draw.polys: Additional supporting functions for random Markov fields

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/extraFunctions.R

Description

This set of functions are useful to get information and to plot maps.

Usage

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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)

Arguments

polys

an object containing the polygon information for the area

object

are either the values to plot in the draw.polys() function or a polygons information for a shape file for function polys2polys

scheme

scheme of colours to use, it can be "heat", "rainbow", "terrain", "topo", "cm" or any colour

swapcolors

to reverse the colours, it just work for "heat", "rainbow", "terrain", "topo", "cm" options

n.col

range for the colours

neighbour.nb

neighbour information for a shape file for function nb2nb

neighbour

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().

x

the factor defining the areas

area

all possible areas involved

...

for extra options

Details

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().

Value

The draw.polys() produces a plot while the rest of the functions produce required object for fitting or plotting.

Author(s)

Fernanda De Bastiani, Mikis Stasinopoulos, Robert Rigby and Vlasios Voudouris

Maintainer: Fernanda <fernandadebastiani@gmail.com>

References

Stasinopoulos, D. M., Rigby, R. A., Heller, G. Z., Voudouris, V. and De Bastiani, F. (2017) Flexible Regression and Smoothing: Using GAMLSS in R. Chapman and Hall, Boca Raton.

De Bastiani, F. Rigby, R. A., Stasinopoulos, D. M., Cysneiros, A. H. M. A. and Uribe-Opazo, M. A. (2016) Gaussian Markov random eld 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.

Rue and Held (2005) Gaussian Markov random fields: theory and applications, Chapman & Hall, USA.

Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).

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, http://www.jstatsoft.org/v23/i07.

See Also

MRF, MRFA

Examples

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# bringing the required libraries
library(spdep)
library(maptools)
# reading the shape file from package spdep
bh <- readShapePoly(system.file("etc/shapes/bhicv.shp",
             package="spdep")[1])
# pick up parts of the data and scale them
  BhData <- data.frame(scale(bh@data[,5:8]))  
# getting the neighbourhood and the polygons using the package 
# spdep functions 
   bh.nb <- poly2nb(bh) # neighbourhood
bh.polys <- bh@polygons # polygons
# now getting the information for the S4 object to required format
# from object S4 to object S3
newpolys <- polys2polys(bh.polys,bh.nb)
  newnb  <- nb2nb(bh.nb)
# drawing the map 
draw.polys(newpolys[[1]])
# plotting one of the variables in BhData
 poo <-BhData$HLCI
names(poo) <- row.names(BhData)
 draw.polys(newpolys[[1]], poo)
# now get the precision matrix
Prec <- nb2prec(newnb, x=as.factor(row.names(BhData)), 
          area=as.factor(row.names(BhData)))

gamlss.spatial documentation built on May 31, 2017, 5:15 a.m.

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