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

[agent dunham]

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

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