squintAngleEstimate: Estimating squint angle of 2-d structure in high-d dataset...

In spinebil: Investigating New Projection Pursuit Index Functions

Description

We estimate the squint angle by interpolating from a random starting plane towards the optimal view until the index value of the selected index function is above the selected cutoff. Since this depends on the direction, this is repeated with n randomly selected planes giving a distribution representative of the squint angle.

Usage

squintAngleEstimate(data, indexF, cutoff, structurePlane, n = 100,
  stepSize = 0.01)

Arguments

data	Input data
indexF	Index function
cutoff	Threshold index value above which we assume the structure to be visible
structurePlane	Plane defining the optimal view
n	Number of random starting planes (default = 100)
stepSize	Interpolation step size fixing the accuracy (default = 0.01)

Value

numeric vector containing all squint angle estimates

Examples

data <- spiralData(4, 100)
indexF <- scagIndex("Skinny")
cutoff <- 0.7
structurePlane <- basisMatrix(3,4,4)
squintAngleEstimate(data, indexF, cutoff, structurePlane, n=1)

spinebil documentation built on Aug. 28, 2019, 5:04 p.m.