Description Usage Arguments Value Author(s) References Examples

Fit a spatial generalised linear mixed model to areal unit data, where the response variable can be binomial, Poisson, or zero-inflated Poisson (ZIP). Note, a Gaussian likelihood is not allowed because of a lack of identifiability among the parameters. The linear predictor is modelled by known covariates and 2 vectors of random effects. The latter are modelled by the BYM conditional autoregressive prior proposed by Besag et al. (1991), and further details are given in the vignette accompanying this package. Inference is conducted in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. Missing (NA) values are allowed in the response, and posterior predictive distributions are created for the missing values using data augmentation. These are saved in the "samples" argument in the output of the function and are denoted by "Y". For the ZIP model covariates can be used to estimate the probability of an observation being a structural zero, via a logistic regression equation. For a full model specification see the vignette accompanying this package.

1 2 3 4 |

`formula` |
A formula for the covariate part of the model using the syntax of the lm() function. Offsets can be included here using the offset() function. The response, offset and each covariate are vectors of length K*1. The response can contain missing (NA) values. |

`formula.omega` |
A one-sided formula object with no response variable (left side of the "~") needed, specifying the covariates in the logistic regression model for modelling the probability of an observation being a structural zero. Each covariate (or an offset) needs to be a vector of length K*1. Only required for zero-inflated Poisson models. |

`family` |
One of either "binomial","poisson" or "zip", which respectively specify a binomial likelihood model with a logistic link function, a Poisson likelihood model with a log link function, or a zero-inflated Poisson model with a log link function. |

`data` |
An optional data.frame containing the variables in the formula. |

`trials` |
A vector the same length as the response containing the total number of trials for each area. Only used if family="binomial". |

`W` |
A non-negative K by K neighbourhood matrix (where K is the number of spatial units). Typically a binary specification is used, where the jkth element equals one if areas (j, k) are spatially close (e.g. share a common border) and is zero otherwise. The matrix can be non-binary, but each row must contain at least one non-zero entry. |

`burnin` |
The number of MCMC samples to discard as the burn-in period. |

`n.sample` |
The number of MCMC samples to generate. |

`thin` |
The level of thinning to apply to the MCMC samples to reduce their temporal autocorrelation. Defaults to 1 (no thinning). |

`prior.mean.beta` |
A vector of prior means for the regression parameters beta (Gaussian priors are assumed). Defaults to a vector of zeros. |

`prior.var.beta` |
A vector of prior variances for the regression parameters beta (Gaussian priors are assumed). Defaults to a vector with values 100000. |

`prior.tau2` |
The prior shape and scale in the form of c(shape, scale) for an Inverse-Gamma(shape, scale) prior for tau2. Defaults to c(1, 0.01). |

`prior.sigma2` |
The prior shape and scale in the form of c(shape, scale) for an Inverse-Gamma(shape, scale) prior for sigma2. Defaults to c(1, 0.01). |

`prior.mean.delta` |
A vector of prior means for the regression parameters delta (Gaussian priors are assumed) for the zero probability logistic regression component of the model. Defaults to a vector of zeros. |

`prior.var.delta` |
A vector of prior variances for the regression parameters delta (Gaussian priors are assumed) for the zero probability logistic regression component of the model. Defaults to a vector with values 100000. |

`MALA` |
Logical, should the function use Metropolis adjusted Langevin algorithm (MALA) updates (TRUE) or simple random walk (FALSE, default) updates for the regression parameters and random effects. |

`verbose` |
Logical, should the function update the user on its progress. |

`summary.results ` |
A summary table of the parameters. |

`samples ` |
A list containing the MCMC samples from the model. |

`fitted.values ` |
A vector of fitted values for each area. |

`residuals ` |
A matrix with 2 columns where each column is a type of residual and each row relates to an area. The types are "response" (raw), and "pearson". |

`modelfit ` |
Model fit criteria including the Deviance Information Criterion (DIC) and its corresponding estimated effective number of parameters (p.d), the Log Marginal Predictive Likelihood (LMPL), the Watanabe-Akaike Information Criterion (WAIC) and its corresponding estimated number of effective parameters (p.w), and the loglikelihood. |

`accept ` |
The acceptance probabilities for the parameters. |

`localised.structure ` |
NULL, for compatability with other models. |

`formula ` |
The formula (as a text string) for the response, covariate and offset parts of the model |

`model ` |
A text string describing the model fit. |

`X ` |
The design matrix of covariates. |

Duncan Lee

Besag, J., J. York, and A. Mollie (1991). Bayesian image restoration with two applications in spatial statistics. Annals of the Institute of Statistics and Mathematics 43, 1-59.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ```
#################################################
#### Run the model on simulated data on a lattice
#################################################
#### Load other libraries required
library(MASS)
#### Set up a square lattice region
x.easting <- 1:10
x.northing <- 1:10
Grid <- expand.grid(x.easting, x.northing)
K <- nrow(Grid)
#### set up distance and neighbourhood (W, based on sharing a common border) matrices
distance <- as.matrix(dist(Grid))
W <-array(0, c(K,K))
W[distance==1] <-1
#### Generate the covariates and response data
x1 <- rnorm(K)
x2 <- rnorm(K)
theta <- rnorm(K, sd=0.05)
phi <- mvrnorm(n=1, mu=rep(0,K), Sigma=0.4 * exp(-0.1 * distance))
logit <- x1 + x2 + theta + phi
prob <- exp(logit) / (1 + exp(logit))
trials <- rep(50,K)
Y <- rbinom(n=K, size=trials, prob=prob)
#### Run the BYM model
formula <- Y ~ x1 + x2
## Not run: model <- S.CARbym(formula=formula, family="binomial", trials=trials,
W=W, burnin=20000, n.sample=100000)
## End(Not run)
#### Toy example for checking
model <- S.CARbym(formula=formula, family="binomial", trials=trials,
W=W, burnin=20, n.sample=50)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.