使用nls函数初始猜测可视化
我正在尝试将一些由高斯钟组成的函数拟合到一些实验数据中。使用的方法是来自R的nls函数。但是很难使初始猜测足够好，这样该方法可以收敛。
是否可以在调用优化例程之前可视化初始猜测？
我正在处理的代码如下所示（我无法提供对数据文件的访问）。
library(signal)
# Load data from file
spectre <- read.table("LIA159.UXD")
# Extract variables and perform median filtering of the signal count
scatterangle <- spectre$V1
signal <- medfilt1(spectre$V2, n = 5)
#Perform a non linear fit of several gauss bells to the signal peaks
res <- nls( signal ~ bg + a*scatterangle
+ h1*exp(-((scatterangle - m1)/s1)^2)
+ h2*exp(-((scatterangle - m2)/s2)^2)
+ h3*exp(-((scatterangle - m3)/s3)^2)
+ h4*exp(-((scatterangle - m4)/s4)^2)
+ h5*exp(-((scatterangle - m5)/s5)^2)
+ h6*exp(-((scatterangle - m6)/s6)^2)
+ h7*exp(-((scatterangle - m7)/s7)^2)
,
start=list(
h1 = 2300, m1 = 23.42, s1 = 0.3,
h2 = 900, m2 = 11.64, s2 = 0.2,
h3 = 100, m3 = 34.80, s3 = 0.6,
h4 = 6, m4 = 39.43, s4 = 1.3,
h5 = 3, m5 = 46.83, s5 = 1.6,
h6 = 10, m6 = 60.23, s6 = 0.3,
h7 = 10, m7 = 61.46, s7 = 0.3,
bg=2, a = -0.1))
# Show the values of the fit
print(summary(res))
plot(signal ~ scatterangle, t='l', axes=F, xlab=expression(2*theta),
ylab="")
# Draw the fitted function on top of the original data.
lines(scatterangle, predict(res, data.frame(scatterangle)), col='red')
没有找到相关结果
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厘恼轨
set.seed(10) bg <- rnorm(10000,2,0.1) scatterangle <- runif(10000,5,35) signal <- bg + -0.4*scatterangle + 2000*exp(-((scatterangle - 24)/0.4)^2) + 1000*exp(-((scatterangle - 12)/0.14)^2)+ rnorm(10000,sd=100) sv <- list( h1 = 2300, m1 = 23.42, s1 = 0.3, h2 = 900, m2 = 11.64, s2 = 0.2, bg=2, a = -0.1) res <- nls( signal ~ bg + a*scatterangle + h1*exp(-((scatterangle - m1)/s1)^2) + h2*exp(-((scatterangle - m2)/s2)^2) , start=sv) signal2 <- with(sv,{ bg + a*scatterangle + h1*exp(-((scatterangle - m1)/s1)^2) + h2*exp(-((scatterangle - m2)/s2)^2) } ) id <- order(scatterangle) plot(signal[id]~scatterangle[id], t='l', axes=F, xlab=expression(2*theta), ylab="",col="grey") lines(scatterangle[id],signal2[id], col='blue',lwd=2) lines(scatterangle[id], predict(res, data.frame(scatterangle))[id], col='red',lwd=2)