我正在尝试使用minpack.lm中的nls.lm函数将非线性模型拟合到心理物理学实验中的一些数据。
我四处搜索,找不到有关该软件包的大量信息,因此基本上复制了 nls.lm 帮助页面上给出的示例格式。不幸的是,我的脚本仍然无法运行,R 抛出此错误:
Error in fn(par, ...) :
unused argument (observed = c(0.1429, 0.2857, 0.375, 0.3846, 0.4667, 0.6154))
看来脚本认为我想要拟合模型的数据是无关紧要的,这绝对是错误的。
我希望它适合模型并为备用参数 (w) 生成 0.5403 的值。
任何帮助将不胜感激。我正在从 Matlab 转移到 如果我的代码看起来很草率,我深表歉意。
这是脚本。
install.packages("pracma")
require(pracma)
install.packages("minpack.lm")
require(minpack.lm)
# Residual function, uses parameter w (e.g. .23) to predict accuracy error at a given ratio [e.g. 2:1]
residFun=function(w,n) .5 * erfc( abs(n[,1]-n[,2])/ ((sqrt(2)*w) * sqrt( (n[,1]^2) + (n[,2]^2) ) ) )
# example for residFun
# calculates an error rate of 2.59%
a=matrix(c(2,1),1,byrow=TRUE)
residFun(.23,a)
# Initial guess for parameter to be fitted (w)
parStart=list(w=0.2)
# Recorded accuracies in matrix, 1- gives errors to input into residFun
# i.e. the y-values I want to fit the model
Acc=1-(matrix(c(0.8571,0.7143,0.6250,0.6154,0.5333,0.3846),ncol=6))
# Ratios (converted to proportions) used in testing
# i.e. the points along the x-axis to fit the above data to
Ratios=matrix(c(0.3,0.7,0.4,0.6,0.42,0.58,0.45,0.55,0.47,0.53,0.49,0.51),nrow=6,byrow=TRUE)
# non-linear model fitting, attempting to calculate the value of w using the Levenberg-Marquardt nonlinear least-squares algorithm
output=nls.lm(par=parStart,fn=residFun,observed=Acc,n=Ratios)
# Error message shown after running
# Error in fn(par, ...) :
# unused argument (observed = c(0.1429, 0.2857, 0.375, 0.3846, 0.4667, 0.6154))
该错误意味着您向函数传递了一个它没有预料到的参数。 ?nls.lm
没有参数observed
,所以它被传递给传递给fn
的函数,在你的例子中,residFun
。但是,residFun
也不期望这个参数,因此出现了错误。你需要像这样重新定义这个函数:
# Residual function, uses parameter w (e.g. .23) to predict accuracy error at a given ratio [e.g. 2:1]
residFun=function(par,observed, n) {
w <- par$w
r <- observed - (.5 * erfc( abs(n[,1]-n[,2])/ ((sqrt(2)*w) * sqrt( (n[,1]^2) + (n[,2]^2) ) ) ))
return(r)
}
它给出以下结果:
> output = nls.lm(par=parStart,fn=residFun,observed=Acc,n=Ratios)
> output
Nonlinear regression via the Levenberg-Marquardt algorithm
parameter estimates: 0.540285874836135
residual sum-of-squares: 0.02166
reason terminated: Relative error in the sum of squares is at most `ftol'.
为什么会这样:
看来您受到了文档中这个示例的启发:
## residual function
residFun <- function(p, observed, xx) observed - getPred(p,xx)
## starting values for parameters
parStart <- list(a=3,b=-.001, c=1)
## perform fit
nls.out <- nls.lm(par=parStart, fn = residFun, observed = simDNoisy,
xx = x, control = nls.lm.control(nprint=1))
请注意,observed
在这里residFun
的论点。