Loaloa {spaMM} | R Documentation |
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")
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
Source
The data were last retrieved on March 1, 2013 from P.J. Ribeiro's web resources
at
www.leg.ufpr.br/doku.php/pessoais:paulojus:mbgbook:datasets
. A current (2022-06-18) source is
https://www.lancaster.ac.uk/staff/diggle/moredata/Loaloa.txt).
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")
if (spaMM.getOption("example_maxtime")>5) {
fitme(cbind(npos,ntot-npos)~1 +Matern(1|longitude+latitude),
data=Loaloa, family=binomial())
}
### 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) {
corrHLfit(cbind(npos,ntot-npos)~elev1+elev2+elev3+elev4+maxNDVI1+seNDVI
+Matern(1|longitude+latitude),method="HL(0,1)",
data=Loaloa,family=binomial(),ranFix=list(nu=0.5))
}
if (spaMM.getOption("example_maxtime")>1.6) {
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")>5.8) {
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")>2.5) {
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")>1.3) {
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")>17) {
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")>5.5) {
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))
}