Table of Contents
Zero-Inflated Poisson Regression is a statistical technique used in SAS data analysis to model count data with excessive zeros. It is an extension of the traditional Poisson regression model, which assumes that the count data follows a Poisson distribution. However, in real-world scenarios, there may be a large proportion of zero values in the data, which violates the assumption of the Poisson model. Zero-Inflated Poisson Regression addresses this issue by incorporating a two-part model, where the first part models the excess zeros and the second part models the non-zero counts. This allows for a more accurate and robust analysis of count data with excessive zeros. It is commonly used in fields such as healthcare, finance, and marketing to analyze data with a high number of zeros, such as insurance claims, financial transactions, and product sales. In SAS, this technique is implemented using the PROC GENMOD procedure, and it allows for the identification of significant predictors and the interpretation of their effects on both the zero and non-zero counts. Overall, Zero-Inflated Poisson Regression is a valuable tool in SAS data analysis for handling count data with excessive zeros and providing more accurate and reliable results.
Zero-InflatedPoisson Regression | SAS Data Analysis Examples
Version info: Code for this page was tested in SAS 9.3
Zero-inflated Poisson regression is used to model count data that has an excess of zero counts.
Further, theory suggests that the excess zeros
are generated by a separate process from the count values and that the excess zeros can
be modeled independently. Thus, the zip model has two parts, a
Poisson count model and the logit model
for predicting excess zeros. You may want to review these Data Analysis Example pages,
Poisson Regression and
Logit Regression.
Please Note: The purpose of this page is to show how to use various data analysis commands.
It does not cover all aspects of the research process which researchers are expected to do. In
particular, it does not cover data cleaning and verification, verification of assumptions, model
diagnostics and potential follow-up analyses.
Examples of zero-inflated Poisson regression
Example 1. School administrators study the attendance behavior of high
school juniors over one semester at two schools. Attendance is measured by
number of days of absent and is predicted by gender of the student and
standardized test scores in math and language arts. Many students have no
absences during the semester.
Example 2. The state wildlife biologists want to model how many fish are
being caught by fishermen at a state park. Visitors are asked whether or not
they have a camper, how many people were in the group, were there children in
the group and how many fish were caught. Some visitors do not fish, but there is
no data on whether a person fished or not. Some visitors who did fish did not
catch any fish so there are excess zeros in the data because of the people that
did not fish.
Description of the data
Let’s pursue Example 2 from above using the dataset
fish.
We have data on 250 groups that went to a park. Each group was questioned
about how many fish they caught (count), how many children were in the
group (child), how many people were in the group (persons), and
whether or not they brought a camper to the park (camper).
In addition to predicting the number of fish caught, there is interest in
predicting the existence of excess zeros, i.e., the zeroes that were not simply
a result of bad luck fishing. We will use the variables child, persons, and
camper in our model. Let’s look at the data.
proc means data = fish mean std min max var; var count child persons; run; The MEANS Procedure Variable Mean Std Dev Minimum Maximum Variance ---------------------------------------------------------------------------------------- count 3.2960000 11.6350281 0 149.0000000 135.3738795 child 0.6840000 0.8503153 0 3.0000000 0.7230361 persons 2.5280000 1.1127303 1.0000000 4.0000000 1.2381687 ---------------------------------------------------------------------------------------- proc univariate data = fish noprint; histogram count / midpoints = 0 to 50 by 1 vscale = count ; run;proc freq data = fish; tables camper; run; The FREQ Procedure Cumulative Cumulative camper Frequency Percent Frequency Percent ----------------------------------------------------------- 0 103 41.20 103 41.20 1 147 58.80 250 100.00
Analysis methods you might consider
Below is a list of some analysis methods you may have encountered.
Some of the methods listed are quite reasonable while others have either fallen out of favor or
have limitations.
SAS zero-inflated Poisson regression analysis using proc genmod
If you are using SAS version 9.2 or higher, you can run a zero-inflated Poisson model using
procgenmod.
proc genmod data = fish;
class camper;
model count = child camper /dist=zip;
zeromodel persons /link = logit;
run;
The GENMOD Procedure
Model Information
Data Set WORK.FISH Written by SAS
Distribution Zero Inflated Poisson
Link Function Log
Dependent Variable count
Number of Observations Read 250
Number of Observations Used 250
Class Level Information
Class Levels Values
camper 2 0 1
Criteria For Assessing Goodness Of Fit
Criterion DF Value Value/DF
Deviance 2063.2168
Scaled Deviance 2063.2168
Pearson Chi-Square 245 1543.4597 6.2998
Scaled Pearson X2 245 1543.4597 6.2998
Log Likelihood 774.8999
Full Log Likelihood -1031.6084
AIC (smaller is better) 2073.2168
AICC (smaller is better) 2073.4627
BIC (smaller is better) 2090.8241
Algorithm converged.
