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.

- 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

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`