Go to “Help” => “SJ and User Written Commands” to explore. To add these three commands to your Stata software execute the following code and click on the links to download the commands:Īs shown in the December, 2015 free webinar “ Stata’s Bountiful Help Resources”, you can also explore all the add-on commands via Stata’s “Help” menu. Winsor2 salary, cuts(0.5 99.5) (makes changes at the 0.5 st and 99.5 th percentile) The user has the option to change the values to the percentile of their choice. Winsor2 salary (makes changes at the 1 st and 99 th percentile for the variable “salary”) Values greater than the 99th percentile are changed to equal the 99th percentile. The default setting changes observations whose values are less than the 1st percentile to the 1 percentile. The command creates a new variable, adding a suffix “_w” to the original variable’s name. This command incorporates coding from the command winsor created by Nicholas Cox and Judson Caskey. This was created by Lian Yujun of Sun Yat-Sen University, China. If you choose to winsorize your data I suggest you check out the command winsor2. Note, winsorizing and deleting observations can introduce statistical bias. We can keep them as they are, winsorize the observations (change their values), or delete them. There are primarily three options for dealing with outliers. In a data set it is not uncommon to have outliers. ![]() In the workshop Managing Data and Optimizing Output in Stata, we used this scalar within a loop to create macros for continuous, categorical and indicator variables.ģ. In addition, the command generates the scalar r(ndistinct). The distinct command along with its min(#) and max(#) options allows an easy search for variables that fit into these categories.įor example, to create a table of all variables with three to seven distinct observations I use the following code: When getting to know a data set, it can be helpful to search for potential indicator, categorical and continuous variables. This command generates a table with the count of distinct observations for each variable in the data set. Nicholas Cox of Durham University and Gary Longton of the Fred Hutchinson Cancer Research Center created the command distinct. I can also separate the predictor variables into individual graphs:Ĭoefplot model_3d || model_3e, yline(0) bycoefs vertical byopts(yrescale) ylabel(, labsize(vsmall))Ģ. Including the code xline(0) creates a vertical line at zero which quickly allows me to determine whether a confidence interval spans both positive and negative territory. Using the coefplot command I can graphically compare the coefficients and confidence intervals for each independent variable used in the models.Ĭoefplot model_3d model_3e, drop(_cons) xline(0) In a recent post on diagnosing missing data, I ran two models comparing the observations that reported income versus the observations that did not report income, models 3d and 3e. This command allows you to plot results from estimation commands. coefplot is a command written by Ben Jann of the Institute of Sociology, University of Bern, Bern, Switzerland. ![]() Below I have highlighted three of the 185 that I have downloaded.ġ. There are over three thousand commands available for downloading. The SSC archive is maintained by the Boston College Department of Economics. Practically all of these commands, which are free, can be downloaded from the SSC (Statistical Software Components) archive. There are countless commands written by very, very smart non-Stata employees that are available to all Stata users. This is because Stata allows members of the Stata community to share their expertise. R is open source.īut, because I have a Stata license (once you have it, it never expires) I think of Stata as being open source. Based on that definition Stata, SPSS and SAS are not open source. ![]() Technically a commercial software package (software you have to pay for) cannot be open source. How so? Is Stata an “open source” software package? Yes I know, there are really, really smart people that use SAS and SPSS as well.īut unlike SAS and SPSS users, Stata users benefit from the contributions made by really, really smart people. Fortunately there are some really, really smart people who use Stata.
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