Loaloa | R Documentation |
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.
data("Loaloa")
The data frame includes 197 observations on the following variables:
latitude, in degrees.
longitude, in degrees.
sample size per location
number of infected individuals per location
maximum normalised-difference vegetation index (NDVI) from repeated satellite scans
standard error of NDVI
altitude, in m.
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
a copy of maxNDVI modified as maxNDVI1[maxNDVI1>0.8] <- 0.8
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).
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.
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))
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.