以下指示代码和输出：
library(minpack.lm) #Levenberg-Marquardt 非线性最小二乘算法（支持参数上下界）
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