Dear All,

I've been using step function to find me the best model.this basically works
by using AIC score fucntion that is implemented on step(). The problem I'm
facing with lots of variables on the model for example :


step(lm(x1~x2,x3,x4,......x13)) sometimes gives me a warning message which
is :

AIC=- inf

Coefficients:
   (Intercept)     wnt3.values     wnt6.values   wnt10b.values
wnt9a.values
        2.3462         -0.4689          2.0730          1.2769
-0.2319
  sfrp1.values    wnt5b.values  sfrp1.1.values    sfrp5.values
fzd5.1.values
       -0.2597          0.3150          0.3811          0.5926
-1.5567
   fzd1.values     fzd4.values     fzd6.values     fzd7.values
fzd7.1.values
        0.6459         -2.3016          0.3636              NA
NA
   fzd8.values
            NA

Warning message:
attempting model selection on an essentially perfect fit is nonsense .

which stops the search.

Does this means that  Residual Sum of Squares (RSS) equals to zero that
makes AIC goes to -inf .And how would I overcome this problem.Can I for
example find those that have strong correlation with x1 first and then use
AIC score to find me the best model among them,.

Regards
Adel,

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