Dear Michael,

Did you measure newborns? If not center age to a value that makes sense in relation with the range of age in your dataset. Then the intercept will be the height at the reference age. And most likeli non-negative.

Best regards,

Thierry

ir. Thierry Onkelinx

Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest

team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance

Kliniekstraat 25

1070 Anderlecht

Belgium

[hidden email]
www.inbo.be

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-----Oorspronkelijk bericht-----

Van:

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[hidden email]] Namens Michael Haenlein

Verzonden: dinsdag 6 december 2011 14:09

Aan:

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Onderwerp: [R] Regression model when dependent variable can only take positive values

Dear all,

I would like to run a regression of the form lm(y ~ x1+x2) where the dependent variable y can only take positive values. Assume, for example, that y is the height of a person (measured in cm), x1 is the gender (measured as a binary indicator with 0=male and 1=female) and x2 is the age of the person (measured in years).

When I run a simple lm(y ~ x1+x2), I obtain an intercept value that is negative. I interpret that in a way that a person who is male (x1=0) and just born (x2=0), has a negative height. This evidently does not make sense. I therefore assume that my estimates might be biased and that I need to use some other form of estimation that takes account of the fact that

y>0 for all observations.

Could anybody please tell me which type of regression would be most recommendable for this type of analysis?

Thanks very much in advance,

Michael

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