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The first line tells collect to report only coefficients (_r_b) an confidence intervals (_r_ci). The second line tells collect how the table is to be laid out. In words, put the covariate names (colname) on the rows of the table, and put each combination of estimation command and coefficient/confidence interval on the columns (cmdset#result).
Summary statistics Results from hypothesis tests Regression results LR and Wald tests, GOF statistics, ... Results from any Stata command
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 mean and posterior standard deviation, involve integration. If the integration cannot be performed analytically to obtain a closed-form solution, sampling techniques such as Monte Carlo integration and MCMC and numerical integration are commonly used.
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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