R编程nls（）：具有错误奇异梯度的confint（）

以下显示代码和输出：

libraryminpack.lm Levenberg-Marquardt非线性最小二乘算法，支持上下参数界

library(ggplot2) #Sophisticated combination of base and lattice graphics
DRP <-read.csv(file="NormalizedDRP.csv", header=TRUE)

    attach(DRP)

> # Lysis: 1% Lysed Algae + Seed # 

> LAS1 <-subset(DRP, RunM=="1% LAS ", select=DayM: NormalizedM)

> LAS1
  DayM NormalizedM
1    0         3.7
2    4         3.0
3   10         8.0
4   21         8.3
5   39         8.7

> fmLAS1 <-nlsLM(NormalizedM~A*(1-exp(-k*DayM)), data=LAS1, start=list(A=8, k=0))

> fmLAS1
Nonlinear regression model
  model: NormalizedM ~ A * (1 - exp(-k * DayM))
   data: LAS1
     A      k 
8.9060 0.1496 
 residual sum-of-squares: 15.98

Number of iterations to convergence: 8 
Achieved convergence tolerance: 1.49e-08

> coef(fmLAS1)
        A         k 
8.9060252 0.1495719 

> confint(fmLAS1)
Waiting for profiling to be done...
Error in prof$getProfile() : singular gradient

> deviance(fmLAS1)
[1] 15.97858

Data Set: I grabbed only a subset of the data for the variables Run M and DayM set to 1%LAS; which stands for 1% Lysed autoclaved Seed in anaerobic digestion

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如果confint尝试计算NormalizedM>A的值，则函数NormalizedM~A*1-exp-k*DayM将生成负数错误日志

你可以试着计算一个人的对数

log_NormalizedM <- log(NormalizedM)

然后用一个线性模型来拟合

fit <- lm(log_NormalizedM ~ DayM)

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您应该发布一份包含完整错误信息的成绩单，因为根本不清楚错误是否来自confint调用。即使这样也可能不够，因为错误表明数据集中存在病理学，仅仅看到一个子集可能无法给出明智的答案。