Loaloa: Loa loa prevalence in North Cameroon, 1991-2001

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)

Arguments

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

Run this code

# NOT RUN {
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")>4.7) {
  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")>2.2) {
  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")>7.2) {
  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")>3.6) {
  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")>7) {
  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")>3.1) {
  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")>18.5) {
  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")>5.2) {
  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))  
}

# }

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