"in non linear modelling finding appropriate starting values is
something like an art"... (maybe from somewhere in Crawley , 2007) Here
a colleague and I just want to compare different response models to a
null model. This has worked OK for almost all the other data sets except
that one (dumped below). Whatever our trials and algorithms, even
subsetting data (to check if some singular point was the cause of the
mess), we do not reach convergence... or screw up with singular
gradients (?) etc...
eg:
nls(pourcma~SSlogis(transat, Asym, xmid, scal), start=c(Asym=30,
xmid=0.07, scal=0.02),data=bdd, weights=sqrt(nbfeces),trace=T,alg="plinear")
As anyone a hint about an alternate approach to fit a model ? Or an idea
to get evidence that such model cannot be fitted to the data....
bdd <-
structure(list(transat = c(0.0697, 0.13079, 0.314265, 0.241613,
0.039319, 0, 0, 0, 0, 0, 0.0805, 0.41, 0.30585, 0.27465, 0.06085,
0.09114, 0.05766, 0.036983, 0.093186, 0.046624, 0, 0, 0, 0, 0.000616,
0, 0.0025, 0.0325, 0.03125, 0.04599, 0.38398, 0.524505, 0.450337,
0.061831, 0.133926, 0.091806, 0.00928, 0.25114, 0.3074, 0.431056,
0.026158), transma = c(0.04141, 0.01599, 0.101803, 0.002378,
0.039319, 0.00472459016393443, 0.0031016393442623, 0.000178524590163934,
0.00255704918032787, 0.000346229508196721, 0.0665, 0.012, 0.0553,
0.0045, 0.0056, 0.00155, 0.00124, 0.011966, 0.001736, 0.004712,
3.62903225806452e-05, 9.79838709677419e-05, 2.20161290322581e-05,
0.00462, 0.0100644444444444, 0.00213111111111111, 0.046, 0.005,
0.01195, 0.07154, 0.08468, 0.141182, 0.086578, 0.027959, 0.003159,
0.003081, 0.13862, 0.00754, 0.078648, 0.068324, 0.025288), nbfeces = c(22L,
26L, 43L, 30L, 35L, 25L, 21L, 36L, 34L, 37L, 23L, 32L, 40L, 35L,
30L, 16L, 25L, 37L, 37L, 34L, 31L, 35L, 41L, 31L, 34L, 39L, 5L,
14L, 31L, 13L, 21L, 34L, 32L, 36L, 36L, 40L, 31L, 35L, 39L, 29L,
32L), pourcma = c(50, 34.6153846153846, 27.9069767441860, 43.3333333333333,
65.7142857142857, 32, 28.5714285714286, 22.2222222222222, 50,
10.8108108108108, 26.0869565217391, 40.625, 12.5, 22.8571428571429,
43.3333333333333, 6.25, 4, 10.8108108108108, 16.2162162162162,
23.5294117647059, 25.8064516129032, 45.7142857142857, 39.0243902439024,
25.8064516129032, 41.6666666666667, 27.5, 20, 14.2857142857143,
22.5806451612903, 15.3846153846154, 38.0952380952381, 17.6470588235294,
78.125, 61.1111111111111, 25, 37.5, 22.5806451612903, 40, 17.9487179487179,
41.3793103448276, 50), pourcat = c(22.7272727272727, 30.7692307692308,
41.8604651162791, 56.6666666666667, 5.71428571428571, 0, 0, 0,
0, 0, 30.4347826086957, 15.625, 45, 74.2857142857143, 13.3333333333333,
50, 12, 18.9189189189189, 27.0270270270270, 20.5882352941176,
0, 0, 0, 0, 0, 5, 40, 0, 0, 7.69230769230769, 9.52380952380952,
38.2352941176471, 59.375, 5.55555555555556, 41.6666666666667,
42.5, 9.67741935483871, 14.2857142857143, 51.2820512820513,
79.3103448275862,
6.25)), .Names = c("transat", "transma", "nbfeces", "pourcma",
"pourcat"), class = "data.frame", row.names = c(NA, -41L))
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