Re: [R] lm ~ v1 + log(v1) + ... improve adj Rsq ¿any sense?

From: Frank Harrell <f.harrell_at_vanderbilt.edu>
Date: Tue, 22 Mar 2011 10:20:47 -0700 (PDT)

If you care about confidence interval coverage, type I error, or predictive accuracy, trying different models in this way is not the way to go.

Frank

agent dunham wrote:
>
> Dear all,
>
> I want to improve my adj - R sq. I 've chequed some established models and
> they introduce two times the same variable, one transformed, and the other
> not. It also improves my adj - R sq.
>
> But, isn't this bad for the collinearity? Do I interpret coefficients as
> usual?
>
> Estimate Std. Error t value Pr(>|t|)
> (Intercept) 1.73140 7.22477 0.240 0.81086
> v1 -0.33886 0.20321 -1.668 0.09705 .
> log(v1) 2.63194 3.74556 0.703 0.48311
> v2 -0.01517 0.01089 -1.394 0.16507
> log(v3) -0.45719 0.27656 -1.653 0.09995 .
> factor1 -1.81517 0.62155 -2.920 0.00392 **
> factor2 -1.87330 0.84375 -2.220 0.02759 *
>
> Analysis of Variance Table
>
> Response: height rise
> Df Sum Sq Mean Sq F value Pr(>F)
> v1 1 51.25 51.246 21.4128 6.842e-06 ***
> log(v1) 1 13.62 13.617 5.6897 0.018048 *
> v2 1 2.84 2.836 1.1850 0.277713
> log(v3) 1 3.02 3.024 1.2638 0.262357
> factor1 1 17.62 17.616 7.3608 0.007279 **
> factor2 1 11.80 11.797 4.9294 0.027586 *
> Residuals 190 454.71 2.393
>
> Thanks,
> user_at_host.com
>



Frank Harrell
Department of Biostatistics, Vanderbilt University
--
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