GarwayHeath

<p>These Garway-Heath angles are used as the dissimilarity metric when implementing the boundary
detection model for a longitudinal series of visual fields.</p>

datasets

Implements a spatially varying change point model with
unique intercepts, slopes, variance intercepts and slopes, and
change points at each location. Inference is within the
Bayesian setting using Markov chain Monte Carlo (MCMC). The
response variable can be modeled as Gaussian (no nugget),
probit or Tobit link and the five spatially varying parameter
are modeled jointly using a multivariate conditional
autoregressive (MCAR) prior. The MCAR is a unique process that
allows for a dissimilarity metric to dictate the local spatial
dependencies. Full details of the package can be found in the accompanying vignette.
Furthermore, the details of the package can be found in the corresponding paper published in Spatial Statistics
by Berchuck et al (2019): "A spatially varying change points model for monitoring glaucoma
progression using visual field data", <doi:10.1016/j.spasta.2019.02.001>.

Samuel I. Berchuck

spCP

Spatially Varying Change Points

GarwayHeath function

<p>A vector with length 54, where each entry represents the angle (in degrees) that the
 underlying retinal nerve fiber enters the optic nerve head. The measure ranges from 0-360,
 where 0  is designated at the 9-o’clock position (right eye) and angles are counted counter
 clockwise. These angles are estimates for the Humphrey Field Analyzer-II (Carl Zeiss Meditec
 Inc., Dublin, CA). The 26th and 35th entries are missing as they correspond to a natural
 blind spot.</p>

GarwayHeath: Garway-Heath angles for the HFA-II

Description

Usage

Arguments

Format

References