turn on suggestions

Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

- JMP User Community
- :
- Discussions
- :
- Discussions
- :
- How can I obtain the BIC and AIC form -log likelih...

Topic Options

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Mar 16, 2017 6:22 PM
(1166 views)

Hello,

I use the JMP pro 12.

I would like to know, how can I obtain the AIC or the BIC from -likelihood, in neural network analysis?

Regards,

Angelo

3 REPLIES

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Mar 17, 2017 8:24 AM
(1152 views)

I have some code that I can share with you to do this - unfortunately I'm travelling at the moment - if you don't get a reply from someone else I'll be able to post it on Monday.

-Dave

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Mar 18, 2017 10:51 AM
(1133 views)

Dear David,

It is good for me. I will wait Monday.

Thank you so much,

Angelo

It is good for me. I will wait Monday.

Thank you so much,

Angelo

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Mar 20, 2017 3:39 PM
(1101 views)

Sorry I was pretty sure I'd done it for neural nets, but it was actually for the nonlinear platform. Here's the code:

dt = Open("$SAMPLE_DATA/Nonlinear Examples/Chemical Kinetics.jmp"); fit = dt << Nonlinear( Y( :Name( "Velocity (y)" ) ), X( :Name( "Model (x)" ) ), Newton, Finish ); rep = fit << Report; matRmse = rep[NumberColBox(5)] << Get As Matrix; /*************************************************** / calculate AICc / ***************************************************/ // AIC = N.ln(SS/N) + 2k // where N = # data points // k = # parameters + 1 // SS = sum of squares for the residuals // AICc = AIC + [ 2k(k+1) / (N-k-1) ] N = NRows(dt); nParameters = 2; k = nParameters + 1; rmse = matRmse[1]; dfSS = N - nParameters; MS = rmse^2; SS = dfSS * MS; // this is correct compared to fit model LL = N*Log(2*Pi()*e()*SS/N); AIC = LL + 2*k; AICc = AIC + ( (2*k*(k+1))/(N-k-1) ); /*************************************************** / calculate BIC / ***************************************************/ BIC = LL + k*Log(N);

In this code I had to determine the log-likelihood value whereas the neural net gives you this so it should be easier. I think you just need to think about what is the value of k. I assume it would be 1 plus the number of weights that are being estimated (for a single layer network that would be #factors x #nodes);

-Dave