LMcircleFit: Geometric circle fit (minimizing orthogonal distances) based...

In conicfit: Algorithms for Fitting Circles, Ellipses and Conics Based on the Work by Prof. Nikolai Chernov

Description

LMcircleFit applies a Geometric circle fit (minimizing orthogonal distances) based on the standard Levenberg-Marquardt scheme

Usage

LMcircleFit(XY, ParIni, LambdaIni = 1, epsilon = 1e-06, IterMAX = 50)

Arguments

XY	array of sample data
ParIni	initial guess (a, b, R)
LambdaIni	initial value for the correction factor lambda
epsilon	tolerance (small threshold)
IterMAX	maximum number of (main) iterations, usually 10-20 will suffice

Value

vector(a, b, R)

vector with the estimates for the circle: center (a,b) and radius R

Author(s)

Jose Gama

Source

Nikolai Chernov, 2014 Fitting ellipses, circles, and lines by least squares http://people.cas.uab.edu/~mosya/cl/

Nikolai Chernov, 2010 Circular and linear regression: Fitting circles and lines by least squares Chapman & Hall/CRC, Monographs on Statistics and Applied Probability, Volume 117

References

Nikolai Chernov, 2014 Fitting ellipses, circles, and lines by least squares http://people.cas.uab.edu/~mosya/cl/

Nikolai Chernov, 2010 Circular and linear regression: Fitting circles and lines by least squares Chapman & Hall/CRC, Monographs on Statistics and Applied Probability, Volume 117

Examples

xy<-calculateCircle(0,0,200,50,randomDist=TRUE,noiseFun=function(x) (x+rnorm(1,mean=0,sd=50)))
plot(xy[,1],xy[,2],xlim=c(-250,250),ylim=c(-250,250));par(new=TRUE)
c4 <- LMcircleFit(xy)
xyc4<-calculateCircle(c4[1],c4[2],c4[3])
plot(xyc4[,1],xyc4[,2],xlim=c(-250,250),ylim=c(-250,250),col='cyan',type='l')

Example output

Loading required package: pracma
Loading required package: geigen

conicfit documentation built on May 2, 2019, 3:11 a.m.