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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.

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In Bayesian analysis, we can use previous information, either belief or experimental evidence, in
a data model to acquire more balanced results for a particular problem. For example, incorporating
prior information can mitigate the effect of a small sample size. Importantly, the use of the prior
evidence is achieved in a theoretically sound and principled way.
By using the knowledge of the entire posterior distribution of model parameters, Bayesian inference
is far more comprehensive and flexible than the traditional inference.
Bayesian inference is exact, in the sense that estimation and prediction are based on the posterior
distribution. The latter is either known analytically or can be estimated numerically with an arbitrary
precision. In contrast, many frequentist estimation procedures such as maximum likelihood rely on
the assumption of asymptotic normality for inference.

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 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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