Currently running quasi-likelihood regressions, where the code is PROC GLIMMIX.
Can someone tell me the difference between the following statements:
model y ~ x /link = logit dist = binomial
model y ~ x / link = log solution
When I ran the code with 'link = logit dist = binomial' I was never able to get a value on the coefficients (which is what I'm looking for).
Link function is different.
model y ~ x /link = logit dist = binomial :
Like proc logistic . link function is log(p/(1-p)) y~binomial distribution
model y ~ x / link = log solution:
link function is log(y) y usually is positive value.
Logistic regression in GLIMMIX often requires more iterations than the default to reach convergence. Providing your code and any quotes from the output regarding non-convergence, etc. would be helpful in addressing your problems.
Steve Denham
You have several issues/problems. With your code with link=log, you are using a normal distribution (the default). This is likely not what you want. Put in the dist= option. Also, your syntax is wrong for GLIMMIX. One does not use "y ~ x". One uses "y=x". Also, you are using pseudo-likelihood, not quasi-likelihood. Quasi-likelihood is when the likelihood is not defined (or definable). If you put in:
random _residual_;
then you would have quasi-likelihood with the binomial. Another way of getting quasi-, is to specify the mean and variance functions directly. Both ways are defining a variance:mean structure that does not correspond to a real probability distribution.
Thanks all for the clarification; I am still running into syntax error codes.
When I include dist = option is says syntax error, expecting one of the foollowing: B, BERNOULLI, BETA, etc.
Below is the code I ran:
proc glimmix data = TEST;
model utility_score = AGE INCOME ... (number of independent variables here) / link = log dist = option;
where utility_score gt 0;
run;
Reason why I'm running quasi-likelihood is because my dependent variable is between 0 and 1; cannot take on the 0 value but can be 1. Also, the dependent variable is skewed (left).
Got it. One (hopefully) last question; I believe that I need dist = beta because my dependent variable is between 0 and 1 (though it cannot take on the value of 0).
But the question I still have remaining is that this dependent variable is left-skewed (skewness: -1.97); is one able to incorporate that in the distribution code anywhere?
If I remember correctly a positively skewed variable would take on a Poisson distribution but I haven't come across how to incorporate a negatively-skewed variable.
Any thoughts would be greatly appreciated.
Are you ready for the spotlight? We're accepting content ideas for SAS Innovate 2025 to be held May 6-9 in Orlando, FL. The call is open until September 16. Read more here about why you should contribute and what is in it for you!
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.