对于这个非线性方程
y = a*(1-exp(-(x/b)))^c
通过使用最小二乘获得参数的估计值并
x <- c(3, 33, 146, 227, 342, 351, 353, 444, 556, 571, 709, 759, 836,
860, 968, 1056, 1726, 1846, 1872, 1986, 2311, 2366, 2608, 2676,
3098, 3278, 3288, 4434, 5034, 5049, 5085, 5089, 5089, 5097, 5324,
5389, 5565, 5623, 6080, 6380, 6477, 6740, 7192, 7447, 7644, 7837,
7843, 7922, 8738, 10089, 10237, 10258, 10491, 10625, 10982, 11175,
11411, 11442, 11811, 12559, 12559, 12791, 13121, 13486, 14708,
15251, 15261, 15277, 15806, 16185, 16229, 16358, 17168, 17458,
17758, 18287, 18568, 18728, 19556, 20567, 21012, 21308, 23063,
24127, 25910, 26770, 27753, 28460, 28493, 29361, 30085, 32408,
35338, 36799, 37642, 37654, 37915, 39715, 40580, 42015, 42045,
42188, 42296, 42296, 45406, 46653, 47596, 48296, 49171, 49416,
50145, 52042, 52489, 52875, 53321, 53443, 54433, 55381, 56463,
56485, 56560, 57042, 62551, 62651, 62661, 63732, 64103, 64893,
71043, 74364, 75409, 76057, 81542, 82702, 84566, 88682)
y <- c(1:136)
df <- data.frame(x,y)
fit <- nls(y ~ a*(1-exp(-x/b))^c, data=df, start = list( a=100,b=1000,c=0.5),
algorithm="port",lower=list(a=100,b=100,c=0.5),upper=list(a=200,b=10000,c=2))
如何使用这个非线性方程的最大似然估计（MLE）来做这个？