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MathGroup Archive 2004

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Re: Regression with missing values

  • To: mathgroup at smc.vnet.net
  • Subject: [mg51837] Re: Regression with missing values
  • From: The Phantom <phantom at aol.com>
  • Date: Wed, 3 Nov 2004 01:24:47 -0500 (EST)
  • References: <clpr5j$a38$1@smc.vnet.net> <clsui7$3uv$1@smc.vnet.net>
  • Sender: owner-wri-mathgroup at wolfram.com

On Fri, 29 Oct 2004 08:21:27 +0000 (UTC), ludenscheid1 at hotmail.com (Nikolas Kiefer) wrote:

>Actually, I didn't want to delete those entries.  To be a little bit
>more explicit about my data, I have circa 5 independent variables
>which place the 6th dependent variable (the analysis result) into a
>category.  Unfortunately, sometimes there exist data for which 1 of
>the 5 independent variables cannot be determined, and thus the data
>really can't be placed into a category.  It's at this point that I was
>hoping it would be possible to still make use of the 6th data variable
>(which depends on the first 5 vars) by somehow "estimating" the
>category to which it should belong to.
>
>I know that certain statistical packages deal with such circumstances
>without simply deleting the data, but can Mathematica?

   If in fact Mathematica makes provision for this, are you sure that the technique
Mathematica implements is what *you* want to do?  You will probably need to deal with the
missing data yourself. See the following paper which also has a number of references to
further explore the problem:

"Comparing Regressions When Some Predictor Values Are Missing", Technometrics, Vol. 18,
No. 2, May 1976

>
>Nik
>
>Bill Rowe <readnewsciv at earthlink.net> wrote in message news:<clpr5j$a38$1 at smc.vnet.net>...
>> On 10/27/04 at 1:54 AM, ludenscheid1 at hotmail.com (Nikolas Kiefer)
>> wrote:
>> 
>> >Can Mathematica handle regression where the dependent variables
>> >sometimes have missing values?  If so, how are these missing values
>> >to be represented in the data?
>> 
>> How do you want the missing values treated? Simply ignored? If so, code the missing values in any manner you find suitable to easily distinguish them from valid values and use DeleteCases to delete them from the data matrix given to the regression routine you want to use.
>> 
>> For example, I have a data file with optical power measurements made in dBm. Valid measurements are negative numbers. So, I used 9999 to replace the missing values. Then I use DeleteCases[data,{_,9999}] to strip these values from the data matrix.


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