JmatrixLMG {conicfit}

Compute the Jacobian matrix using geometric parameters

Description

JmatrixLMG Computes the Jacobian matrix (Implicit method) using geometric parameters

Usage

JmatrixLMG(XY,A,XYproj)

Arguments

XY

array of sample data

A

initial parameter vector c(Xc,Yc,a,b,alpha)

XYproj

corresponding n projection points on the conic

Value

list(Res, J)

list with the Residual Sum of Squares and the Jacobian matrix

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 <- matrix(c(1,7,2,6,5,8,7,7,9,5,3,7,6,2,8,4),8,2,byrow=TRUE)
A <- matrix(c(0,0,2,1,0),ncol=1)
XYproj=matrix(c(0.394467220216675,0.980356518335872,0.833315950425981,
0.909063326557293,1.40466123643977,0.711850899213363,
1.70601340510202,0.521899957274429,1.89925244997324,0.313384799914835,
1.06482258038841,0.846485805004280,1.95308457257492,
0.215325713960169,1.91319150256275,0.291418202297698),8,2,byrow=TRUE)
JmatrixLMG(XY,A,XYproj)