Examples of _your_model
Description of the Data
_Describe_your_data_in_this_section, in Stata, all the data sets should be read in from the internet by using
Let’s look at the data.
use https://stats.idre.ucla.edu/stat/stata/notes/hsb2, clear summarize stay Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- stay | 1493 9.728734 8.132908 1 74 histogram stay, discrete
Some Strategies You Might Be Tempted To Try
Before we show how you can analyze these data with a zero-truncated negative binomial analysis, let’s consider some other methods that you might use.
- Enter your words here.
- Here is another point of interest.
Stata Zero-Truncated Negative Binomial Analysis
ztnb stay age hmo died Fitting Zero-truncated poisson model: (output omitted)
Sample Write-Up of the Analysis
Before we begin the sample write-up we need to get the output into a form more acceptable for publication. In order to go back to the non-exponentiated version of the coefficients we will quietly rerun the ztnb command. The estout command (search estout by Ben Jann of ETH Zurich), will get us close to what we want.
quietly ztnb estout, cells(b(star fmt(%8.2f)) se(par fmt(%8.2f))) stats(ll chi2, fmt(%8.2f)) b/se stay age -0.02 (0.01) hmo -0.15* (0.06) died -0.22*** (0.05) _cons 2.41*** (0.08) lnalpha _cons -0.57*** (0.07) ll -4755.28 chi2 25.29
With a little bit of manual editing we can produce an acceptable table of the output. I also manually added the likelihood ratio test for alpha from the non-robust ztnb.
model stay age -0.02 (0.01) hmo -0.15* (0.06) died -0.22*** (0.05) constant 2.41*** (0.07) likelihood ratio test for alpha 4307.04 log psuedo- likelihood -6908.80 chi-squared 25.29 legend: coefficient/(standard error) *** p<0.001
The zero-truncated negative binomial regression model predicting length of hospital stay from age, hmo membership and death during the hospital stay was statistically significant (chi-squared = 25.29, df = 3, p<.001). The likelihood ratio test for alpha, the over dispersion parameter, was significant (chi-squared = 4307.04, df = 1, p<.001) indicating that the zero-truncated negative binomial model is preferred over a zero-truncated poisson model. The predictors hmo and died were each statically significant. The effect of age was not significant at the .05 level.
For these data the expected log count for those enrolled in an hmo was -0.15 that of those not so enrolled. This amounts to a difference of about 1.25 days. Patients who died during the hospital stay had an expected log count difference of -0.22 or almost two days.
Cautions, Flies in the Ointment
- Stata Online Manual
- Related Stata Commands
- ztp -- zero-truncated poisson regression.
Long, J. S. 1997. Regression Models for Categorical and Limited Dependent Variables.
Thousand Oaks, CA: Sage Publications.