R help - Feb 2012 - glm.fit and pearson's correlation coefficient

I did a linear correlation of data using glm.fit and stored the output in the
object "f":
f <- glm.fit(x, y, w)
I am intereseted in estimating the quality of the correlation. I am used to
do it using pearson correlation coefficient "r" or "r^2".
Can I extract this
coefficient from the output of glm.fit? 
Is there another number in the output of glm.fit that represents the quality
of the correlation?

Here is a printout of the object f:
$coefficients
[1] 95772.92

$residuals
 [1]   -5146.208  -17221.948  -12743.726  -14536.236  -36311.931  -33651.931
 [7]  -75054.063  -73207.873 -136695.506 -142442.126 -268970.512 -260546.762
[13] -281117.024 -247116.524    9115.715   64791.715

$fitted.values
 [1]    74702.88    74702.88   149405.76   149405.76   299769.24   299769.24
 [7]   598580.75   598580.75  1197161.51  1197161.51  2394323.01  2394323.01
[13]  4788646.02  4788646.02 57463752.29 57463752.29

$effects
                                                                              
-81641362.05    -17217.24    -12734.32    -14526.83    -36293.05   
-33633.05 
                                                                              
   -75016.37    -73170.18   -136620.11   -142366.73   -268819.73  
-260395.98 
                                               <NA> 
  -280815.45   -246814.95     12734.59     68410.59 

$R
          [,1]
[1,] -852.4472

$rank
[1] 1

$qr
$qr
               [,1]
 [1,] -8.524472e+02
 [2,]  9.150126e-04
 [3,]  1.830025e-03
 [4,]  1.830025e-03
 [5,]  3.671781e-03
 [6,]  3.671781e-03
 [7,]  7.331832e-03
 [8,]  7.331832e-03
 [9,]  1.466366e-02
[10,]  1.466366e-02
[11,]  2.932733e-02
[12,]  2.932733e-02
[13,]  5.865466e-02
[14,]  5.865466e-02
[15,]  7.038559e-01
[16,]  7.038559e-01

$rank
[1] 1

$qraux
[1] 1.000915

$pivot
[1] 1

$tol
[1] 1e-11

attr(,"class")
[1] "qr"

$family

Family: gaussian 
Link function: identity 


$linear.predictors
 [1]    74702.88    74702.88   149405.76   149405.76   299769.24   299769.24
 [7]   598580.75   598580.75  1197161.51  1197161.51  2394323.01  2394323.01
[13]  4788646.02  4788646.02 57463752.29 57463752.29

$deviance
[1] 337719889404

$aic
[1] 429.7723

$null.deviance
[1] 5.570009e+15

$iter
[1] 2

$weights
 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

$prior.weights
 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

$df.residual
[1] 15

$df.null
[1] 15

$y
 [1]    69556.67    57480.93   136662.03   134869.52   263457.31   266117.31
 [7]   523526.69   525372.88  1060466.00  1054719.38  2125352.50  2133776.25
[13]  4507529.00  4541529.50 57472868.00 57528544.00

$converged
[1] TRUE

$boundary
[1] FALSE
> coefficients(f1)
[1] 95772.92



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