[R] Why terms are dropping out of an lm() model
john at pitney.org
Fri Aug 27 00:17:24 CEST 2004
> John Pitney wrote:
>> Hi all!
>> I'm fairly new to R and not too experienced with regression. Because
>> of one or both of those traits, I'm not seeing why some terms are being
>> dropped from my model when doing a regression using lm().
>> I am trying to do a regression on some experimental data d, which has
>> two numeric predictors, p1 and p2, and one numeric response, r. The aim
>> is to compare polynomial models in p1 and p2 up to third order. I don't
>> understand why lm() doesn't return coefficients for the p1^3 and p2^3
>> terms. Similar loss of terms happened when I tried orthonormal
>> polynomials to third order.
>> I'm satisfied with the second-order regression, by the way, but I'd
>> still like to understand why the third-order regression doesn't work
>> like I'd expect.
>> Can anyone offer a pointer to help me understand this?
>> Here's what I'm seeing in R 1.9.1 for Windows. Note the NA's for p1^3
>> and p2^3 in the last summary.
>> [stuff deleted]
>> -0.089823 -0.017707 0.001952 0.020820 0.059302
>> Coefficients: (2 not defined because of singularities)
> Did you miss reading the above line? Seems you supplied a singular model
> to `lm' and since the default for `lm' is `singular.ok = TRUE,' it just
> pivoted these columns out in the QR-decomposition.
Yes, I missed that line. The model matrix is indeed singular.
Thanks for the quick and helpful response, and sorry for posting before
thinking carefully enough!
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