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

[Frank Harrell]

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
--
View this message in context: http://r.789695.n4.nabble.com/lm-v1-log-v1-improve-adj-Rsq-any-sense-tp3396935p3397136.html
Sent from the R help mailing list archive at Nabble.com.