fitConic (version 1.2.1)

fitConic: Fit Data to A Conic Section Curve

Description

This function fits data to an ellipse, hyperbola, or parabola. It can do so without any initial conditions, or can accept initial parameter values when known.

Usage

fitConic(X, Y = NULL, parInit = NULL, conicType = c("e", "h", "p"),
weights = NULL, LambdaIni = 1, epsilonP = 1e-06, epsilonF = 1e-06, IterMAX = 20000)

Value

parA vector of the six "ABCDEF" coefficients RSS 'root sum square' figure of merit describing the relative fit quality iters number of iterations at convergence exitCode 1 means ellipse, 2 means hyperbola, 3 means parabola. If other values show up (possibly -1, 0, 4), most likely the dataset led to a degenerate case such as a line fit.

Arguments

X

vector of x-values, or a Nx2 array of x and y values. In the latter case, the input y is ignored.

Y

vector of y-values.

parInit

Optional. A vector either of six values representing an initial guess at the "ABCDEF" coefficients of the quadratic, or five values representing an initial guess at the "hvab,theta" coefficients. In the latter case, a value of either "e" or "h" is required for conicType. See the Details section for more information.

conicType

If parInit is either NULL or the "hvab,theta" option, conicType is required. Enter either "e", "h", or "p" for fitting to ellipse, hyperbola, or parabola.

weights

Optional vector of weights to apply to data. Must be same length as the input data. Only non-negative integer weights are allowed. See the Details section.

LambdaIni

A control parameter used in the fitting algorithm. Typically there is no reason to change from the default value.

epsilonP

A tolerance value to determine whether convergence has occurred.

epsilonF

A tolerance parameter for determining when to adjust lambda away from the input value LambdaIni.

IterMAX

A "safety" value to avoid loop thrashing when convergence isn't taking place.

Author

Carl Witthoft <[e-mail]>

Details

ParInit, when supplied is either a 6-value set representing the standard quadratic form Ax^2 + Bxy + Cy^2 +Dx + Ey +F = 0 or a 5-value set representing the "hvab,theta" form ((x-h)cosA +(y-v)sinA)^2/a^2 + ((x-h)sinA-(y-v)cosA)^2/b^2 = 1 . In the latter case the value conicType is required, because ellipses and hyperbolas have a different sign for the y-term. In most cases, the bootstrapper tools work well enough to allow the main algorithm to fit to an ellipse or hyperbola. However, "knowledge is power." If you have a good idea approximately what the ParIni values are, entering them will help avoid convergence to the wrong local minimum. The algorithm branch which fits data to parabolas does not use or need initialization, as it uses a RANSAC-type search to find the best rotation angle, and then does a simple quadratic polynomial fit. The weights input is restricted to nonnegative integers at this time. Doubles are rounded and negative values are set to zero. A zero weight will remove the matching data value from the dataset.

References

https://people.cas.uab.edu/~mosya/cl/ for information on the original "LMA" fitting algorithm. https://math.stackexchange.com/questions/426150 and https://math.stackexchange.com/questions/2800817 for various related equations concerning conic sections. https://en.wikipedia.org/wiki/Ellipse for several parameter conversion formulas

See Also

createConic , fitParabola

Examples

##-create a hyperbola, add noise
Ang =  0.42 #radians
xh <- seq(-20,20,by=0.1)
parAxyh <- c(0, 1, 0, -2, 4, -15 )
parAxyhr <- rotateA(parAxyh, Ang)$parA
newxyr <-createConic(xh,parAxyhr)
newxyrn <- createConic(xh,parAxyhr,ranFun=rnorm, noise= 0.05)
plot(newxyr, t = 'l',asp=TRUE)
points(newxyrn, pch = '.', cex = 3)
# Now find the hyperbola for that dataset
hypfitr <-fitConic(newxyrn, conicType = 'h')
hypdatr <- createConic(xh, hypfitr$parA)
lines(hypdatr, col='red')