Hi, everyone,

I have one question about the Poisson regression.

I have three data frames: X, Y and Z, as follows.

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X

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Y

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Z

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Y1

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Y2

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Y3

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Z1

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Z2

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Z3

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4

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446.3768

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-3.25575

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27.85502

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58.89589

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21.44272

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1.429652

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9

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437.569

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-0.998

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24.81672

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57.96077

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19.24162

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1.591081

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13

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426.9423

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1.20875

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31.47058

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58.06966

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17.69528

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1.933682

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4

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413.7538

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2.03325

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36.00697

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54.45679

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16.94219

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2.322985

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11

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400.1235

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2.6575

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34.32918

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52.57843

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16.59206

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2.392409

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16

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387.4307

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4.24225

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33.24652

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48.60116

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16.29428

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2.542971

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I want to quantify the relative contributions of Y and Z to the variation of X using variation partitioning method. However, the X only has one column (500 rows) and contains some zero values (about 90 rows are zero), while both Y and Z contain three explanatory variables. As I know, the variation partitioning method is based on the linear regression model when the response data table only has one column. Can I use the variation partitioning method directly? Or should I use the Poisson regression method because many zero values are included in my response data table? As we know, adjusted r-square is not available in Poisson regression; instead, we can only get a pseudo r-square. Furthermore, I have not found that Poisson regression contains partial regression algorithm to quantify the relative importance of two explanatory data table. Therefore, if I have to use the Poisson regression, how can I achieve my aim?

Thanks for any help and best wishes for you!

 

Lin

July 30th, 2012

 
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