2

nls功能正常工作如下:

 x <- 1:10
 y <- 2*x + 3                            # perfect fit
 yeps <- y + rnorm(length(y), sd = 0.01) # added noise
 nls(yeps ~ a + b*x, start = list(a = 0.12345, b = 0.54321))#

因为我使用的模型有很多参数,或者我事先不知道参数列表中会包含什么,所以我想要如下

tmp <- function(x,p) { p["a"]+p["b"]*x }
p0 <- c(a = 0.12345, b = 0.54321)
nls(yeps ~ tmp(x,p), start = list(p=p0))

有谁知道如何修改nls函数以便它可以接受公式中的参数向量参数而不是许多单独的参数?

4

1 回答 1

4

您可以像这样给出初始系数的向量:

tmp  <- function(x, coef){
       a <- coef[1]
       b <- coef[2]
       a +b*x
     }

x <- 1:10
yeps <- y + rnorm(length(y), sd = 0.01)  # added noise
nls(yeps ~ a + b*x, start = list(a = 0.12345, b = 0.54321))#                     
nls(yeps ~ tmp(x,coef), start = list(coef = c(0.12345, 0.54321)))

Nonlinear regression model
  model:  yeps ~ tmp(x, coef) 
   data:  parent.frame() 
coef1 coef2 
    3     2 
 residual sum-of-squares: 0.0016

Number of iterations to convergence: 2 
Achieved convergence tolerance: 3.47e-08 

PS:

 example(nls)

应该是了解如何使用nls的良好开端。

于 2013-03-01T11:35:59.747 回答