Question

我试图使用nls函数拟合Gompertz曲线，这总是给出了＆＃34;奇异渐变＆＃34;错误。我知道很多问题都是针对这个错误的，但我仍然无法弄清楚我的问题。我尝试了多个公式变体，起始值，nlsLM函数，......我觉得我错过了一些非常简单的东西，但我一直在搜索几个小时。这是我的数据和功能：

a=structure(data.frame(sl = c(15.31, 14.21, 23.99, 13.34, 15.14, 14.83, 
                    23.53, 17.31, 14.69, 17.9, 11.9, 14.98, 22.43, 17.84, 13.84, 
                    13.5, 11.54, 12.76, 15.56, 16.11, 12.17, 17.74, 14.62, 11.36, 
                    10.57, 15.69, 20.91, 10.46, 10.78, 8.84, 9.89, 15.22, 9.34, 8.82, 
                    11.3, 9.11, 16.64, 12.94, 8.77, 9.44, 8.33, 10.14, 10.87, 9.92, 
                    10.01, 9.95, 10.38, 9.44, 11.09, 10.35, 13.04, 13.1, 12.78, 9.55, 
                    10.62, 10.7, 15, 8.48, 12.86, 16.18, 15.88, 23.51, 7.13, 8.75, 
                    18.09, 14.94, 17.64, 18.14, 15.71, 10.12, 23.74, 14, 7.36, 11.1, 
                    11.04, 23, 20.25, 17.57, 23.48, 16.98, 16.38, 17.77, 13.67, 10.55, 
                    15.66, 13.82, 15.37, 18.75, 14.74, 11.94, 19.52, 11.6, 8.91, 
                    11.84, 13.47, 11.55, 10.51, 12.09, 10.66, 10.92, 11.57, 13.19, 
                    10.01, 14.13, 14.83, 13.44, 12.5, 15.94, 12.83, 13.02, 22.19, 
                    20.62, 13.14, 14.24, 15.12, 15.79, 15.9, 9.76, 12.44, 10.75, 
                    10.62, 10.99, 10.12, 11.51, 11.22, 9.69, 11.58, 11.57, 13.16, 
                    12.97, 21.37, 13.97, 10.45, 10.91, 9.06, 10.86, 11.01, 12.05, 
                    11.33, 11.51, 17.62, 12.98, 6.51, 11.42, 12.6, 13.28, 10.68, 
                    13.25, 15.55, 10.24, 13.14, 14.44, 11.75, 11.75, 12.73, 20.19, 
                    13.11, 8.2, 15.17, 17.24, 17.51, 16.95, 12.79, 12.12, 12.97, 
                    14.46, 12.14, 8.8, 10.86, 14.75, 18.15, 15.95, 10.85, 14.92, 
                    12.9, 12.11, 12.77, 12.86, 16.09, 8.84, 14.88, 13.37, 10.76, 
                    14.56, 13.28, 13.46, 9.35, 11.16, 12.79, 15.25, 14.98, 10.21, 
                    9.9, 11.74, 14.78, 15.56, 11.16, 10.42, 9.12, 12.13, 11.82, 9.75, 
                    12.85, 10.57, 11.8, 11.09, 10.45, 8.71, 12.23, 14.31, 15.55, 
                    11.15, 10.17, 12.33, 12.02, 10.02, 12.75, 11.27, 13.45, 11.75, 
                    14.36, 12.76, 9.29, 12.92, 12.08, 9.86, 11.01, 12.23, 12.2, 11.43, 
                    13.97, 10.95, 13.16, 11.34, 10.66, 16.02, 22.03, 11.15, 9.29, 
                    10.01, 13.15, 9.24, 11.99, 9.16, 12.1, 15.12, 15.18, 15.09, 15.64, 
                    19.34, 17.82, 21.6, 10.98, 12.93, 9.93, 13.3, 16.12, 11.15, 13.58, 
                    19.41, 8.03, 14.04, 15.43, 14.65, 14.55, 16.64, 19.01, 17.38, 
                    15.08, 14.94, 13.04, 13.47, 13.78, 13.97, 10.98, 11.22, 10.93, 
                    18.87, 8.47, 13.57, 11.64, 13.23, 11.26, 12.53, 13.69, 14.32, 
                    10.35, 15.87, 11.71, 13.16, 14.96, 10.66, 7.05, 14.2, 15.35, 
                    14.31, 17.81, 11.28, 16.73, 17.44, 13.84, 15.83, 14.42, 12.51, 
                    8.77, 15.9, 19.89, 14.89, 10.9, 14.71, 13.31, 16.32, 12.64, 8.81, 
                    13.28, 12.2, 16.08, 10.3, 17.56, 18.79, 18.33, 17.77, 15.83, 
                    14.56, 13.91, 14.4, 14.22, 14.47, 13.98, 13.09, 11.45, 13.88, 
                    13.41, 8.96, 14.11, 9.99, 10.42, 11.4, 11.5), age = c(36L, 36L, 
                                                                          56L, 34L, 34L, 40L, 55L, 40L, 33L, 39L, 33L, 32L, 53L, 38L, 31L, 
                                                                          37L, 31L, 31L, 37L, 37L, 30L, 36L, 35L, 29L, 29L, 35L, 49L, 28L, 
                                                                          28L, 28L, 27L, 33L, 27L, 27L, 26L, 26L, 32L, 32L, 26L, 26L, 26L, 
                                                                          26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 31L, 31L, 31L, 25L, 
                                                                          25L, 25L, 31L, 25L, 30L, 38L, 38L, 38L, 24L, 24L, 38L, 37L, 37L, 
                                                                          37L, 37L, 23L, 43L, 36L, 22L, 28L, 27L, 42L, 42L, 42L, 41L, 41L, 
                                                                          34L, 34L, 34L, 26L, 33L, 33L, 33L, 33L, 31L, 31L, 37L, 28L, 27L, 
                                                                          26L, 32L, 31L, 30L, 30L, 30L, 29L, 28L, 28L, 27L, 40L, 39L, 32L, 
                                                                          32L, 32L, 32L, 31L, 52L, 44L, 37L, 37L, 37L, 37L, 37L, 30L, 30L, 
                                                                          29L, 29L, 29L, 28L, 28L, 28L, 28L, 28L, 28L, 34L, 34L, 41L, 34L, 
                                                                          27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 40L, 33L, 26L, 26L, 32L, 
                                                                          32L, 32L, 32L, 39L, 25L, 31L, 31L, 31L, 31L, 31L, 38L, 31L, 24L, 
                                                                          38L, 38L, 38L, 38L, 30L, 30L, 30L, 36L, 29L, 29L, 29L, 36L, 43L, 
                                                                          35L, 28L, 35L, 28L, 28L, 28L, 35L, 35L, 21L, 34L, 27L, 27L, 34L, 
                                                                          33L, 33L, 26L, 25L, 32L, 32L, 39L, 31L, 24L, 31L, 31L, 38L, 30L, 
                                                                          30L, 30L, 30L, 30L, 30L, 36L, 28L, 28L, 28L, 28L, 27L, 34L, 34L, 
                                                                          34L, 26L, 26L, 25L, 32L, 32L, 32L, 32L, 32L, 38L, 38L, 31L, 23L, 
                                                                          30L, 30L, 30L, 30L, 36L, 36L, 29L, 36L, 29L, 29L, 29L, 29L, 42L, 
                                                                          35L, 28L, 28L, 34L, 34L, 27L, 33L, 26L, 26L, 40L, 39L, 39L, 39L, 
                                                                          46L, 38L, 45L, 31L, 38L, 24L, 37L, 44L, 30L, 37L, 43L, 29L, 36L, 
                                                                          36L, 36L, 35L, 42L, 42L, 42L, 42L, 35L, 34L, 34L, 34L, 34L, 34L, 
                                                                          33L, 26L, 40L, 26L, 33L, 33L, 33L, 32L, 32L, 32L, 39L, 31L, 38L, 
                                                                          31L, 31L, 37L, 30L, 23L, 37L, 37L, 37L, 37L, 30L, 36L, 36L, 36L, 
                                                                          36L, 35L, 27L, 27L, 34L, 40L, 33L, 25L, 32L, 32L, 39L, 30L, 28L, 
                                                                          28L, 27L, 30L, 28L, 40L, 39L, 39L, 37L, 37L, 37L, 31L, 36L, 35L, 
                                                                          35L, 35L, 35L, 34L, 34L, 32L, 26L, 32L, 30L, 28L, 26L, 26L)))


