Dear R gurus,
I have the following x (conc)-y (rk) data.
rk<-c(0.016,0.032,0.048,0.095,0.111,0.143,0.190,0.206,0.222,0.270,0.286,0.302,0.317,0.381,0.397,0.444,0.460,
0.476,0.492,0.508,0.524,0.540,0.556,0.651,0.698,0.714,0.810,0.825,0.841,0.921,0.937,0.952,0.968,0.984,1.000)
cc<-c(0.4,0.53,1,1.595,1.643,1.8,2.667,3.315,3.6,3.9,3.908,4,4.429,5.8,6,7.826,8.54,8.66,9.298,9.601,10,10,11.056,
15.577,23.402,23.615,80,80,89,710,2950,6400,6400,6800,69755.766)
conc<-log(cc+1)
Assuming a log-normal distribution, I would like to bootstrap only the
response variable (rk; say 100 times per x), plot simultaneously the
log-normal curves for each bootstrap replicate, estimate the 95% confidence
interval, and extract the central tendency and confidence intervals for a
specific rk (i.e., 0.15). I have attempted to follow the code by John Fox
with little success. Any help will be greatly appreciated!.
Adriana
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