, cross-fit partialing out, and two more. If English is not your first language, by “works a treat”, I mean great. Lassowill select the covariates from thex‘s specified and fit the model on them.lassowill be unlikely to choose the covariates that belong in the true model, but it will choose covariates that are collinear with them, and that works a treat for prediction. By the way, when I say lasso, I mean lasso, elastic net, and square-root lasso, but if you want a features list, click the title. I suspect inference will be of more interest to our users, but we needed prediction to implement inference. There are two parts to our implementation of lasso: prediction and inference.
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