[R] how to plot a logarithmic regression line

[arun]

HI,
Try ?curve

fit <- lm(Mean_Percent_of_Range~log(No.ofPoints))
 coef(fit)
 #    (Intercept) log(No.ofPoints) 
  #     -74.52645         46.14392 



 plot(Mean_Percent_of_Range ~ No.ofPoints) 
curve(coef(fit)[[1]]+coef(fit)[[2]]*log(x),add=TRUE,col=2)


A.K.



I realize this is a stupid question, and I have honestly tried to find 
the answer online, but nothing I have tried has worked. I have two 
vectors of data: 

"Mean_percent_of_range" 
10.90000  17.50000  21.86667  25.00000  25.40000  26.76667  29.53333
  32.36667  43.13333  41.80000 50.56667  49.26667  50.36667  51.93333 
 59.70000  63.96667  62.53333  60.80000  64.23333  66.00000 74.03333 
 70.40000  77.06667  76.46667  78.13333  89.46667  88.90000  90.03333 
 91.60000  94.30000 95.50000  96.20000  96.50000  91.40000  98.20000 
 96.60000  97.40000  99.00000 100.00000 

and 
"No.ofPoints" 
5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 
39 40 41 42 43 

When I plot these, I get a logarithmic curve (as I should for this type of data) 
> plot(Mean_Percent_of_Range ~ No.ofPoints) 

All that I want to do is plot best fit regression line for that 
curve. From what I have read online, it seems like the code to do that 
should be 
> abline(lm(log(Mean_Percent_of_Range) ~ log(No.ofPoints))) 
but that gives me a straight line that isn't even close to fitting the data 

How do I plot the line and get the equation of that line and a correlation coefficient? 
Thanks