Question

以下表示代码和输出：

library（minpack.lm）＃Levenberg-Marquardt非线性最小二乘算法（支持下限和上限参数）

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

Answer 1

如果confint尝试计算NormalizedM＆gt;的值，则函数NormalizedM~A *（1-exp（（ - k）* DayM））将生成负数错误的日志。甲

您可以尝试计算NormalizedM的对数

log_NormalizedM <- log(NormalizedM)

并为此拟合线性模型。

fit <- lm(log_NormalizedM ~ DayM)

R Programming nls（）：confint（），带有错误的奇异梯度

1 个答案: