r - Very high residual Sum-of-Squares -

i'm having problem square sum-of-residues of fitting. square sum of residues high indicates fit not good. however, visually looks fine have high residual value ... can me know what's going on?

my data:

x=c(0.017359, 0.019206, 0.020619, 0.021022, 0.021793, 0.022366, 0.025691, 0.025780, 0.026355, 0.028858, 0.029766, 0.029967, 0.030241, 0.032216, 0.033657,  0.036250, 0.039145, 0.040682, 0.042334, 0.043747, 0.044165, 0.044630, 0.046045, 0.048138, 0.050813, 0.050955, 0.051910, 0.053042, 0.054853, 0.056886, 0.058651, 0.059472, 0.063770,0.064567, 0.067415, 0.067802, 0.068995, 0.070742,0.073486, 0.074085 ,0.074452, 0.075224, 0.075853, 0.076192, 0.077002,  0.078273, 0.079376, 0.083269, 0.085902, 0.087619, 0.089867, 0.092606, 0.095944, 0.096327, 0.097019, 0.098444, 0.098868, 0.098874, 0.102027, 0.103296,  0.107682, 0.108392, 0.108719, 0.109184, 0.109623, 0.118844, 0.124023, 0.124244, 0.129600, 0.130892, 0.136721, 0.137456, 0.147343, 0.149027, 0.152818, 0.155706,0.157650, 0.161060, 0.162594, 0.162950, 0.165031, 0.165408, 0.166680, 0.167727, 0.172882, 0.173264, 0.174552,0.176073, 0.185649, 0.194492,  0.196429, 0.200050, 0.208890, 0.209826, 0.213685, 0.219189, 0.221417, 0.222662, 0.230860, 0.234654, 0.235211, 0.241819, 0.247527, 0.251528, 0.253664,  0.256740, 0.261723, 0.274585, 0.278340, 0.281521, 0.282332, 0.286166, 0.288103, 0.292959, 0.295201, 0.309456, 0.312158, 0.314132, 0.319906, 0.319924,  0.322073, 0.325427, 0.328132, 0.333029, 0.334915, 0.342098, 0.345899, 0.345936, 0.350355, 0.355015, 0.355123, 0.356335, 0.364257, 0.371180, 0.375171, 0.377743, 0.383944, 0.388606, 0.390111, 0.395080, 0.398209, 0.409784, 0.410324, 0.424782 )   y= c(34843.40, 30362.66, 27991.80 ,28511.38, 28004.74, 27987.13, 22272.41, 23171.71, 23180.03, 20173.79, 19751.84, 20266.26, 20666.72, 18884.42, 17920.78, 15980.99, 14161.08, 13534.40, 12889.18, 12436.11, 12560.56, 12651.65, 12216.11, 11479.18, 10573.22, 10783.99, 10650.71, 10449.87, 10003.68,  9517.94,  9157.04,  9104.01,  8090.20,  8059.60,  7547.20,  7613.51,  7499.47,  7273.46,  6870.20,  6887.01, 6945.55,  6927.43,  6934.73,  6993.73,  6965.39,  6855.37,  6777.16,  6259.28,  5976.27,  5835.58,  5633.88,  5387.19,  5094.94,  5129.89,  5131.42,  5056.08,  5084.47,  5155.40,  4909.01,  4854.71, 4527.62,  4528.10,  4560.14,  4580.10,  4601.70,  3964.90,  3686.20,  3718.46,  3459.13,  3432.05,  3183.09,  3186.18,  2805.15,  2773.65,  2667.73,  2598.55,  2563.02,  2482.63,  2462.49,  2478.10, 2441.70,  2456.16,  2444.00,  2438.47,  2318.64,  2331.75,  2320.43,  2303.10,  2091.95,  1924.55, 1904.91,  1854.07,  1716.52,  1717.12,  1671.00,  1602.70,  1584.89,  1581.34,  1484.16,  1449.26, 1455.06,  1388.60,  1336.71,  1305.60,  1294.58,  1274.36,  1236.51,  1132.67,  1111.35,  1095.21,  1097.71,  1077.05,  1071.04,  1043.99,  1036.22,   950.26,   941.06,   936.37,   909.72,   916.45, 911.01, 898.94,   890.68,   870.99,   867.45,   837.39,   824.93,   830.61,   815.49,   799.77,   804.84,   804.88,   775.53,   751.95,   741.01,   735.86,   717.03,   704.57,   703.74,   690.63, 684.24,   650.30,   652.74,   612.95 ) 

then make fit using nlslm function (minpack.lm package):

library(magicaxis) library(minpack.lm)  sig.backg=3*10^(-3)   mod <- nlslm(y ~ *( 1 + (x/b)^2 )^c+sig.backg,              start = c(a = 0, b = 1, c = 0),              trace = true)  ## plot data magplot(x, y, main = "data", log = "xy", pch=16) ## plot fitted values lines(x, fitted(mod), col = 2, lwd = 4 ) 

this value residue:

> print(mod) nonlinear regression model   model: y ~ * (1 + (x/b)^2)^c + sig.backg    data: parent.frame()                   b          c  68504.2013     0.0122    -0.6324   residual sum-of-squares: 12641435  number of iterations convergence: 34  achieved convergence tolerance: 0.0000000149 

sum-of-squares residual high : 12641435 ...

is or wrong adjustment? bad?

it makes sense, since squared mean of response variable 38110960. can scale data if prefer work smaller numbers.