BookmarkSubscribeRSS Feed
Kyra
Quartz | Level 8

Hi,

 

I am wondering if i should do Proc GLM (linear regression) vs Proc GENMOD (poisson regression) for my outcome below.

 

Days to goal feed is my outcome which is positive integers (no decimals).

My exposure is type of feed which is bolus vs continuous.

 

Thanks,

 

 

4 REPLIES 4
Reeza
Super User

@Kyra wrote:

Hi,

 

I am wondering if i should do Proc GLM (linear regression) vs Proc GENMOD (poisson regression) for my outcome below.

 

Days to goal feed is my outcome which is positive integers (no decimals).

My exposure is type of feed which is bolus vs continuous.

 

Thanks,

 

 


Graph your data. If you lambda is higher it starts to approximate a normal distribution anyways and doesn't matter too much. If it's definitely not normal, or the lambda isn't high enough to approximate a continuous distribution, then I would recommend Poisson. Also, Poisson measures the number of events within a specific time period, so I think a GLM would work better here as well. But I'll move this to the Stat procedures forum and someone can provide a more robust answer. 

 

PaigeMiller
Diamond | Level 26

The response variable does not have to be (approximately) normal for GLM to work. 

 

The residuals from the fitted GLM model have to be (approximately) iid normal for the GLM hypothesis tests to be valid. Plot the residuals from the GLM model. 

--
Paige Miller
Reeza
Super User
I was thinking of normal in terms of allowing it to go below zero, not the distribution requirement for regression. That part I remember 🙂

Is your data time to event analysis or number of events analysis?
StatDave
SAS Super FREQ

While relying on the normal approximation and using PROC GLM is certainly reasonable if conditions allow (more likely if the mean is not small), the positive integer nature of the response makes it clearly nonnormal making it generally safer to use PROC GENMOD. Also, if the data are counts, the assumption of equal variances needed by PROC GLM is also probably violated. If you fit a Poisson model, you should check for evidence of overdispersion which is common and consider instead using the negative binomial distribution or other alternative if overdispersion exists. See this note. Also, when you say the response is positive, if you mean it cannot be zero, then you might want to fit a truncated Poisson or negative binomial model as discussed in this note

sas-innovate-2024.png

Available on demand!

Missed SAS Innovate Las Vegas? Watch all the action for free! View the keynotes, general sessions and 22 breakouts on demand.

 

Register now!

What is ANOVA?

ANOVA, or Analysis Of Variance, is used to compare the averages or means of two or more populations to better understand how they differ. Watch this tutorial for more.

Find more tutorials on the SAS Users YouTube channel.

Discussion stats
  • 4 replies
  • 2371 views
  • 4 likes
  • 4 in conversation