This module performs Geographically Weighted Discriminant Analysis [1], which includes the probabilities for each level, the highest probability and the entropy of the probabilities.
The arguments were taken from Gollini et al.[2]
Grouping factor
: Variable used for grouping.
Discriminators
:Variables used as discriminators.
Mean.gw
: if true, localised mean is used for GW
discriminant analysis; otherwise, global mean is used.
Cov.gw
:if TRUE, localised variance-covariance
matrix is used for GW discriminant analysis; otherwise, global
variance-covariance matrix is used
Prior.gw
: if TRUE, localised prior probability is used
for GW discriminant analysis; otherwise, fixed prior probability is
used.
longlat
: if TRUE, great circle distances will be
calculated.
wqda
: if TRUE, a weighted quadratic discriminant
analysis will be applied; otherwise a weighted linear discriminant
analysis will be applied.
Adaptive
: If TRUE, find an adaptive kernel with a
bandwidth proportional to the number of nearest neighbors (i.e. adaptive
distance); otherwise, find a fixed kernel (bandwidth is a fixed
distance).
Distance bandwidth
: bandwidth used in the weighting
function. It has two options, automatic
which is calculated
in the Bandwidth selection module and manual
in which the
user enter the value.
Power (Minkowski distance)
: the power of the Minkowski
distance (p=1 is manhattan distance, p=2 is euclidean distance).
Kernel
: A set of five commonly used kernel
functions;
Theta (Angle in radians)
: an angle in radians to rotate
the coordinate system, default is 0
An object of class “gwda”. This includes a SpatialPointsDataFrame or SpatialPolygonsDataFrame object, SDF, (see package “sp”) with the probabilities for each level, the highest probabiliity and the entropy of the probabilities in its “data” slot.
[1] Brunsdon, C, Fotheringham S, and Charlton, M (2007), Geographically Weighted Discriminant Analysis, Geographical Analysis 39:376-396. https://doi.org/10.1111/j.1538-4632.2007.00709.x
[2] Gollini, I., Lu, B., Charlton, M., Brunsdon, C., & Harris, P. (2015). GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models. Journal of Statistical Software, 63(17), 1–50. https://doi.org/10.18637/jss.v063.i17