I'm having a problem with the square sum-of-residues of an fitting. The square sum of the residues is too high which indicates that the fit is not very good. However, visually it looks fine to have this very high residual value ... Can anyone help me to 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 the nlsLM function (minpack.lm package):
library(magicaxis)
library(minpack.lm)
sig.backg=3*10^(-3)
mod <- nlsLM(y ~ a *( 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 is the residue:
> print(mod)
Nonlinear regression model
model: y ~ a * (1 + (x/b)^2)^c + sig.backg
data: parent.frame()
a b c
68504.2013 0.0122 -0.6324
residual sum-of-squares: 12641435
Number of iterations to convergence: 34
Achieved convergence tolerance: 0.0000000149
sum-of-squares residual is too high : 12641435 ...
Is that so or is something wrong with the adjustment? It is bad?
It makes sense, since the squared mean of your response variable is 38110960. You can scale your data if you prefer to work with smaller numbers.