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以上这些命令都用一下，你会发觉每个命令都有自己的特色，有得主攻tabulation输出，有的则是ereturn结果输出，有的则是估计参数的输出。里面都有详细的帮助文件，因此我们并没有给出具体示例。若有不懂之处，请发送邮件或留言请教其他圈友。

Using the Parameter Estimates(用到那些保存结果的参数，比如e()）

Suppose that we want to make a table of just the means in the above example. We can do this be taking advantage of a very nice feature of Stata. Most Stata commands produce temporary variables containing the key results. These temporary variables will continue to store these values until you use another Stata command that replaces those results, or until you end your Stata session.

You can look at what results have been stored using either return list or for estimation commands ereturn list. Here's a subset of the estimate parameters stored temporarily after the svy:mean command that produced the table above:

ereturn list

scalars:
               e(df_r) =  43
      e(N_strata_omit) =  0
          e(singleton) =  0
             e(census) =  0
           e(N_subpop) =  253890607
              e(N_sub) =  18081
              e(N_pop) =  261897236
              e(N_psu) =  86
           e(N_strata) =  43
             e(N_over) =  1
                  e(N) =  20607

     (lots of results omitted here)

matrices:
                  e(b) :  1 x 3
                  e(V) :  3 x 3
            e(_N_subp) :  1 x 3
           e(V_srssub) :  3 x 3
              e(V_srs) :  3 x 3
                 e(_N) :  1 x 3

     (mores results omitted here)

For purposes of this example, we're only interested in the means. The estimates of the means are contained in a matrix called e(b), which has dimensions 1 x 3.

The matrix e(b) is temporary. We want to create variables that we can export to Excel, so we first need to put the means into variables that won't go away when we run another estimation command. There are lots of ways to do this. We'll demonstrate one way that uses official Stata commands and is flexible, but it requires you to manage the data a bit more than you might care to do. After that we'll talk about a number of user-contributed commands that work with results from estimation commands.

First, let's verify that we have in fact selected the right matrix of estimates:

matrix list e(b)

e(b)[1,3]
         var1       var2       var3
y1  289.68245   106.6606  396.34305

We can compare these values with the means printed in the table above and see that we have the estimates of the means. Next put them into a matrix, and then create a variable for each cell in the matrix:

matrix means = e(b)
gen meanvar1= means[1,1]
gen meanvar2= means[1,2]
gen meanvar3= means[1,3]
list meanvar*  in 1

     +--------------------------------+
     | meanvar1   meanvar2   meanvar3 |
     |--------------------------------|
  1. | 289.6824   106.6606    396.343 |
     +--------------------------------+

keep in 1
(21661 observations deleted)

Since every observation has the same values for the three means, we can keep just one observation.

At this point we can browse the data, copy, and paste it to an empty Excel file template we've created with column and row labels, colors, and all the other formatting we want. We find this to be the easiest approach.

Another approach is to export the results to Excel.

export excel "c:\tables\means.xlsx", firstrow(variables)

Here is an example of how to write a program to build a more complex table with many rows.

putexcel(这个非常实用)

New in Stata 13 is the command putexcel. It reads the parameter estimates described above and writes them directly to an Excel spreadsheet. You can specify the starting cell (upper left) and even write column headers. Here's an example that accomplishes the task described above.

svy, subpop(subpop): mean var1 var2 var3
putexcel C4=("Means") B5=matrix(e(b)) using "c:\tables\means.xlsx" 

As we saw above, the svy: mean command puts the means into the matrix e(b). We then instructed putexcel to write those 3 means into a row in a spreadsheet, starting with cell B5. We also wrote the label "Means" centered above the 3 means in cell C4. The putexcel command is very powerful and flexible. See the PDF help for full details.

