Stata 数据管理统计绘图软件
快速,简单并易于使用
点击式的界面和强大,直观的命令语言让Stata使用起来快速,精确并易于使用。
所有的分析结果都可以被复制和存档，并用来出版和审查。不管您什么时候写的内容，版本控制系统确保统计程序可
继续生成同样的结果。
统计功能介绍
Stata使得大量的统计工具用于指尖
● 基本表格和总结
● 案例对照分析
● ARIMA
● ANOVA 和MANOVA
● 线性回归
● 时间序列平滑
● 多层模型
● 生存分析
● 动态面板数据回归
● 结构方程建模
● 二进制，计数和审查结果
● ARCH
■ 标准方法，如■ 高级方法，如
● 多重替代法
● 调查数据
● Treatment effects
● 精确统计
● 贝叶斯分析
● ……
Lasso is a machine-learning technique used for model selection, prediction, and inference.
The new lasso command selects “optimal” predictors for continuous, count, and binary outcomes using deviances from linear, Poisson, logit, or probit regression models.
For instance, if you type
. lasso linear y x1-x500
lasso will select a subset of the specified covariates—say, x2, x10, x11, and x21. You can then use the standard predict command to obtain predictions of y.
If you instead have a binary or count outcome, you can use lasso logit, lasso probit, or lasso poisson in the same way. And if you prefer to select variables using the elastic net or square-root lasso method, you can use the elasticnet or sqrtlasso command.
Sometimes, variable selection or prediction is the final goal of lasso. Other times, you are interested in estimating and testing coefficients. Stata 16 provides 11 commands that allow you to estimate coefficients, standard errors, and confidence intervals and to perform tests for variables of interest while using lasso methods to select from among potential control variables. The commands are
dsregress, dslogit, dspoisson, poregress, pologit, popoisson, poivpoisson, xporegress, xpologit,
xpopoisson, and xpoivregress.
The ds commands perform double-selection lasso, the po commands perform partialing-out lasso, and the xpo commands perform cross-fit partialing-out lasso. They do this for models with continuous, binary, and count outcomes. They can even handle endogenous covariates in models for continuous outcomes. The literature currently discusses many methods for lasso-based inference. We make some of these methods available so that researchers can select their favorite. In fact, there are even more lasso-based methods of inference in the literature, and often researchers may use the tools available in lasso, sqrtlasso, and elasticnet to implement other methods.
The lasso and elasticnet commands are standard lasso tools often requested for variable selection and prediction. The lasso tools for inference implement newer methods developed primarily by econometricians. However, these inference methods will be popular in all disciplines because they provide a method for testing and interpreting coefficients on variables of interest.
Users can easily learn all about the lasso features in the new Lasso Reference Manual.
In Stata 16, you can embed and execute Python code from within Stata. Stata's new python command allows you to easily call Python from Stata and output Python results within Stata.
You can invoke Python interactively or in do-files and ado-files so that you can leverage Python's extensive language features. You can also execute a Python script file (.py) directly through Stata.
In addition, we introduced the Stata Function Interface (sfi) Python module, which provides a bi-directional connection between Stata and Python. This module lets you access Stata's current dataset, frames, macros, scalars, matrices, value labels, characteristics, global Mata matrices, and more.
All of this means that you can now use any Python package directly within Stata. For instance, you can use Matplotlib to draw 3-dimensional graphs. You can use NumPy for numerical computations. You can use Scrapy to scrape data from the web. You can access additional machine-learning techniques such as neural networks and support vector machines through TensorFlow and scikit-learn. And much more.
Finally, Stata’s Do-file Editor now includes syntax highlighting for the Python language.
While advanced users and programmers might be most likely to take advantage of Python integration, the availability of Python within Stata will excite many more users in all disciplines.
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