nls(sl~a*exp(-b*exp(-k*age)),data=a,start=list(a=60,b=0.2,k=50))

感谢您的帮助

Answer 1

尝试DE，甚至从您的缩写开始就很容易收敛到类似的解决方案：

library(NMOF)
 yf= function(params,x){
   a=params[1];b=params[2];k=params[3]
   a*exp(-b*exp(-k*x))
   }

 algo5 <- list(printBar = FALSE,
               nP  = 200L,
               nG  = 1000L,
               F   = 0.50,
               CR  = 0.99,
               min = rep(-5,3),
               max = rep(5,3))

 OF2 <- function(Param, data) { 
   x <- data$x
   y <- data$y
   ye <- data$model(Param,x)
   aux <- y - ye; aux <- sum(aux^2)
   if (is.na(aux)) aux <- 1e10
   aux
 }

 data5 <- list(x = a$age, y = a$sl,  model = yf, ww = 1)
 system.time(sol5 <- DEopt(OF = OF2, algo = algo5, data = data5))
 sol5$xbest
 OF2(sol5$xbest,data5)

plot(a$age, a$sl, pch=16, cex=0.3,ylim=c(0,30), ylab="SL (mm)", xlab="Age (days)", xlim=c(1,80)) 
gomp.new=curve(yf(sol5$xbest,x), 1, 85, add=T, lwd=2.5,col="blue",n=1000)

#>  sol5$xbest
#[1] 37.49220109  3.50998966  0.03758578

另一个：＆＃34;初始参数估计时的奇异梯度矩阵＆＃34;

1 个答案: