Some statistical procedures such as regression analysis will not work as well, or at all, on a data set with missing values. The observations with missing values have to be deleted or the missing values have to be substituted in order for a statistical procedure to produce meaningful results. Most statistical programs including SAS, SPSS, and Stata will automatically remove such cases from any analysis you run without deleting the cases from the dataset.

Different variables have different amounts of missing data and hence, changing the variables in a model changes the number of cases with complete data on all the variables in the model.

However, the presence of missing data can influence our results, especially when a dataset or even a single variable, has a high percentage of values missing. Thus it is always a good idea to check a dataset for missing data, and to think about how the missing data may influence our analyses. This page shows a few methods of looking at missing values in a dataset, this information can be used to make better informed decisions about how to handle the missing values.

Before we begin, we need some data with missing values, the code below inputs a small dataset into Stata, and then displays that data. In a small dataset, like the one below, it is very easy to look at the raw data and see where values are missing. However, when datasets are large, we need a more systematic way to examine our dataset for missing values.

Below we show you some ways to do that, using the data below as an example. The first thing we are going to do is determine which variables have a lot of missing values. We have created a small Stata program called mdesc that counts the number of missing values in both numeric and character variables.

You can download mdesc from within Stata by typing search mdesc see How can I use the search command to search for programs and get additional help? Now we know the number of missing values in each variable. For instance, variable salepric has four missing values and saltoapr has two missing values. We can also look at the distribution of missing values across observations. The code below creates a variable called nmis that gives the number of missing values for each observation.

John deere 329d reviewThe function rmiss2 used here is an extension to the egen function rmiss. It counts the number of missing values in the varlist. You can download rmiss2 over the internet from within Stata by typing search rmiss2 see How can I use the search command to search for programs and get additional help?

Below we tabulate the variable we just created. Looking at the frequency table we know that there are four observations with no missing values, nine observations with one missing values, one observation with two missing values and one observation with three missing values. We can also look at the patterns of missing values. You can download mvpatterns over the internet from within Stata by typing search mvpatterns see How can I use the search command to search for programs and get additional help?

The command mvpatterns produces output for all variables in the dataset, for missing data patterns across a subset of variables, a variable list can be included, for example, mvpatterns landval improval totval. The output produced by mvpatterns is shown below. The first table lists the variables, their storage type typethe number of observations obsthe number of missing values mvand the variable label if the variables has one.

The second table contains the information on the pattern of missing values. The first block of columns in the output shows the patterns of missing data.

The columns follow the same order as the variable list in the first table, so that the first column in the output below represents landvalthe second improvaland so on. Based on the information in the second table we know that there are four observations with no missing values, two cases missing on just the variable salepricand one observation with missing values on improvalsalepric and saltoapr.

The examples above all work regardless of whether the variables of interest i. When all of the variables you wish to check for missing values are numeric we can use a program called misschk to simplify the steps of examining the missing data in our dataset.

Note: numeric variables include those with value labels that are strings, as long as the actual values of the variables are stored as numbers.

Softcam key freesat 2019You can download misschk from within Stata by typing search misschk see How can I use the search command to search for programs and get additional help?Results from the most Stata procedures can be retraced from the computer's memory.

Typically, this holds true for the most recently executed procedure. Most Stata procedures store some, many or perhaps all elements that were used during computation in memory from which they can be retrieved; they remain available until the next procedure produces new elements to be stored.

There is considerable variation as to what is stored: command tab1 stores only the number of cases and the number of rows, other procedures store a wealth of information. Stata distinguishes several classes of elements, of which re and c are most important. Class r results can be expected from descriptive procedures, whereas estimation procedures statistical models, but also command such as mean yield e results.

You can access all current r elements via return list and the e elements via ereturn list. A single element of the list can be addressed just by its name.

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Thus, display e N will yield the number of cases in the last estimation, or you may use e N as an expression in a computation where it will be replaced by the number stored in e N.

The r and e elements may be single numbers called "scalars" in matrix languagematrices, or strings. You will note that those elements that are found under the rubric "matrices" are not displayed when you type return list or ereturn list ; what is displayed is the format of the matrices, e. Matrices are displayed with the help of the matrix list command.

For instance, from ereturn list or from the help function you may have learned that the variance-covariance matrix is stored in matrix e V. So, just type. Typing creturn list gives an overview of all c elements and their current settings. Class c elements are helpful particularly for programmers; average users may never have to use them. You may also use these expressions in the generation of variables, as in the following example that computes the predicted values as follows normally you will do this via the post-estimation command predictof course :.

Note that this works only for single equation models and for simple variables. If you have used factor variablesinteractions and the like, or if there are several dependent variables, the names of the coefficients will be more complex. In this case you may re-run your last estimation with option coeflegendas in this example of a linear regression model:.

Imagine that you run a regression model for several countries and wish to collect the regression coefficients from the different countries for further use in a graph, for instance. To achieve this, use the statsby command, which works like this:.

If you do it this way, Stata will replace the dataset you have used by the elements collected by statsby. If you wish to keep your data in memory, you can store the elements in a file as follows:.Login or Register Log in with.

Forums FAQ. Search in titles only. Posts Latest Activity. Page of 1. Filtered by:. Ethan Schoolman. Hi all: I am using "macro list" to display the contents of all the macros I have created locals and globals.

But what command to I use, or what option, to display the contents of just one macro, or just two? I have not been able to figure this out Tags: None. Iuri Gavronski. Hi, I normally use display, such as Code:.

How to embed xls file in htmlComment Post Cancel. And, that seems to have done it. Thank you! William Lisowski. While display is the common tool for displaying things flexibly, we should note for future readers that the macro list command will accept macro names and display just those macros. Ah, that is very helpful, too.

I did search the Stata command help files for something like this, but asked it to "help macro list", and it returned "help macrolists", which I did not notice the difference, and which was not what I was looking for. But if I just type "help macro", it does indeed show "macro list" as one option. Thank you. Previous Next. Yes No. OK Cancel.Login or Register Log in with. Forums FAQ. Search in titles only.

Plymouth voyager fuse box diagramPosts Latest Activity. Page of 1. Filtered by:. Conner Mullally. Dear all, In Stata 14, I'm using a loop to build a matrix containing summary statistics for several variables, which is then written to an Excel spreadsheet using putexcel. I'm trying to format each number to look the way I want it to once it is written to the Excel file.

The problem is that whenever I have a number large enough to have a comma, only the digits prior to the comma are written to the Excel file. In the code below, the line Code:. Tags: None. William Lisowski. I think the problem is that a Stata matrix stores numbers, not character strings, and it will never include the comma. I don't see any way to associate a format with a particular column or row or cell of a matrix, but I'm also not that experienced with them.

You might look at help matrix list and try matrix list before your putexcel to see exactly what you have in the matrix. Also, help putexcel suggests that you may want to use the keepcellformat option to preserve the formats already assigned to the cells in the worksheet.

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If you have a look at help listyou will discover plenty of options to customize the display. Learn more. Asked 8 years, 8 months ago. Active 6 years, 9 months ago. Viewed 6k times.

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How can I do that in Stata? Nick Cox 27k 5 5 gold badges 24 24 silver badges 43 43 bronze badges. Active Oldest Votes. Sign up or log in Sign up using Google. Sign up using Facebook.

Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. The Overflow How many jobs can be done at home? Featured on Meta. Community and Moderator guidelines for escalating issues via new response…. Feedback on Q2 Community Roadmap. Triage needs to be fixed urgently, and users need to be notified upon….This allows the user, as well as other Stata commands, to easily make use of this information.

Stata calls these returned results. Returned results can be very useful when you want to use information produced by a Stata command to do something else in Stata. For example, if you want to mean center a variable, you can use summarize to calculate the mean, then use the value of the mean calculated by summarize to center the variable.

