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Multiple datasets in memory in Stata 16
You can now load multiple datasets into memory. You type
. use people
and people.dta is loaded into memory. Next, you type
. frame create counties
. frame counties: use counties
and you have two datasets in memory. people.dta is in the frame named default, and counties.dta is in the frame named counties. Your current frame is still default. Most Stata commands use the data in the current frame. For example, if you typed
. list
then people.dta will be listed. If you typed
. frame counties: list
then counties.dta will be listed. Or you could make counties the current frame by typing
. frame change counties
and list will now list the counties data.
Navigating frames is easy and so is linking them. Imagine that both datasets have a variable named countycode that identifies counties in the same way. Type
. frlink m:1 countycode, frame(counties)
and each person in the default frame is linked to a county in the counties frame. This means you can now use the frget command to copy variables from the counties frame to the current frame. Or you can use the frval() function to directly access the values of variables in the counties frame. For instance, if we have each individual’s income in the default frame and median county income in the counties frame, we can generate a new variable containing relative income by typing
. generate rel_income = income / frval(counties, median_income)
This is the beginning. While this example uses only two frames, you can have up to 100 frames in memory at once, and you can have many links among those frames.

Stata是一款完整的、集成的统计软件包,提供您需要的一切数据分析、数据管理和图形。
统计功能介绍
Stata使得大量的统计工具用于指尖
标准方法,如
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
广义线性模型(GLM)
聚类分析
对比和比较
功率分析
样本选择
……
方法,如
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
多重替代法
调查数据
Treatment effects
统计
贝叶斯分析
……
Stata是一个统计分析软件,但它也具有很强的程序语言功能,这给用户提供了一个广阔的开发应用的天地,用户可以充分发挥自己的聪明才智,熟练应用各种技巧,真正做到随心所欲。事实上,Stata的ado文件(统计部分)都是用Stata自己的语言编写的。
Stata其统计分析能力远远**过了SPSS,在许多方面也**过了SAS!由于Stata在分析时是将数据全部读入内存,在计算全部完成后才和磁盘交换数据,因此计算速度较快(一般来说, SAS的运算速度要比SPSS至少快一个数量级,而Stata的某些模块和执行同样功能的SAS模块比,其速度又比SAS快将近一个数量级!)Stata也是采用命令行方式来操作,但使用上远比SAS简单。其生存数据分析、纵向数据(重复测量数据)分析等模块的功能甚至**过了SAS。用Stata绘制的统计图形相当精美,很有特色。
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Posterior / Likelihood Prior
If the posterior distribution can be derived in a closed form, we may proceed directly to the
inference stage of Bayesian analysis. Unfortunately, except for some special models, the posterior
distribution is rarely available explicitly and needs to be estimated via simulations. MCMC sampling
can be used to simulate potentially very complex posterior models with an arbitrary level of precision.
MCMC methods for simulating Bayesian models are often demanding in terms of specifying an efficient
sampling algorithm and verifying the convergence of the algorithm to the desired posterior distribution.
Inference is the next step of Bayesian analysis. If MCMC sampling is used for approximating the
posterior distribution, the convergence of MCMC must be established before proceeding to inference.
Point and interval estimators are either derived from the theoretical posterior distribution or estimated
from a sample simulated from the posterior distribution. Many Bayesian estimators, such as posterior

summarize shows that the new variable has a mean of approximately zero; 10��9 is the precision of
a float and is close enough to zero for all practical purposes. If we wanted, we could have typed
egen double stdage = std(age), making stdage a double-precision variable, and the mean would
have been 10��16. In any case, summarize also shows that the standard deviation is 1. correlate
shows that the new variable and the original variable are perfectly correlated.
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