我一直在研究一个曲线拟合脚本，该脚本将 3 个指数修改的高斯 (EMG) 拟合到卷积曲线。我的基函数类似于高斯分布，但包括第三个参数(前两个是 mu 和 sigma )，它确定函数的指数分量的权重。
所以总的来说，每个 EMG 峰值需要 3 个参数，加上一个幅度系数(为了匹配值 > 1.0 的实验数据)
要对 3 个 EMG 峰进行去卷积，要最小化的参数总数为 3x4 = 12
在某些情况下，拟合效果很好，但在许多情况下它无法收敛，并返回这样的错误
Convergence failure: false convergence (8)
仅经过 50 次左右的迭代(远低于限制)。
通过使用跟踪选项，我可以看到结果非常接近匹配数据。通过根据我的初始估计值绘制曲线，还可以看出起始参数与数据在合理的范围内:
数据 = 黑色(添加噪声)，初始 = 橙色，错误前的最终迭代 = 红色
这是我的代码示例，我在其中调用
nls() :t <- 0.05
fit <- nls(y ~ emgmix(a,b,c,d,a1,b1,c1,d1,a2,b2,c2,d2),
start = list(
a=pk1coef[2],
b=pk1coef[2],
c=t,
d=y[pk1c[2]]*40,
a1=pk2coef[1],
b1=pk2coef[2],
c1=t,
d1=y[pk2c[2]]*40,
a2=pk3coef[1],
b2=pk3coef[2],
c2=t,
d2=y[pk3c[2]]*40),
lower=rep(0.001,12),
control = list(maxiter = 1000),
trace = TRUE,
algorithm = "port",
)
那么为什么当算法似乎成功时我会收到错误消息？
0: 562831.45: 341.700 10.6000 0.0500000 27623.1 419.300 10.8000 0.0500000 2132.38 497.000 14.1000 0.0500000 1026.47
1: 405050.97: 341.603 10.5350 0.0508866 27738.3 419.883 10.7618 0.0471600 2065.57 498.294 14.0557 0.0465954 1057.21
2: 115191.71: 341.507 10.5354 0.0556600 27858.3 421.299 10.1276 0.0418391 1986.87 503.484 13.9263 0.0356617 1262.92
3: 38076.077: 342.417 11.2347 0.0632863 27377.3 420.770 14.8188 0.0546385 2213.08 505.655 18.1187 0.0495791 1407.27
4: 36401.368: 343.360 11.7864 0.0723805 26889.9 426.228 23.2991 0.115937 2330.60 507.362 26.3221 0.0784007 1706.85
5: 16394.715: 343.437 11.7838 0.0741048 26883.4 423.172 19.5157 0.154983 2482.43 519.106 27.3302 0.165639 1558.34
6: 12437.878: 343.449 11.7884 0.0743107 26868.4 426.309 21.3703 0.207416 2569.34 517.635 24.8692 0.263019 1512.44
7: 12248.298: 343.429 11.7789 0.0740482 26885.0 426.114 20.9106 0.213771 2551.15 516.084 24.6528 0.200320 1527.81
8: 12235.845: 343.430 11.7791 0.0740580 26884.1 426.175 20.9728 0.214606 2555.89 515.690 24.4315 0.192340 1523.57
9: 12230.776: 343.430 11.7794 0.0740656 26883.7 426.227 20.9872 0.217407 2556.37 515.362 24.3697 0.180294 1523.84
10: 12217.446: 343.432 11.7803 0.0740936 26881.7 426.645 21.0955 0.238821 2558.55 514.148 24.1081 0.139162 1524.57
11: 12185.853: 343.435 11.7813 0.0741224 26879.7 427.203 21.2201 0.274725 2561.21 513.228 23.8124 0.126246 1525.05
12: 12174.404: 343.436 11.7819 0.0741410 26878.4 427.589 21.2985 0.310384 2564.07 512.065 23.4146 0.106315 1524.86
13: 12161.212: 343.437 11.7826 0.0741606 26877.1 427.933 21.3557 0.351018 2565.29 512.085 23.3748 0.109496 1524.09
14: 12155.955: 343.437 11.7826 0.0741621 26876.9 428.243 21.3974 0.394982 2565.96 511.729 23.2536 0.104486 1524.67
15: 12152.489: 343.438 11.7827 0.0741652 26876.7 428.497 21.4262 0.441353 2566.25 511.693 23.2270 0.104343 1524.34
16: 12150.125: 343.438 11.7829 0.0741713 26876.3 428.714 21.4500 0.491154 2566.61 511.637 23.2104 0.103651 1524.53
17: 12148.632: 343.438 11.7829 0.0741714 26876.3 429.008 21.4756 0.569129 2566.55 511.659 23.2185 0.103983 1524.51
