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

View source: R/gmrf-for-gamlss.R

The function `gmrf()`

can be used to fit Markov Random Field additive terms within GAMLSS.

1 2 3 4 5 |

`x` |
a factor containing the areas |

`precision` |
the precision matrix if set |

`neighbour` |
an object containing the neighbour information for the area if set |

`polys` |
the polygon information if set |

`area` |
this argument is here to allow more areas than the levels of the factor |

`adj.weight` |
a value to adjust the iterative weight if necessary |

`df` |
degrees of freedom for fitting if required, only for |

`lambda` |
The smoothing parameter |

`start` |
starting value for the smoothing parameter |

`method` |
"Q" for Q-function, or "A" for alternating method |

`y` |
working response variable |

`w` |
iterative weights |

`xeval` |
whether to predict or not |

`control` |
to be use for some of the argument of |

`...` |
for extra arguments |

The function `gmrf()`

is to support the function `MRF()`

and `MRFA()`

within GAMLSS.
It is intended to be called within a GAMLSS formula. The function `gmrf()`

is not intended to be used directly. It is calling the function `MRFA()`

and `MRF()`

within the GAMLSS fitting algorithm.
The results using the option `method="Q"`

or `method="A"`

should produce identical results.

a fitted gamlss object

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

Maintainer: Fernanda <fernandadebastiani@gmail.com>

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. (see also http://www.gamlss.org/)

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.

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

1 2 3 4 5 6 7 8 9 10 11 | ```
library(gamlss)
library(mgcv)
data(columb)
data(columb.polys)
vizinhos=polys2nb(columb.polys)
precisionC <- nb2prec(vizinhos,x=columb$district)
# MRFA
m1<- gamlss(crime~ gmrf(district, polys=columb.polys, method="Q"), data=columb)
m2<- gamlss(crime~ gmrf(district, polys=columb.polys, method="A"), data=columb)
AIC(m1,m2, k=0)
draw.polys(columb.polys, getSmo(m2), scheme="topo")
``` |

```
Loading required package: gamlss.dist
Loading required package: MASS
Loading required package: gamlss
Loading required package: splines
Loading required package: gamlss.data
Loading required package: nlme
Loading required package: parallel
********** GAMLSS Version 5.0-2 **********
For more on GAMLSS look at http://www.gamlss.org/
Type gamlssNews() to see new features/changes/bug fixes.
Loading required package: gamlss.add
Loading required package: mgcv
This is mgcv 1.8-20. For overview type 'help("mgcv-package")'.
Loading required package: nnet
Attaching package: 'nnet'
The following object is masked from 'package:mgcv':
multinom
Loading required package: rpart
Loading required package: spam
Loading required package: dotCall64
Loading required package: grid
Spam version 2.1-1 (2017-07-02) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.
Attaching package: 'spam'
The following objects are masked from 'package:base':
backsolve, forwardsolve
GAMLSS-RS iteration 1: Global Deviance = 326.4786
GAMLSS-RS iteration 2: Global Deviance = 326.4786
Warning message:
In sqrt(diag(shes)) : NaNs produced
GAMLSS-RS iteration 1: Global Deviance = 326.4786
GAMLSS-RS iteration 2: Global Deviance = 326.4786
df AIC
m2 24.46858 326.4786
m1 24.46858 326.4786
```

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