User-Contributed Commands(用户编写的程序)

It is a testament to both the cleverness and generosity of the Stata user community that so many powerful open-source commands exist outside the set shipped by Stata Corporation, and further that the authors maintain these commands and continue to offer improved versions. Most of these commands are archived at Boston College in the SSC (Statistical Software Components) website. To read about and install these commands, you can search the SSC website, or you can use the ssc command in Stata.

ssc describe parmest
ssc install parmest

CPC maintains the most current version of each of the commands listed below. You will need to install them locally if you run Stata on a standalone computer.

The discussion below is brief, intended only to point you to the command you might need. See the help for each command for details.

tabout（主要用于tabulation）

The tabout command produces publication-quality cross tabulations. Lots of features are available to customize the table. The output file may be in tab-delimited or html format. Tab-delimited format files may be copied into Word and converted into a table using the Table menu (Table, Convert, Text to table). Html format files can be opened in your browser and copied. Inside Word, use Paste Special and paste the table as Formatted Text (rtf). There's a tutorial on the command as well.

outreg(与outreg2一样的威武)

The outreg command uses the saved results after an estimation command to create a text file of the results you select. You can then turn this text file into a Word table. This command has lots of formatting features, and you can combine the results from several regressions into a single table. Here's a simple example to get you started.

cd "c:\tables\"
sysuse auto, clear
regress mpg foreign weight headroom trunk length turn displacement
outreg using outtab1, replace

The output file is tab-delimited and has the extension .out. Open the file in Word, highlight the rows of results, click on Insert, Table, Convert text to table.

outreg2

This is an extension of outreg to enhance its capabilities with multiple models and to provide more format options. Here's an example from the help. Follow the instructions above to convert the results to a Word table.

   cd "c:\tables\"
    sysuse auto, clear
    regress mpg foreign weight headroom trunk length turn displacement
    outreg2 using outtab2, replace cttop(full)
    regress mpg foreign weight headroom
    outreg2 using outtab2, see

esttab(这个outreg2用得最频繁)

This command is part of the estout package. The esttab command produces a table of regression results that have been stored by the eststor command, or the current results if nothing has been stored. The output table may be tab, csv, rtf, html, or other formats. Tab-delimited format files may be imported into Excel using the Import Wizard. Rtf files go directly into Word. There's a steep learning curve to this powerful command, but if you produce publication-quality tables often that have to look "just so", it's worth the time to study this command. Here's a simple example that outputs to .rtf.

eststo: svy, subpop(subpop  if childage == 1): mean var1 var2 var3
esttab using "c:\tables\esttab_means.rtf", replace

The command gives you a clickable link to esttab_means.rtf in the Results Window. There's a tutorial available for esttab here.

xml_tab(输出到excel)

Like esttab, the xml_tab command works from stored estimates. The output is in Excel's xml format. This format allows for a very feature-rich table that takes advantage of many of Excel's capabilities. Here's a simple example.

svy, subpop(subpop): mean enerbev1 enerfood1 enertot1
xml_tab e(b), save("d:\statatemp\xml_tab.xml") replace

The command gives you a clickable link to xml_tab.xml. You can store multiple sets of estimates using estimates store and use xml_tab to combine them in a single table.

parmest(别忘了，metaparm用于meta分析)

The parmest command comes in a package of four modules: parmest, parmby, parmcip, and metaparm. The parmest and parmby commands put each estimation result into a separate observation in an output dataset. parmcip inputs these variables and adds new variables containing confidence intervals and p-values. metaparm does a metanalysis on sets of estimation results.You can see an example here.

logout(比较快速)

The logout command captures the results that are printed to the Results Window (log) and writes them to Excel, Word, or other formats. The success of the formatting in the output file depends on the complexity of the results table you are exporting. Compared with copying from the log by hand and pasting into a spreadsheet, this approach may not produce as well formatted tables, but it can be automated. Here's an example:

logout, save(c:\tables\logout_means) excel replace: ///
   svy, subpop(subpop): mean var1 var2 var3

This command produces a file call logout_means.xml, and it gives you a clickable link to this file in the Results Window.

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