spaMM (version 1.4.1)

HLCor: Fits a (spatially) correlated mixed model, for given correlation parameters

Description

A convenient interface for HLfit, constructing the correlation matrix of random effects from the arguments, then estimating fixed effects and dispersion parameters using HLfit.

Usage

HLCor(formula,ranPars,data,distMatrix,uniqueGeo,adjMatrix,
      corrMatrix,verbose=c(warn=TRUE,trace=FALSE,summary=FALSE),...)

Arguments

formula

A predictor, i.e. a formula with attributes (see Predictor), or possibly simply a simple formula if an offset is not required.

ranPars

A list of values for correlation parameters (some of which are mandatory), and possibly also dispersion parameters (optional, but passed to HLfit if present). By default, the Matern.corr model i

data

The data frame to be analyzed.

distMatrix

A distance matrix between geographic locations, forwarded to Matern.corr

uniqueGeo

A matrix of non-redundant geographic locations. This is useful if the rho parameter is a vector with different values for different geographic coordinates, in which case a scaled distance matrix has to be reconstructed from uniqueG

adjMatrix

An adjacency matrix, used if a random effect of the form y ~ adjacency(1|) is present. Its rows and columns must be ordered as increasing values of the levels of the geographic location index specifying the spatial

corrMatrix

A arbitrary (valid) correlation matrix, used if a random effect term of the form corrMatrix(1|) is present. Each row corresponds to levels of a variable . This allows to analyze non-spatial model by giving for example a mat

verbose

A vector of booleans. trace controls various diagnostic (possibly messy) messages about the iterations. summary controls whether a summary of the fit is called by HLfit. warn is for programming

...

Further parameters passed to HLfit or to designL.from.corr.

Value

• The return value of an HLfit call, with the following additional attributes:
• HLCorcall
  the HLCor call
• info.uniqueGeo
  Unique geographic locations.

Details

The correlation matrix for random effects can be specified by various combination of formula terms and other arguments (see Examples): [object Object],[object Object],[object Object],[object Object]

References

Wall M.M. (2004) A close look at the spatial structure implied by the CAR and SAR models: Journal of Statistical Planning and Inference 121: 311-324. Martellosio, F. (2012) The correlation structure of spatial autoregressions, Econometric Theory 28, 1373-1391.

See Also

Matern.corr, HLfit, corrHLfit

Examples

#### Matérn correlation using only the Matern() syntax
data(Loaloa)
HLCor(cbind(npos,ntot-npos)~elev1+elev2+elev3+elev4+maxNDVI1+seNDVI
              +Matern(1|longitude+latitude),data=Loaloa,
      family=binomial(),ranPars=list(nu=0.5,rho=1/0.7)) ## takes ~ 6s
#### Using a corrMatrix (not necessarily Matérn)
data(blackcap) 
## Here we manually reconstruct the correlation matrix 
##  of the ML fit produced by corrHLfit:
MLcorMat <- Matern.corr(proxy::dist(blackcap[,c("latitude","longitude")]),
                        nu=0.6285603,rho=0.0544659)
HLCor(migStatus ~ means+ corrMatrix(1|latitude+longitude),data=blackcap,
      corrMatrix=MLcorMat,HLmethod="ML")
      
#### Matérn correlation using a distMatrix
MLdistMat <- as.matrix(proxy::dist(blackcap[,c("latitude","longitude")]))
HLCor(migStatus ~ means+ Matern(1|latitude+longitude),data=blackcap,
      distMatrix=MLdistMat,HLmethod="ML",ranPars=list(nu=0.6285603,rho=0.0544659))

#### example with an adjacency matrix (autoregressive model)
data(scotlip)
HLCor(cases~I(prop.ag/10) +adjacency(1|gridcode)+offset(log(scotlip$expec)),
      ranPars=list(rho=0.174),
      adjMatrix=Nmatrix,family=poisson(),data=scotlip)