02-15-2016 06:56 PM - edited 02-15-2016 06:58 PM
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).
02-15-2016 08:56 PM
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.
02-16-2016 09:34 AM
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.
02-16-2016 05:50 PM
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:
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.
02-16-2016 06:10 PM
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;
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).
02-17-2016 08:49 AM
02-18-2016 12:27 PM
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.
02-18-2016 12:38 PM