r - getting non linear multivariate regression parameter estimates -


i'm looking @ getting parameter estimates data has 3 dimensions. i've plotted out using manipulate function in mathematica. however, when use constants mathematica think fit, end single gradient error. so, there graphical method can use in r plot estimates against 3d plot of data, or have suggestions how rectify error?

distance<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,15 ,15 ,15 ,15 ,15 ,15 ,15 ,15 ,15 ,15 ,15 ,15, 15 ,15, 15 ,15 ,15 ,15,15 ,15 ,20, 20 ,20, 20 ,20, 20 ,20, 20 ,20 ,20 ,20 ,20 ,20 ,20, 20 ,20, 20 ,20, 20 ,20, 20) height<-c(400   ,300 ,  200,     0  ,-200 , -400  ,-600 , -800 ,-1000 ,-1000 ,-1200, -1220 ,-1300 ,-1400,-1400 ,-1500, -1600, -1700 ,-1700 ,-1800 ,-1900 ,  400 ,  200 ,    0  ,-200,  -400  ,-600 , -800,-1000 ,-1200, -1200 ,-1400 ,-1600 ,-1600 ,-1800 ,-2000 ,-2000 ,-2200 ,-2200 ,-2400 ,-2600 ,-2800,-3000  , 400 ,  200  ,   0  ,-200  ,-300 , -400 , -500  ,-600  ,-700  ,-800  ,-900 ,-1000 ,-1100,-1200 ,-1200 ,-1400, -1600 ,-1800 ,-1800 ,-2000 ,-2200 ,-2400 ,-2400 ,-2600 ,-2800 ,-3000  , 400,200  ,   0,  -200,  -400  ,-600  ,-800, -1000 ,-1200 ,-1400 ,-1600 ,-1600 ,-1800 ,-2000, -2200,-2400 ,-2400, -2600, -2800 ,-3000  ,1000 ,  800  , 600 ,  400  , 200  ,   0  ,-200 , -400 , -600, -800 ,-1000, -1200 ,-1400, -1600 ,-1800, -2000 ,-2200, -2400 ,-2600 ,-2800 ,-3000) value<-c(163301.080, 269704.110 ,334570.550, 409536.530, 433021.260 ,418962.060, 349554.460, 253987.570,124461.710, 140750.480  ,52612.790 , 54286.427  ,26150.025  ,14631.210  ,15780.244 ,  8053.618, 4402.581,   2251.137  , 2743.511 ,  1707.508 ,  1246.894 ,176232.060 ,270797.240 ,323096.710,333401.080, 311949.900 ,272821.770 ,189571.850 ,114263.560  ,51939.070  ,62578.665  ,36905.438,  22625.515,  22940.591  ,14576.295  , 9686.653 , 10344.214  , 6912.779  , 7092.919   ,5366.797, 4058.492,   3270.734  , 2528.644  ,89311.555 ,116698.175 ,143588.620 ,139203.190, 145399.445, 145635.715, 134671.110 ,128931.160 ,119734.835 ,108708.815 , 90221.955  ,81692.585 , 64882.275,58215.735,  60443.190  ,44690.690 , 33224.152 , 24140.272 , 24913.280  ,19082.689  ,13920.669, 11074.718,  10015.653   ,8743.850  , 7516.880 ,  6377.743 , 36888.842  ,43088.720  ,47904.490, 51298.710,  51120.887  ,47687.488  ,42238.912 , 38563.007 , 33902.918  ,28565.303  ,23700.862,24818.393,  21620.129 , 17816.061  ,15377.097 , 12992.321  ,12985.911  ,11177.941   ,9536.621,8357.279,  13052.178 , 14325.789  ,15120.314 , 16227.575  ,17226.307  ,18557.270  ,18680.326, 18844.544,  18205.607,  17770.311  ,16605.438 , 16062.309  ,14785.654  ,14324.493  ,13373.627,12135.392,  10632.699,   9155.762  , 8240.951,   6934.240  , 6475.927)   fit<-nls(value~a*(exp(-(height+b)^2/(2*c^2))+(distance-d)^2/(2*e^2))+g*exp(-abs((-h*height)^2+(-i*distance)^2))+f, start = list(a=300000,b=200,c=0.003,d=0,e=0.1,f=1100,g=50000,h=0.001,i=0.085)) summary(fit) 

thanks suggestions

though can't answer you'd find nls2 package useful. below quick test.

# fit nls fails fit<-nls(value~a*(exp(-(height+b)^2/(2*c^2))+(distance-d)^2/(2*e^2))+g*exp(-abs((-h*height)^2+(-i*distance)^2))+f,          start = list(a=300000,b=200,c=0.003,d=0,e=0.1,f=1100,g=50000,h=0.001,i=0.085),          algorithm = "plinear")  error in qr.solve(qr.b, cc) : singular matrix 'a' in solve  # fit nls2 looks successful library(nls2) fit2 <- nls2(value~a*(exp(-(height+b)^2/(2*c^2))+(distance-d)^2/(2*e^2))+g*exp(-abs((-h*height)^2+(-i*distance)^2))+f,           start = list(a=300000,b=200,c=0.003,d=0,e=0.1,f=1100,g=50000,h=0.001,i=0.085),           algorithm = "brute-force")  fit2 nonlinear regression model model: value ~ * (exp(-(height + b)^2/(2 * c^2)) + (distance - d)^2/(2 *     e^2)) + g * exp(-abs((-h * height)^2 + (-i * distance)^2)) +     f data: null       b       c       d       e       f       g       h        3.0e+05 2.0e+02 3.0e-03 0.0e+00 1.0e-01 1.1e+03 5.0e+04 1.0e-03 8.5e-02  residual sum-of-squares: 1.045e+21  number of iterations convergence: 9  achieved convergence tolerance: na  # still there issue summary(fit2) error in chol2inv(object$m$rmat()) :    element (8, 8) zero, inverse cannot computed 

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