If  collinearity exists, one of the solutions is regulazation version of
regression. There are different types of regularization method. like Ridge,
LASSO, elastic net etc. For example, in  MASS package you can get ridge
regression.

Alex


On Thu, Feb 26, 2009 at 1:58 PM, Bob Gotwals <gotwals@ncssm.edu> 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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>

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