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
- :
- Nonlinear regression with unequally spaced data

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

May 24, 2011 5:55 PM
(993 views)

I am trying to fit a nonlinear function to data that is only a function of time but the time spacing between points is not constant. These are voltage measurements that were collected with a short time interval at first because the voltage was changing rapidly. This was followed by three other time periods where the time spacing between points was constant within each time period but the spacing got progressively larger as the voltage became more stable. All of the data has similar error bars in voltage.

My question is: will the nonlinear regression algorithm in JMP 8.0.2 work properly with unequally spaced data?

I have used it for fitting some data and it works fairly well. However, it sometimes will not fit the data with the largest time interval very well. It seems to me that the shorter time intervals will effectively be weighted more than the long time intervals because there are more data points per unit time. So I have tried compensating for this by using a weighting function that is 1 for the first interval and then progressively gets larger in proportion to the time interval. This seems to give a better fit (although the SSE is larger) but I haven't analyzed much data yet this way. I am just wondering if I am on the right track?

My question is: will the nonlinear regression algorithm in JMP 8.0.2 work properly with unequally spaced data?

I have used it for fitting some data and it works fairly well. However, it sometimes will not fit the data with the largest time interval very well. It seems to me that the shorter time intervals will effectively be weighted more than the long time intervals because there are more data points per unit time. So I have tried compensating for this by using a weighting function that is 1 for the first interval and then progressively gets larger in proportion to the time interval. This seems to give a better fit (although the SSE is larger) but I haven't analyzed much data yet this way. I am just wondering if I am on the right track?

- Tags:
- asfass

1 REPLY

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

May 25, 2011 12:42 AM
(953 views)