BookmarkSubscribeRSS Feed
rif_160
Calcite | Level 5

i am exploring SAS Enterprise Miner right now. i am working with proc Genmod. 

 

 

Analysis Of Maximum Likelihood Parameter Estimates
 
                                       Standard     Wald 95% Confidence
Parameter            DF    Estimate       Error           Limits

Intercept             1     -2.5659      0.0666     -2.6964     -2.4354
Brand        PBB      1      1.0135      0.0804      0.8559      1.1711
Brand        PFl      1     -0.2243      0.0991     -0.4185     -0.0301
Brand        PGen     1      0.2179      0.0901      0.0413      0.3945
Brand        PHse     1      0.9059      0.0814      0.7465      1.0654
Brand        PPk      1      2.3614      0.0750      2.2145      2.5083
Brand        PSS      0      0.0000      0.0000      0.0000      0.0000
Scale                 0      1.0000      0.0000      1.0000      1.0000

NOTE: The scale parameter was held fixed.

to my understanding, i am reading the output like, for example for Brand PPk estimate is 2.3614. that means this brand is 2.3614 likely to be the choice than the other 5. and for brand PSS its 0.0000 that means it was never choosed.

 

Am i right in onterpreting the results??? 

1 REPLY 1
rif_160
Calcite | Level 5
Posterior Correlation Matrix
 
                               Brand       Brand       Brand       Brand       Brand
Parameter      Intercept         PBB         PFl        PGen        PHse         PPk

Intercept          1.000      -0.829      -0.688      -0.738      -0.820      -0.888
BrandPBB          -0.829       1.000       0.566       0.617       0.676       0.742
BrandPFl          -0.688       0.566       1.000       0.510       0.561       0.609
BrandPGen         -0.738       0.617       0.510       1.000       0.594       0.653
BrandPHse         -0.820       0.676       0.561       0.594       1.000       0.727
BrandPPk          -0.888       0.742       0.609       0.653       0.727       1.000

for this posterior corelation matrix, output will be interprted as for example, Brand PBB and Brand PPk are strongly corelated as 0.742 is close to 1. ????

sas-innovate-2024.png

Join us for SAS Innovate April 16-19 at the Aria in Las Vegas. Bring the team and save big with our group pricing for a limited time only.

Pre-conference courses and tutorials are filling up fast and are always a sellout. Register today to reserve your seat.

 

Register now!

What is Bayesian Analysis?

Learn the difference between classical and Bayesian statistical approaches and see a few PROC examples to perform Bayesian analysis in this video.

Find more tutorials on the SAS Users YouTube channel.

Click image to register for webinarClick image to register for webinar

Classroom Training Available!

Select SAS Training centers are offering in-person courses. View upcoming courses for:

View all other training opportunities.

Discussion stats
  • 1 reply
  • 673 views
  • 0 likes
  • 1 in conversation