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
- :
- nominal logistic fit

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
- Permalink
- Email to a Friend
- Report Inappropriate Content

Jun 2, 2016 1:25 AM
(3174 views)

Hi , In nominal logistic fit. I get a significant model, all parameters are significant by the effect LRT. but all the parameters estimations are not significant. how can it be??

Solved! Go to Solution.

1 ACCEPTED SOLUTION

Accepted Solutions

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Permalink
- Email to a Friend
- Report Inappropriate Content

Jun 2, 2016 10:34 AM
(4582 views)

Solution

The cause is collinearity among the independent variables.

The overall test just says that the model including all the variables is better than the mean of the response.

The test for significance of the individual parameters is conditioned on all the other parameters in the model.

If there is substantial correlation between two independent variables, both important in explaining the response, then neither will be significant with the other in the model.

Collinearity can be more complicated than just two variables being correlated. It can happened if one variable is correlated with a linear combination of several other variables which is common when one has many independent variables.

The solution in general is to identify the subsets of variables that are highly correlated among each other and use a subset that is not too correlated.

Not being a JMP expert, more SAS, I will leave it to others to give you more specific advice on how do identify the highly correlated subsets

Some ideas that come to mind are looking at pairwise correlations. Another idea is to use the partition platform to pick candidate independent variables.

If your goal is prediction, then using recursive partitioning is fine. If you goal is inference, using recursive partitioning to select variables will bias the p-values small and make any inference suspect.

5 REPLIES

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Permalink
- Email to a Friend
- Report Inappropriate Content

Jun 2, 2016 10:34 AM
(4583 views)

The cause is collinearity among the independent variables.

The overall test just says that the model including all the variables is better than the mean of the response.

The test for significance of the individual parameters is conditioned on all the other parameters in the model.

If there is substantial correlation between two independent variables, both important in explaining the response, then neither will be significant with the other in the model.

Collinearity can be more complicated than just two variables being correlated. It can happened if one variable is correlated with a linear combination of several other variables which is common when one has many independent variables.

The solution in general is to identify the subsets of variables that are highly correlated among each other and use a subset that is not too correlated.

Not being a JMP expert, more SAS, I will leave it to others to give you more specific advice on how do identify the highly correlated subsets

Some ideas that come to mind are looking at pairwise correlations. Another idea is to use the partition platform to pick candidate independent variables.

If your goal is prediction, then using recursive partitioning is fine. If you goal is inference, using recursive partitioning to select variables will bias the p-values small and make any inference suspect.

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Permalink
- Email to a Friend
- Report Inappropriate Content

Jun 5, 2016 4:56 AM
(2990 views)

Thank you very much for the replay, I will check if I have correlation between my parameters

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Permalink
- Email to a Friend
- Report Inappropriate Content

Jun 3, 2016 5:43 AM
(2990 views)

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Permalink
- Email to a Friend
- Report Inappropriate Content

Jun 3, 2016 10:40 AM
(2990 views)

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Permalink
- Email to a Friend
- Report Inappropriate Content

Jun 5, 2016 5:01 AM
(2990 views)