极大似然比函数c语言实现,请教非线性函数的极大似然估计

Type ?nls in R for more details!

Examples from R help

DNase1

## using a selfStart model

fm1DNase1

summary(fm1DNase1)

## using conditional linearity

fm2DNase1

data = DNase1,

start = list(xmid = 0, scal = 1),

alg = "plinear", trace = TRUE)

summary(fm2DNase1)

## without conditional linearity

fm3DNase1

data = DNase1,

start = list(Asym = 3, xmid = 0, scal = 1),

trace = TRUE)

summary(fm3DNase1)

## using Port's nl2sol algorithm

fm4DNase1

data = DNase1,

start = list(Asym = 3, xmid = 0, scal = 1),

trace = TRUE, algorithm = "port")

summary(fm4DNase1)

## weighted nonlinear regression

Treated

weighted.MM

{

## Purpose: exactly as white book p. 451 -- RHS for nls()

##  Weighted version of Michaelis-Menten model

## ------------------------------------------------------------

## Arguments: 'y', 'x' and the two parameters (see book)

## ------------------------------------------------------------

## Author: Martin Maechler, Date: 23 Mar 2001, 18:48

pred

(resp - pred) / sqrt(pred)

}

Pur.wt

start = list(Vm = 200, K = 0.1),

trace = TRUE)

summary(Pur.wt)

## The two examples below show that you can fit a model to

## artificial data with noise but not to artificial data

## without noise.

x

y

yeps

nls(yeps ~ a + b*x, start = list(a = 0.12345, b = 0.54321),

trace = TRUE)

## Not run:

nls(y ~ a + b*x, start = list(a = 0.12345, b = 0.54321),

trace = TRUE)

## End(Not run)