Analysis Of Maximum Likelihood Parameter Estimates
Standard Wald 95% Confidence Wald
Parameter DF Estimate Error Limits Chi-Square Pr > ChiSq
Intercept 1 2.4319 0.0413 2.3510 2.5128 3472.23 ChiSq
Intercept 1 1.2974 0.3739 0.5647 2.0302 12.04 0.0005
persons 1 -0.5643 0.1630 -0.8838 -0.2449 11.99 0.0005
The output begins with a summary of the model and the data.
This is followed by a list of goodness of fit statistics.
The next block of output includes parameter estimates from the count portion of
the model. It also includes the standard errors, Wald 95% confidence
intervals, Wald Chi-square statistics, and p-values for the parameter estimates.
The last block of output corresponds to the zero-inflation portion of the
model. This is a logistic model predicting the zeroes. The output
includes parameter estimates for the inflation model predictors and their
standard errors, Wald 95% confidence intervals, Wald Chi-square statistics, and
p-values.
All of the predictors in both the count and inflation portions of the model are
statistically significant. This model fits the data significantly better
than the null model, i.e., the intercept-only model. To show that this is the
case, we can run the null model (a model without any predictors) and compare the null model with the current
model using chi-squared test on the difference of log likelihoods.
proc genmod data = fish;
model count = /dist=zip;
zeromodel / link = logit ;
run;
The GENMOD Procedure
Model Information
Data Set WORK.FISH Written by SAS
Distribution Zero Inflated Poisson
Link Function Log
Dependent Variable count
Number of Observations Read 250
Number of Observations Used 250
Criteria For Assessing Goodness Of Fit
Criterion DF Value Value/DF
Deviance 2254.0459
Scaled Deviance 2254.0459
Pearson Chi-Square 248 1918.7890 7.7371
Scaled Pearson X2 248 1918.7890 7.7371
Log Likelihood 679.4854
Full Log Likelihood -1127.0229
AIC (smaller is better) 2258.0459
AICC (smaller is better) 2258.0945
BIC (smaller is better) 2265.0888
Algorithm converged.
Analysis Of Maximum Likelihood Parameter Estimates
Standard Wald 95% Confidence Wald
Parameter DF Estimate Error Limits Chi-Square Pr > ChiSq
Intercept 1 2.0316 0.0349 1.9631 2.1000 3388.16 ChiSq
Intercept 1 0.2728 0.1277 0.0225 0.5232 4.56 0.0327
The log likelihoods for the full model and null mode are -1031.6084 and -1127.0229,
respectively. The chi-squared value is 2*( -1031.6084 – -1127.0229) = 190.829.
Since we have three predictor variables in the full model, the degrees of freedom
for the chi-squared test is 3. This yields a p-value <.0001. Thus, our
overall model is statistically significant.
We can use the estimate
statement to help understand our model. We will
compute the expected counts for the categorical variable camper while holding the
continuous variable child at its mean value using the atmeans option,
as well as calculate the predicted probability that an observation came from the
zero-generating process. In the estimate statement, we provide values at
which to evaluate each coefficient for both the Poisson model and the
zero-inflation model. The sets of coefficients of the two models are
separated by the @ZERO keyword.
