为什么这个双指数模型的奇异梯度误差

我想比较以下数据的单指数和双指数拟合。单指数函数拟合得很好，但我找不到双指数函数的起始值（我也尝试过根据合理的值手动输入一些起始值）。

有人能解释为什么双指数函数不适合这些数据以及原因吗？是否有其他可能起作用的软件包，或者这是数据的限制？

谢谢

# Dummy data
dat <-  structure(list(x = c(5, 10, 30, 60, 120, 180, 240), y = c(0.000215875066736858, 
                                                          0.000213143286462547, 0.000184642176655826, 0.000154069098478106, 
                                                          0.000100308278926715, 6.56265655274e-05, 4.80349446283348e-05
)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
                                                            -7L))


# Mono-exponential model
nls_mod_mono <-  nls(y ~ statforbiology::NLS.expoDecay(x, C0, lambda1), dat)
# Predict and plot
xNew <- seq(0, 240, length.out = 100) # new grid of times
yNew <- predict(nls_mod_mono, list(x = xNew)) # predict activity
dfNew <-  data.frame(x = xNew, y = yNew)
ggplot(data = dfNew, aes(x = x, y = y*100)) +
  geom_line() +
  geom_point(data = dat, aes(x = x, y = y*100), size = 3)

# Bi-exponential model
params <-  getInitial(y ~ SSbiexp(x, intercept_initial, log_slope_initial, intercept_terminal, log_slope_terminal), data = dat)

#...failing to find initial values

nls_mod_bi <-  nls(y ~ C0*exp(-lambda1*x) + C1*exp(-lambda2*x), start = list(C0 = params[1], lambda1 = exp(params[2]), C1 = params[3], lambda2 = exp(params[4])), data = dat)