gw.pcplot: Geographically weighted parallel coordinate plot for...

In GWmodel: Geographically-Weighted Models

Geographically weighted parallel coordinate plot for investigating multivariate data sets

Description

This function provides a geographically weighted parallel coordinate plot for locally investigating a multivariate data set. It has an option that weights the lines of the plot with increasing levels of transparency, according to their observation's distance from a specified focal/observation point.

Usage

gw.pcplot(data,vars,focus,bw,adaptive = FALSE, ylim=NULL,ylab="",fixtrans=FALSE, 
          p=2, theta=0, longlat=F,dMat,...) 

Arguments

data	a Spatial*DataFrame, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp
vars	a vector of variable names to be evaluated
focus	an integer, indexing to the observation point
bw	bandwidth used in the weighting function;fixed (distance) or adaptive bandwidth(number of nearest neighbours)
adaptive	if TRUE calculate an adaptive kernel where the bandwidth (bw) corresponds to the number of nearest neighbours (i.e. adaptive distance); default is FALSE, where a fixed kernel is found (bandwidth is a fixed distance)
ylim	the y limits of the plot
ylab	a label for the y axis
fixtrans	if TRUE, the transparency of the neighbouring observation plot lines increases with distance; If FALSE a standard (non-spatial) parallel coordinate plot is returned.
p	the power of the Minkowski distance, default is 2, i.e. the Euclidean distance
theta	an angle in radians to rotate the coordinate system, default is 0
longlat	if TRUE, great circle distances will be calculated
dMat	a pre-specified distance matrix, it can be calculated by the function gw.dist
...	other graphical parameters, (see par)

Author(s)

Binbin Lu [e-mail]

References

Harris P, Brunsdon C, Charlton M, Juggins S, Clarke A (2014) Multivariate spatial outlier detection using robust geographically weighted methods. Mathematical Geosciences 46(1) 1-31

Harris P, Clarke A, Juggins S, Brunsdon C, Charlton M (2015) Enhancements to a geographically weighted principal components analysis in the context of an application to an environmental data set. Geographical Analysis 47: 146-172

GWmodel documentation built on July 9, 2023, 5:52 p.m.