MPLE: Maximum pseudolikelihood estimation
In automultinomial: Models for Spatially Correlated Data

Description Usage Arguments Value Examples

Fits an autologistic model or automultinomial model. Takes as arguments a design matrix X, a response vector y (in factor form), and a square symmetric adjacency matrix encoding the neighborhood structure. When the number of levels of the response y is >2, the function fits a multicategory generalization of the autologistic model. For a full description of the models the package fits and a user guide, please see the vignette.

1 2	MPLE(X, y, A, ciLevel = 0.95, method = "asymptotic", burnIn = 300, nBoot = 500)

`X`	the n-by-p design matrix
`y`	the response vector (required to be a factor)
`A`	the square symmetric adjacency matrix A encoding the neighborhood structure
`ciLevel`	the confidence level to be used for inference. Defaults to 0.95 for 95 percent intervals.
`method`	"boot" for parametric bootstrap and "asymptotic" for asymptotic confidence intervals.
`burnIn`	the number of burnin samples to use for the Gibbs sampler when method="boot"
`nBoot`	the number of bootstrap samples to use when method="boot"

a fitted auto- model MPLE object

##########generating coefficient values and data
A=igraph::get.adjacency(igraph::make_lattice(c(40,40))) #adjacency matrix A
X=cbind(rep(1,1600),matrix(rnorm(1600*4),ncol=4)) #design matrix
gamma=0.6 #correlation parameter
beta=matrix(rnorm(5)*0.3,ncol=1) #covariate parameters
y=drawSamples(beta,gamma,X,A,burnIn=10,nSamples=1)

##########fitting model
fit=MPLE(X = X,y=factor(y),A = A,ciLevel = 0.99,method = "asymptotic")