proc genmod data = fish;
class camper;
model count = child camper /dist=zip;
zeromodel persons /link = logit ;
estimate "camper = 0" intercept 1 child .684 camper 1 0 @ZERO intercept 1 persons 2.528;
estimate "camper = 1" intercept 1 child .684 camper 0 1 @ZERO intercept 1 persons 2.528;
run;
Contrast Estimate Results
Mean Mean L'Beta Standard
Label Estimate Confidence Limits Estimate Error Alpha
camper = 0 2.4220 1.9724 2.9741 0.8846 0.1048 0.05
camper = 0 (Zero Inflation) 0.4677 0.3838 0.5536 -0.1292 0.1756 0.05
camper = 1 5.5768 4.8823 6.3701 1.7186 0.0679 0.05
camper = 1 (Zero Inflation) 0.4677 0.3838 0.5536 -0.1292 0.1756 0.05
Contrast Estimate Results
L'Beta Chi-
Label Confidence Limits Square Pr > ChiSq
camper = 0 0.6792 1.0899 71.28 <.0001
camper = 0 (Zero Inflation) -0.4735 0.2150 0.54 0.4619
camper = 1 1.5856 1.8516 641.42 <.0001
camper = 1 (Zero Inflation) -0.4735 0.2150 0.54 0.4619
In the Mean Estimate column, we find predicted counts of fish from the
Poisson model, ignoring the zero-inflation model, for both camper = 0 and
camper = 1, as well as the predicted probability of belonging to the
zero-generating process from the zero-inflation model. The zero-inflation
model does not include camper as a predictor, so the probability of zero
for both zero-inflation models is the same. To get the expected counts of
fish from the mixture of the two models, simply multiply the expected
counts from the Poisson model by the probability of getting a non-zero from the
zero-inflation model (1 – p(zero)). Thus, the expected counts of fish for
camper = 0 and camper = 1 including zero-inflation are
2.422*(1-0.4677) = 1.289 and 5.5768*(1-0.4677) = 2.968, respectively.
SAS zero-inflated Poisson
analysis using proc countreg
Proc countreg is another option for running a zero-inflated Poisson
regression in SAS (again, version 9.2 or higher). This procedure allows
for a few more options
specific to count outcomes than proc genmod. The proc countreg
code for the original model run on this page appears below. We indicate
method = qn to specify the quasi-Newton optimization process that matches
the proc genmod results.
proc countreg data = fish method = qn;
class camper;
model count = child camper / dist= zip;
zeromodel count ~ persons;
run;
The COUNTREG Procedure
Class Level Information
Class Levels Values
camper 2 0 1
Model Fit Summary
Dependent Variable count
Number of Observations 250
Data Set MYLIB.FISH
Model ZIP
ZI Link Function Logistic
Log Likelihood -1032
Maximum Absolute Gradient 3.69075E-7
Number of Iterations 13
Optimization Method Quasi-Newton
AIC 2075
SBC 2096
Algorithm converged.
Parameter Estimates
Standard Approx
Parameter DF Estimate Error t Value Pr > |t|
Intercept 1 2.431911 0.041271 58.93 <.0001
child 1 -1.042838 0.099988 -10.43 <.0001
camper 0 1 -0.834022 0.093627 -8.91 <.0001
camper 1 0 0 . . .
Inf_Intercept 1 1.297439 0.373850 3.47 0.0005
Inf_persons 1 -0.564347 0.162962 -3.46 0.0005
SAS Zero-inflated Poisson analysis using proc nlmixed
For those using a version of SAS prior to 9.2, a zero-inflated negative
binomial model is doable, though significantly more difficult. Please see
this code fragment: Zero-inflated Poisson and Negative Binomial Using Proc Nlmixed.
Things to consider
See also
References
Cite this article
stats writer (2024). What is Zero-Inflated Poisson Regression and how is it used in SAS data analysis?. PSYCHOLOGICAL SCALES. Retrieved from https://scales.arabpsychology.com/stats/what-is-zero-inflated-poisson-regression-and-how-is-it-used-in-sas-data-analysis/
stats writer. "What is Zero-Inflated Poisson Regression and how is it used in SAS data analysis?." PSYCHOLOGICAL SCALES, 29 Jun. 2024, https://scales.arabpsychology.com/stats/what-is-zero-inflated-poisson-regression-and-how-is-it-used-in-sas-data-analysis/.
stats writer. "What is Zero-Inflated Poisson Regression and how is it used in SAS data analysis?." PSYCHOLOGICAL SCALES, 2024. https://scales.arabpsychology.com/stats/what-is-zero-inflated-poisson-regression-and-how-is-it-used-in-sas-data-analysis/.
stats writer (2024) 'What is Zero-Inflated Poisson Regression and how is it used in SAS data analysis?', PSYCHOLOGICAL SCALES. Available at: https://scales.arabpsychology.com/stats/what-is-zero-inflated-poisson-regression-and-how-is-it-used-in-sas-data-analysis/.
[1] stats writer, "What is Zero-Inflated Poisson Regression and how is it used in SAS data analysis?," PSYCHOLOGICAL SCALES, vol. X, no. Y, ص Z-Z, June, 2024.
stats writer. What is Zero-Inflated Poisson Regression and how is it used in SAS data analysis?. PSYCHOLOGICAL SCALES. 2024;vol(issue):pages.