[R] Singularity in a regression?

[David Winsemius]

I saw Ted's reply and it is certainly sensible. I would wonder whether  
to model ought to be recast so that the scientific question is more  
clear? You are obviously studying the effect of different  
substitutions (F, Cl, Br, I, Me) and different positions around an  
aromatic ring (meta, para). Why not consider the order of  
electrophilicity (or possibly size) and the position as two different  
variables, one ordered and the other binomial?

After recoding, your formula might then look like  activity ~ electro  
+ position ... or possibly activity ~ electro + size + position, and  
you would be less likely to run into difficulties with collinearity.  
You would also have some science in your model rather than casting  
aimlessly about in the data. If your ordering is sensible, you end up  
testing with 2 or 3 degrees of freedom.

-- 
David Winsemius

On Feb 26, 2009, at 7:58 AM, Bob Gotwals wrote:

> R friends,
>
> In a matrix of 1s and 0s, I'm getting a singularity error.  Any  
> helpful ideas?
>
> lm(formula = activity ~ metaF + metaCl + metaBr + metaI + metaMe +
>    paraF + paraCl + paraBr + paraI + paraMe)
>
> Residuals:
>       Min         1Q     Median         3Q        Max
> -4.573e-01 -7.884e-02  3.469e-17  6.616e-02  2.427e-01
>
> Coefficients: (1 not defined because of singularities)
>            Estimate Std. Error t value Pr(>|t|)
> (Intercept)   7.9173     0.1129  70.135  < 2e-16 ***
> metaF        -0.3973     0.2339  -1.698 0.115172
> metaCl            NA         NA      NA       NA
> metaBr        0.3454     0.1149   3.007 0.010929 *
> metaI         0.4827     0.2339   2.063 0.061404 .
> metaMe        0.3654     0.1149   3.181 0.007909 **
> paraF         0.7675     0.1449   5.298 0.000189 ***
> paraCl        0.3400     0.1449   2.347 0.036925 *
> paraBr        1.0200     0.1449   7.040 1.36e-05 ***
> paraI         1.3327     0.2339   5.697 9.96e-05 ***
> paraMe        1.2191     0.1573   7.751 5.19e-06 ***
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Residual standard error: 0.2049 on 12 degrees of freedom
> Multiple R-squared: 0.9257,	Adjusted R-squared: 0.8699
> F-statistic: 16.61 on 9 and 12 DF,  p-value: 1.811e-05
>
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