title: Fitting logarithmic curve in R
body: <p>If I have a set of points in R that are linear I can do the following to plot the points, fit a line to them, then display the line:</p>
  
  <pre><code>x=c(61,610,1037,2074,3050,4087,5002,6100,7015)
  y=c(0.401244, 0.844381, 1.18922, 1.93864, 2.76673, 3.52449, 4.21855, 5.04368, 5.80071)
  
  plot(x,y)    
  Estimate = lm(y ~ x)    
  abline(Estimate)
  </code></pre>
  
  <p>Now, if I have a set of points that looks like a logarithmic curve fit is more appropriate such as the following: </p>
  
  <pre><code>x=c(61,610,1037,2074,3050,4087,5002,6100,7015)        
  y=c(0.974206,1.16716,1.19879,1.28192,1.30739,1.32019,1.35494,1.36941,1.37505)
  </code></pre>
  
  <p>I know I can get the standard regression fit against the log of the x values with the following:</p>
  
  <pre><code>logEstimate = lm(y ~ log(x))
  </code></pre>
  
  <p>But then how do I transform that logEstimate back to normal scaling and plot the curve against my linear curve from earlier? </p>