An “estimation command” in Stata is a generic term used for a command that runs a statistical model. Examples are regress, ANOVA, Poisson, logit, and mixed.
Stata has more than 100 estimation commands.
Creating the “best” model requires trying alternative models. There are a number of different model building approaches, but regardless of the strategy you take, you’re going to need to compare them.
Running all these models can generate a fair amount of output to compare and contrast. How can you view and keep track of all of the results?
You could scroll through the results window on your screen. But this method makes it difficult to compare differences.
You could copy and paste the results into a Word document or spreadsheet. Or better yet use the “esttab” command to output your results. But both of these require a number of time consuming steps.
But Stata makes it easy: my suggestion is to use the post-estimation command “estimates”.
What is a post-estimation command? A post-estimation command analyzes the stored results of an estimation command (regress, ANOVA, etc).
As long as you give each model a different name you can store countless results (Stata stores the results as temp files). You can then use post-estimation commands to dig deeper into the results of that specific estimation.
Here is an example. I will run four regression models to examine the impact several factors have on one’s mental health (Mental Composite Score). I will then store the results of each one.
regress MCS weeks_unemployed i.marital_status
estimates store model_1
regress MCS weeks_unemployed i.marital_status kids_in_house
estimates store model_2
regress MCS weeks_unemployed i.marital_status kids_in_house religious_attend
estimates store model_3
regress MCS weeks_unemployed i.marital_status kids_in_house religious_attend income
estimates store model_4
To view the results of the four models in one table my code can be as simple as:
estimates table model_1 model_2 model_3 model_4
But I want to format it so I use the following:
estimates table model_1 model_2 model_3 model_4, varlabel varwidth(25) b(%6.3f) /// star(0.05 0.01 0.001) stats(N r2_a)
Here are my results:
My base category for marital status was “widowed”. Is “widowed” the base category I want to use in my final analysis? I can easily re-run model 4, using a different reference group base category each time.
Putting the results into one table will make it easier for me to determine which category to use as the base.
Note in table 1 the size of the samples have changed from model 2 (2,070) to model 3 (2,067) to model 4 (1,682). In the next article we will explore how to use post-estimation data to use the same sample for each model.
Jeff Meyer is a statistical consultant with The Analysis Factor, a stats mentor for Statistically Speaking membership, and a workshop instructor. Read more about Jeff here.
I recently opened a very large data set titled “1998 California Work and Health Survey” compiled by the Institute for Health Policy Studies at the University of California, San Francisco. There are 1,771 observations and 345 variables. (more…)
This free, one-hour webinar is part of our regular Craft of Statistical Analysis series. In it, we will introduce and demonstrate two of the core concepts of mixed modeling—the random intercept and the random slope.
Most scientific fields now recognize the extraordinary usefulness of mixed models, but they’re a tough nut to crack for someone who didn’t receive training in their methodology.
But it turns out that mixed models are actually an extension of linear models. If you have a good foundation in linear models, the extension to mixed models is more of a step than a leap. (Okay, a large step, but still).
You’ll learn what random intercepts and slopes mean, what they do, and how to decide if one or both are needed. It’s the first step in understanding mixed modeling.
Date: Friday, August 21, 2015
Time: 12pm EDT (New York time)
Cost: Free
***Note: This webinar has already taken place. Sign up below to get access to the video recording of the webinar.
Have you ever worked with a data set that had so many observations and/or variables that you couldn’t see the forest for the trees? You would like to extract some simple information but you can’t quite figure out how to do it.
Get to know Stata’s collapse command–it’s your new friend. Collapse allows you to convert your current data set to a much smaller data set of means, medians, maximums, minimums, count or percentiles (your choice of which percentile).
Let’s take a look at an example. I’m currently looking at a longitudinal data set filled with economic data on all 67 counties in Alabama. The time frame is in decades, from 1960 to 2000. Five time periods by 67 counties give me a total of 335 observations.
What if I wanted to see some trend information, such as the total population and jobs per decade for all of Alabama? I just want a simple table to see my results as well as a graph. I want results that I can copy and paste into a Word document.
Here’s my code:
preserve
collapse (sum) Pop Jobs, by(year)
graph twoway (line Pop year) (line Jobs year), ylabel(, angle(horizontal))
list
And here is my output:
By starting my code with the preserve command it brings my data set back to its original state after providing me with the results I want.
What if I want to look at variables that are in percentages, such as percent of college graduates, mobility and labor force participation rate (lfp)? In this case I don’t want to sum the values because they are in percent.
Calculating the mean would give equal weighting to all counties regardless of size.
Fortunately Stata gives you a very simple way to weight your data based on frequency. You have to determine which variable to use. In this situation I will use the population variable.
Here’s my coding and results:
Preserve
collapse (mean) lfp College Mobil [fw=Pop], by(year)
graph twoway (line lfp year) (line College year) (line Mobil year), ylabel(, angle(horizontal))
list
It’s as easy as that. This is one of the five tips and tricks I’ll be discussing during the free Stata webinar on Wednesday, July 29th.
Jeff Meyer is a statistical consultant with The Analysis Factor, a stats mentor for Statistically Speaking membership, and a workshop instructor. Read more about Jeff here.
Stata allows you to describe, graph, manipulate and analyze your data in countless ways. But at times (many times) it can be very frustrating trying to create even the simplest results. Join us and learn how to reduce your future frustrations.
This one hour demonstration is for new and intermediate users of Stata. If you’re a beginner, the drop down commands can be extremely daunting.
If you’re an intermediate user and not constantly using Stata, it’s impossible to remember which commands generate the results you are looking to create.
This webinar, by guest presenter Jeff Meyer, will give you five actionable tips (and examples you can re-use) that will make your next analysis in Stata much simpler.
We’ll explore:
- Save time with a do-file to create the table you want exactly as you want.
- A few methods (some easier than others) to create dummy variables out of a categorical variable with several categories
- At least three ways to insert a table into a document
- Quickly alter the looks of your graphs through the use of macros
- How to aggregate data to the group level based on a number of parameters
Date: Wednesday, July 29, 2015
Time: 4pm EDT (New York time)
Cost: Free
***Note: This webinar has already taken place. Sign up below to get access to the video recording of the webinar.
Our next free webinar is titled: “Random Intercept and Random Slope Models” and is coming up in August
Jeff Meyer is a statistical consultant with The Analysis Factor, a stats mentor for Statistically Speaking membership, and a workshop instructor. Read more about Jeff here.
One of Stata’s incredibly useful abilities is to temporarily store calculations from commands.
Why is this so useful? (more…)
