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 advanced(?) data masking options for sigmoidialfit		  New Topic  Reply to Topic
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suras

Singapore
1 Posts
Posted - 02/01/2010 :  01:23:58 AM  Show Profile  Edit Topic  Reply with Quote  View user's IP address  Delete Topic
Hi,
First: I am a newbie on Origin. I tried to find answers for my questions on the forum but without luck. However since I am new to this, it is likely already described but i just dont know the right terms to use for the search... Sorry if that is the case.

Anyway; my question
General problem: to do intelligent masking of data before fitting.

I am analyzing multiple (96 as minimum, sometimes over 1000) sigmoidial curves.
To get good precision fits with low and well behaved residuals its crucial that the all the data points that's not a part of the transition are masked. The values for this (the points that needs to be masked) is not global, so the masking has to be done for each curve to be fitted individually. This is what I want to learn how to do. So i need guidance in how to approach this in the most intelligent way.

In the most simple case, with just one well defined transition, just normalizing the dataset and going with max and min can be "ok" but not more. But on several occasions i have more complex data to fit with multiple transitions or a lot of noise.

I guess this is a problem for most sigmoidial fitting?

Thanks in advance
/S

easwar

USA
1964 Posts
Posted - 02/01/2010 :  09:53:19 AM  Show Profile  Edit Reply  Reply with Quote  View user's IP address  Delete Reply
Hi,

Can you send us some of your data? Please include best and worst case datasets, and refer to this post in your message. You can send files using the instructions on this page:
http://originlab.com/index.aspx?s=1&lm=123&pid=752

Easwar
OriginLab
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