Contents
What is the function of Lasso in Stata?
Lasso, elastic net, and square-root lasso are designed for model selection and prediction. Stata’s lasso, elasticnet, and sqrtlasso commands implement these methods. lasso and elasticnet fit continuous, binary, and count outcomes, while sqrtlasso fits continuous outcomes.
How does lassologit treat PSI as a row vector?
Note that lassologit treats Psi as a row vector. N number of observations lassologit uses coordinate descent algorithms for logistic lasso as described in Friedman 2010, Section 3. Penalization level: choice of lambda Penalized regression methods rely on tuning parameters that control the degree and type of penalization.
What are the two loss measures in lassologit?
The prediction (classification) performance is assessed based on loss measures. cvlassologit offers two loss measures: deviance and miss-classification error (defined below). For more information, see cvlasso (for the linear case).
How is the random assignment in Stata reproducible?
The assignment of each observation in sample to 1 or 2 is random, but the rseed option makes the random assignment reproducible. The one-way tabulation of sample produced by tabulate verifies that sample contains the requested 75%–25% division.
Are there different versions of the lasso for linear models?
There are different versions of the lasso for linear and nonlinear models. Versions of the lasso for linear models, logistic models, and Poisson models are available in Stata 16. We discuss only the lasso for the linear model, but the points we make generalize to the lasso for nonlinear models.
What’s the difference between lasso and elastic net?
Lasso was originally an acronym for “least absolute shrinkage and selection operator”. Today, lasso is considered a word and not an acronym. Lasso is used for prediction, for model selection, and as a component of estimators to perform inference. Lasso, elastic net, and square-root lasso are designed for model selection and prediction.
When do you use lasso for covariate selection?
When you use the lasso for covariate selection, covariates with estimated coefficients of zero are excluded, and covariates with estimated coefficients that are not zero are included. That the number of potential covariates p can be greater than the sample size n is a much discussed advantage of the lasso.
Is the Stata software a trademark of StataCorp?
NetCourseNow is a trademark of StataCorp LLC. Other brand and product names are registered trademarks or trademarks of their respective companies. For copyright information about the software, type help copyright within Stata. The suggested citation for this software is StataCorp. 2021. Stata: Release 17. Statistical Software.