Using returned results will eliminate the need to retype or cut and paste the value of the mean. Another example of how returned results can be useful is if you want to generate predicted values of the outcome variable when the predictor variables are at a specific set of values, again here, you could retype the coefficients or use cut and paste, but returned results make the task much easier. The best way to get a sense of how returned results work is to jump right in and start looking at and using them.

The code below opens an example dataset and uses summarize abbreviated sum to generate descriptive statistics for the variable read. This produces the expected output, but more importantly for our purposes, Stata now has results from the summarize command stored in memory. But how do you know what information has been stored? Above is a list of the returned results, as you can see each result is of the form r … where the ellipses "…" is a short label.

As you might imagine, different commands, and even the same command with different options, store different results. Below we summarize the variable read again, but add the detail option. Then we use return list to get the list of returned results. Just as the detail option adds additional information to the output, it also results in additional information stored in the returned results.

The new list includes all of the information returned by the sum command above, plus skewness; kurtosis; and a number of percentiles, including the 1st r p25 and 3rd r p75 quartiles and the median r p Now that we have some sense of what results are returned by the summarize command, we can make use of the returned results.

Following through with one of the examples mentioned above, we will mean center the variable read.

### How can I access information stored after I run a command in Stata (returned results)? | Stata FAQ

Notice that instead of using the actual value of the mean of read in this command, we used the name of the returned result i. As the code above suggests, we can use returned results pretty much the same way we would use an actual number. This is because Stata uses the r … as a placeholder for a real value.

For another example of this, say that we want to calculate the variance of read from its standard deviation ignoring the fact that summarize returns the variance in r Var. We can do this on the fly using the display command as a calculator.

The second line of code below does this. We can even check the result by cutting and pasting the value of the standard deviation from the output, which is done in the third command below. The results are basically the same, the very slight difference is rounding error because the stored estimate r sd contains more digits of accuracy than the value of the standard deviation displayed in the output.

Now that you know a little about returned results and how they work you are ready for a little more information about them. Commands that perform estimation, for example regressions of all types, factor analysis, and anova are e-class commands. Other commands, for example summarize, correlate and post-estimation commands, are r-class commands. The distinction between r-class and e-class commands is important because Stata stores results from e-class and r-class commands in different "places.

First, you need to know whether results are stored in r or e as well as the name of the result in order to make use of them. A potentially more important ramification of the difference in how results from r-class and e-class commands are returned is that returned results are held in memory only until another command of the same class is run. That is, returned results from previous commands are replaced by subsequent commands of the same class.Login or Register Log in with.

Mental health testForums FAQ. Search in titles only. Posts Latest Activity. Page of 1. Filtered by:. Kees Maat. Row and column names of a matrix 24 Aug I was wondering how to extract row and column names from a matrix. It appeared that it is somethink like this in case I want the second label: Code:. Tags: None. Clyde Schechter.

Both of those codes work. I don't know that there is much reason to choose one over the other. But in practice that is probably seldom a problem. If you don't, read the macro chapter These are mostly used in writing programs. They enable the programmer to capture lots of "system" information. There are many of them.

Anyway, the macro extended function :rownames all macro extended functions begin with a colon [:] takes a matrix name as its argument and it returns a local macro containing the names of the rows of that matrix. If you wanted to refer to that result again later, you could save it in a local macro: Code:.

Comment Post Cancel. Nick Cox.

**t-test for two independent samples in Stata®**

A minor twist on Clyde's excellent explanation is that parse was really the forerunner of syntax in Stata 5 and earlier. Nick is right, of course. I mis-remembered. The common use of -tokenize- in the past was before -foreach- was introduced. In the pre-foreach era, you would get, say, a list of variable names in a local macro. So instead of modern: Code:.

Thank you Clyde and Nickvery clear explanations. As for whateverthis is fill in any statementright? Previous Next. Yes No. OK Cancel.

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