Hi David, my first attempt to work through your code was successful, my predicted line is pretty close to the ecdf-function. I have no idea why you inverted the gumbel-function and what the advantage of this strategy is?
I interpret your (1:100/100)-trick like this: you build a sequence of 100 Tics. In addition to this 100 is identical to the number of created points. In my real world situation, ecdf() will produce ties, same values, and this results that I do not know what number knot() will return, then this will result in a non-symmetric-data.frame()-error. R-Code: ############### Tutorial 3, Gamma-Distribution-Points to Gumbel ################ # --- create Points --- # http://r.789695.n4.nabble.com/ecdf-to-nls-how-to-transform-data-td3671754.html print( "Tutorial 3" ) p <- rgamma( 100, 2) print( p ) ep <- ecdf( p ) x <- knots(ep) hist( p, main="Tutorial 3" ) ypre <- seq( from=1, to=100, by=1 ) y <- ypre / 100 df <- data.frame( x=x, y=y ) plot( df ) res <- nls( y ~ exp( - exp( - ( x - mue ) / beta )), start=list(mue=2, beta=2), data=df, trace=TRUE ) print( summary(res) ) #print( str(res)) lines( x, predict( res, list( x=x )), lty = 1, col = "blue" ) # predicted vals Console-Output: [1] "Tutorial 3" [1] 4.75748951 5.36642043 2.03025702 1.80216440 1.40745277 0.91393251 [7] 1.30575505 2.07451301 1.18500815 0.24972503 1.36604865 2.36786796 [13] 1.37386798 1.72390843 1.93700139 0.78722468 2.92828385 1.51165415 [19] 4.17000960 2.98356424 1.20195996 1.85899533 0.94863757 1.50438053 [25] 0.50854274 3.32760075 2.21334316 0.58463817 2.26985676 2.88033389 [31] 2.00718903 1.19043470 1.62945752 0.32010478 0.54837755 1.32965131 [37] 3.32024573 0.53825226 3.30077557 0.46426513 1.00681720 0.59276030 [43] 1.31431609 2.27822419 2.56404713 0.52459218 1.05228996 2.23799673 [49] 1.14438962 2.41311612 1.40254244 1.20379073 0.44195457 0.95880408 [55] 1.45254027 4.49645001 2.18826796 1.07161515 1.62617544 3.15506003 [61] 2.15611491 2.43261017 1.40293461 2.79977886 3.44503201 3.25282551 [67] 0.91570531 0.70243512 5.67971774 3.55532192 1.71515046 2.97718949 [73] 0.47145131 0.32879089 0.60735231 0.92388638 4.29277857 1.73681839 [79] 0.09387383 2.81281295 1.84419058 2.63070353 1.52298124 2.89761263 [85] 1.05251987 1.24258703 3.09130229 2.66738574 3.17035060 2.15287327 [91] 2.63042751 1.69736779 3.21126033 1.56920999 2.68985477 0.82952068 [97] 3.62230865 1.65286592 2.76798783 2.08091935 Formula: y ~ exp(-exp(-(x - mue)/beta)) Parameters: Estimate Std. Error t value Pr(>|t|) mue 1.382744 0.005926 233.3 <2e-16 *** beta 0.984836 0.008816 111.7 <2e-16 *** -- View this message in context: http://r.789695.n4.nabble.com/ecdf-to-nls-how-to-transform-data-tp3671754p3674227.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.