This set of functions were useful in the past to get information and to plot maps but somehow now seem redundant.
draw.polys(polys, object = NULL, scheme = NULL,
swapcolors = FALSE, n.col = 100, ...)
polys2nb(polys)
nb2prec(neighbour,x,area=NULL)
polys2polys(object, neighbour.nb)
nb2nb(neighbour.nb)The draw.polys() produces a plot while the rest of the functions produce required object for fitting or plotting.
an object containing the polygon information for the area
are either the values to plot in the draw.polys() function or a polygons information for a shape file for function polys2polys
scheme of colours to use, it can be "heat", "rainbow", "terrain", "topo", "cm" or any colour
to reverse the colours, it just work for "heat", "rainbow", "terrain", "topo", "cm" options
range for the colours
neighbour information for a shape file for function nb2nb
the neighbour information, and if the neighbour is from S4 shape file than use nb2nb to transfer it to the appropriate neighbour for MRF(), MRFA(), mrf() and mrfa().
the factor defining the areas
all possible areas involved
for extra options
Fernanda De Bastiani, Mikis Stasinopoulos, Robert Rigby and Vlasios Voudouris
Maintainer: Fernanda <fernandadebastiani@gmail.com>
draw.polys() plots the fitted values of fitted MRF object.
polys2nb() gets the neighbour information from the polygons.
nb2prec() creates the precision matrix from the neighbour information.
polys2polys() transforms a shape file polygons (S4 object) to the polygons required form for the functions MRF() and MRFA().
nb2nb() transforms from a shape file neighbour (S4 object) to the neighbour required form for functions MRF().
De Bastiani, F. Rigby, R. A., Stasinopoulos, D. M., Cysneiros, A. H. M. A. and Uribe-Opazo, M. A. (2016) Gaussian Markov random spatial models in GAMLSS. Journal of Applied Statistics, pp 1-19.
Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), Appl. Statist., 54, part 3, pp 507-554.
Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC. An older version can be found in https://www.gamlss.com/.
Rue and Held (2005) Gaussian markov random fields: theory and applications, Chapman & Hall, USA.
Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, https://www.jstatsoft.org/v23/i07/.
Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.
(see also https://www.gamlss.com/).
MRF, MRFA