# Curve fitting in R using nls

I'm trying to fit a curve over (the tail of) the following data:

`[1] 1 1 1 1 1 1 2 1 2 2 3 2 1 1 4 3 2 11 6 2 16 7 17 36[25] 27 39 41 33 42 66 92 138 189 249 665 224 309 247 641 777 671 532 749 506 315 292 281 130[49] 137 91 40 27 34 19 1`

I'm using the following function in R to accomplish this:

nls(y~axexp(-b*x^2),start=list(a=1,b=1),trace=TRUE)

However, I'm getting the following error:

3650202 : 1 1

Error in numericDeriv(form[[3L]], names(ind), env) :

Missing value or an infinity produced when evaluating the model

When using the following, artificial values for x and y, everything works just fine:

y=x*exp(-.5*x^2)+rnorm(length(x),0,0.1)

`x[1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90[20] 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35 1.40 1.45 1.50 1.55 1.60 1.65 1.70 1.75 1.80 1.85[39] 1.90 1.95 2.00 2.05 2.10 2.15 2.20 2.25 2.30 2.35 2.40 2.45 2.50 2.55 2.60 2.65 2.70 2.75 2.80[58] 2.85 2.90 2.95 3.00 3.05 3.10 3.15 3.20 3.25 3.30 3.35 3.40 3.45 3.50 3.55 3.60 3.65 3.70 3.75[77] 3.80 3.85 3.90 3.95 4.00 4.05 4.10 4.15 4.20 4.25 4.30 4.35 4.40 4.45 4.50 4.55 4.60 4.65 4.70[96] 4.75 4.80 4.85 4.90 4.95 5.00y[1] -0.080214106 0.075247488 0.076355116 -0.020087646 0.181314038 0.075832658 0.248303254[8] 0.364244010 0.453655908 0.347854869 0.514373164 0.384051249 0.618584696 0.515684390[15] 0.534737770 0.609279111 0.618936091 0.534443863 0.739118585 0.677679546 0.526011452[22] 0.645645150 0.578274968 0.589619834 0.476186241 0.621638333 0.601663144 0.535981735[29] 0.518434367 0.581735107 0.423872948 0.445335110 0.340884242 0.317121065 0.342683141[36] 0.278351104 0.402947372 0.429483276 0.276655872 0.108164828 0.389994138 0.372300257[43] -0.057320612 0.131271986 0.226212869 0.131171973 0.245970674 0.009926555 0.173465207[50] 0.141220590 0.280616078 0.108515613 0.117697407 0.130700771 0.058540888 0.251613512[57] 0.168094899 -0.058382571 0.123306762 -0.048605186 -0.010131767 0.076701962 -0.051982924[64] 0.058427540 0.144665070 0.063998841 -0.010495697 0.119868854 0.114447318 0.006759691[71] 0.025041761 -0.178145771 0.041547126 0.122084819 0.034283141 0.209140060 0.197024853[78] -0.005491966 -0.033260219 -0.028123314 -0.005775553 -0.040781462 0.090024896 0.116390743[85] -0.017811031 0.094039200 -0.147064060 -0.057249278 0.211587898 -0.066153592 0.032100332[92] -0.092756136 -0.125906598 0.136937364 0.046453010 0.002000336 -0.134047101 0.089748847[99] -0.019355567 -0.042158950 0.149594368`

Can anyone point out what I'm doing wrong? Thanks for your help.

Well I found the answer to my problem. The starting values for the real data are completely different from the dummy values: a=500 and b=.1 result in a nice fit. Just thought it might be useful to mention that here.