spaMM (version 2.0.0)

Loaloa:

Description

This data set describes prevalence of infection by the nematode Loa loa in North Cameroon, 1991-2001. This is a superset of the data discussed by Diggle and Ribeiro (2007) and Diggle et al. (2007). The study investigated the relationship between altitude, vegetation indices, and prevalence of the parasite.

Usage

data(Loaloa)

Format

The data frame includes 197 observations on the following variables:

latitude

latitude, in degrees.

longitude

longitude, in degrees.

ntot

sample size per location

npos

number of infected individuals per location

maxNDVI

maximum normalised-difference vegetation index (NDVI) from repeated satellite scans

seNDVI

standard error of NDVI

elev1

altitude, in m.

elev2,elev3,elev4

Additional altitude variables derived from the previous one, provided for convenience: respectively, positive values of altitude-650, positive values of altitude-1000, and positive values of altitude-1300

maxNDVI1

a copy of maxNDVI modified as maxNDVI1[maxNDVI1>0.8] <- 0.8

References

Diggle, P., and Ribeiro, P. 2007. Model-based geostatistics, Springer series in statistics, Springer, New York.

Diggle, P. J., Thomson, M. C., Christensen, O. F., Rowlingson, B., Obsomer, V., Gardon, J., Wanji, S., Takougang, I., Enyong, P., Kamgno, J., Remme, J. H., Boussinesq, M., and Molyneux, D. H. 2007. Spatial modelling and the prediction of Loa loa risk: decision making under uncertainty, Ann. Trop. Med. Parasitol. 101, 499-509.

Examples

data(Loaloa)

### Variations on the model fit by Diggle et al. 
###    on a subset of the Loaloa data
### In each case this shows the slight differences in syntax,
###    and the difference in 'typical' computation times, 
###    when fit using corrHLfit() or fitme().

if (spaMM.getOption("example_maxtime")>16) {
  corrHLfit(cbind(npos,ntot-npos)~elev1+elev2+elev3+elev4+maxNDVI1+seNDVI
                   +Matern(1|longitude+latitude),HLmethod="HL(0,1)",
                 data=Loaloa,family=binomial(),ranFix=list(nu=0.5)) 
}
if (spaMM.getOption("example_maxtime")>4.5) {
  fitme(cbind(npos,ntot-npos)~elev1+elev2+elev3+elev4+maxNDVI1+seNDVI
                   +Matern(1|longitude+latitude),method="HL(0,1)",
                 data=Loaloa,family=binomial(),fixed=list(nu=0.5)) 
}

if (spaMM.getOption("example_maxtime")>22) {
  corrHLfit(cbind(npos,ntot-npos)~elev1+elev2+elev3+elev4+maxNDVI1+seNDVI
            +Matern(1|longitude+latitude),
              data=Loaloa,family=binomial(),ranFix=list(nu=0.5))  
}
if (spaMM.getOption("example_maxtime")>8) {
  fitme(cbind(npos,ntot-npos)~elev1+elev2+elev3+elev4+maxNDVI1+seNDVI
            +Matern(1|longitude+latitude),
              data=Loaloa,family=binomial(),fixed=list(nu=0.5),method="REML")
}

## Diggle and Ribeiro (2007) assumed (in this package notation) Nugget=2/7:
if (spaMM.getOption("example_maxtime")>21) {
  corrHLfit(cbind(npos,ntot-npos)~elev1+elev2+elev3+elev4+maxNDVI1+seNDVI
           +Matern(1|longitude+latitude),
             data=Loaloa,family=binomial(),ranFix=list(nu=0.5,Nugget=2/7))  
}
if (spaMM.getOption("example_maxtime")>5) {
  fitme(cbind(npos,ntot-npos)~elev1+elev2+elev3+elev4+maxNDVI1+seNDVI
           +Matern(1|longitude+latitude),method="REML",
             data=Loaloa,family=binomial(),fixed=list(nu=0.5,Nugget=2/7))  
}

## with nugget estimation:
if (spaMM.getOption("example_maxtime")>23) {
  corrHLfit(cbind(npos,ntot-npos)~elev1+elev2+elev3+elev4+maxNDVI1+seNDVI
           +Matern(1|longitude+latitude),
             data=Loaloa,family=binomial(),
             init.corrHLfit=list(Nugget=0.1),ranFix=list(nu=0.5))  
}
if (spaMM.getOption("example_maxtime")>14) {
  fitme(cbind(npos,ntot-npos)~elev1+elev2+elev3+elev4+maxNDVI1+seNDVI
           +Matern(1|longitude+latitude),
             data=Loaloa,family=binomial(),method="REML",
             init=list(Nugget=0.1),fixed=list(nu=0.5))  
}