18: 12147.015: 343.438 11.7827 0.0741674 26876.5 429.225 21.4869 0.653321 2566.19 511.648 23.2186 0.103855 1524.68
19: 12145.989: 343.438 11.7828 0.0741677 26876.4 429.391 21.4974 0.738613 2566.22 511.659 23.2218 0.103998 1524.65
20: 12145.369: 343.438 11.7829 0.0741710 26876.2 429.533 21.5074 0.830413 2566.43 511.651 23.2199 0.103902 1524.69
21: 12145.021: 343.438 11.7829 0.0741707 26876.2 429.685 21.5152 0.947698 2566.43 511.656 23.2211 0.103965 1524.66
22: 12144.714: 343.438 11.7828 0.0741698 26876.3 429.815 21.5202 1.08360 2566.35 511.653 23.2208 0.103927 1524.70
23: 12144.463: 343.438 11.7828 0.0741698 26876.3 429.913 21.5239 1.22124 2566.36 511.656 23.2217 0.103966 1524.69
24: 12144.317: 343.438 11.7829 0.0741705 26876.2 429.992 21.5273 1.35908 2566.42 511.651 23.2198 0.103907 1524.69
25: 12144.214: 343.438 11.7829 0.0741712 26876.2 430.059 21.5299 1.50140 2566.47 511.654 23.2205 0.103943 1524.67
26: 12144.204: 343.438 11.7829 0.0741712 26876.2 430.059 21.5300 1.51704 2566.50 511.650 23.2189 0.103890 1524.67
27: 12144.204: 343.438 11.7829 0.0741713 26876.2 430.059 21.5303 1.51705 2566.51 511.650 23.2188 0.103891 1524.67
28: 12144.204: 343.438 11.7829 0.0741714 26876.2 430.059 21.5305 1.51706 2566.53 511.651 23.2185 0.103891 1524.65
29: 12144.204: 343.438 11.7829 0.0741714 26876.2 430.059 21.5305 1.51706 2566.53 511.651 23.2185 0.103891 1524.65
30: 12144.204: 343.438 11.7829 0.0741714 26876.2 430.059 21.5305 1.51706 2566.53 511.651 23.2185 0.103891 1524.65
31: 12144.204: 343.438 11.7829 0.0741714 26876.2 430.059 21.5305 1.51706 2566.53 511.651 23.2185 0.103891 1524.65
最佳答案
我遇到了类似的问题，所以我所做的是使用 warnOnly=T“强制”新的迭代。 ，这将导致实际估计，然后将这些估计用作第二个 nls() 中的新起始值.这就是我的代码最终的样子:
a_start2 = 40
b_start2 = 200
p_start2 = 16.5 * mean(no.stage[Position == i & Stage == j & Year == k]) + 74.167
subset1 = which(Position == i & Stage == j & Year == k)
m2 = nls(
Percent ~ ((a) / sqrt(2*b*pi)) * exp(-(((DAFB - p)^2) / (2*b))),
start = list(a = a_start2, b = b_start2, p = p_start2),
control = list(maxiter = 50000, minFactor = 1/2000, warnOnly = TRUE),
algorithm = "port",
lower = list(a = 0.1, b = 100, p = -100),
upper = list(a = 200, b = 800, p = 400),
subset = subset1
)
print(summary(m2))
a_start3 = coef(summary(m2))["a", "Estimate"]
b_start3 = coef(summary(m2))["b", "Estimate"]
p_start3 = coef(summary(m2))["p", "Estimate"]
m3 = nls(
Percent ~ ((a) / sqrt(2*b*pi)) * exp(-(((DAFB - p)^2) / (2*b))),
start = list(a = a_start3, b = b_start3, p = p_start3),
control = list(maxiter = 50000, minFactor = 1/2000, warnOnly = TRUE),
algorithm = "port",
lower = list(a = 0.1, b = 100, p = -100),
upper = list(a = 200, b = 800, p = 400),
subset = subset1
)
关于r - nls() 错误收敛(尽管起始值很好)，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/38064979/