Package: PrevMap 1.5.4

PrevMap: Geostatistical Modelling of Spatially Referenced Prevalence Data

Provides functions for both likelihood-based and Bayesian analysis of spatially referenced prevalence data. For a tutorial on the use of the R package, see Giorgi and Diggle (2017) <doi:10.18637/jss.v078.i08>.

Authors:Emanuele Giorgi, Peter J. Diggle

PrevMap_1.5.4.tar.gz
PrevMap_1.5.4.zip(r-4.5)PrevMap_1.5.4.zip(r-4.4)PrevMap_1.5.4.zip(r-4.3)
PrevMap_1.5.4.tgz(r-4.4-any)PrevMap_1.5.4.tgz(r-4.3-any)
PrevMap_1.5.4.tar.gz(r-4.5-noble)PrevMap_1.5.4.tar.gz(r-4.4-noble)
PrevMap_1.5.4.tgz(r-4.4-emscripten)PrevMap_1.5.4.tgz(r-4.3-emscripten)
PrevMap.pdf |PrevMap.html✨
PrevMap/json (API)

# Install 'PrevMap' in R:
install.packages('PrevMap', repos = c('https://giorgilancs.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/giorgilancs/prevmap/issues

Datasets:
  • data_sim - Simulated binomial data-set over the unit square
  • galicia - Heavy metal biomonitoring in Galicia
  • galicia.boundary - Boundary of Galicia
  • liberia - Heavy metal biomonitoring in Galicia
  • loaloa - Loa loa prevalence data from 197 village surveys

On CRAN:

4.32 score 42 scripts 460 downloads 15 mentions 42 exports 23 dependencies

Last updated 2 years agofrom:44bad53ac2. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 01 2024
R-4.5-winNOTENov 01 2024
R-4.5-linuxNOTENov 01 2024
R-4.4-winNOTENov 01 2024
R-4.4-macNOTENov 01 2024
R-4.3-winNOTENov 01 2024
R-4.3-macNOTENov 01 2024

Exports:adjust.sigma2autocor.plotbinary.probit.Bayesbinomial.logistic.Bayesbinomial.logistic.MCMLcontinuous.samplecontrol.mcmc.Bayescontrol.mcmc.Bayes.SPDEcontrol.mcmc.MCMLcontrol.priorcontrol.profilecreate.ID.coordsdens.plotdiscrete.sampleglgm.LALaplace.samplingLaplace.sampling.lrLaplace.sampling.SPDElinear.model.Bayeslinear.model.MLElm.ps.MCMLloglik.ciloglik.linear.modelmatern.kernelpoint.mappoisson.log.MCMLs_variogramset.par.psshape.maternspat.corr.diagnosticspatial.pred.binomial.Bayesspatial.pred.binomial.MCMLspatial.pred.linear.Bayesspatial.pred.linear.MLEspatial.pred.lm.psspatial.pred.poisson.MCMLtrace.plottrace.plot.MCMLtrend.plotvariog.diagnostic.glgmvariog.diagnostic.lmvariogram

Dependencies:bootdigestgenericslatticelme4MASSMatrixmaxLikminqamiscToolsnlmenloptrnumDerivpdistrasterRcppRcppEigensandwichspsplancsterratruncnormzoo

Readme and manuals

Help Manual

Help pageTopics
Adjustment factor for the variance of the convolution of Gaussian noiseadjust.sigma2
Plot of the autocorrelgram for posterior samplesautocor.plot
Bayesian estimation for the two-levels binary probit modelbinary.probit.Bayes
Bayesian estimation for the binomial logistic modelbinomial.logistic.Bayes
Monte Carlo Maximum Likelihood estimation for the binomial logistic modelbinomial.logistic.MCML
Extract model coefficientscoef.PrevMap
Extract model coefficients from geostatistical linear model with preferentially sampled locationscoef.PrevMap.ps
Spatially continuous samplingcontinuous.sample
Contour plot of a predicted surfacecontour.pred.PrevMap
Control settings for the MCMC algorithm used for Bayesian inferencecontrol.mcmc.Bayes
Control settings for the MCMC algorithm used for Bayesian inference using SPDEcontrol.mcmc.Bayes.SPDE
Control settings for the MCMC algorithm used for classical inference on a binomial logistic modelcontrol.mcmc.MCML
Priors specificationcontrol.prior
Auxliary function for controlling profile log-likelihood in the linear Gaussian modelcontrol.profile
ID spatial coordinatescreate.ID.coords
Simulated binomial data-set over the unit squaredata_sim
Density plot for posterior samplesdens.plot
Spatially discrete samplingdiscrete.sample
Heavy metal biomonitoring in Galiciagalicia
Boundary of Galiciagalicia.boundary
Maximum Likelihood estimation for generalised linear geostatistical models via the Laplace approximationglgm.LA
Langevin-Hastings MCMC for conditional simulationLaplace.sampling
Langevin-Hastings MCMC for conditional simulation (low-rank approximation)Laplace.sampling.lr
Independence sampler for conditional simulation of a Gaussian process using SPDELaplace.sampling.SPDE
Heavy metal biomonitoring in Galicialiberia
Bayesian estimation for the geostatistical linear Gaussian modellinear.model.Bayes
Maximum Likelihood estimation for the geostatistical linear Gaussian modellinear.model.MLE
Monte Carlo Maximum Likelihood estimation of the geostatistical linear model with preferentially sampled locationslm.ps.MCML
Loa loa prevalence data from 197 village surveysloaloa
Profile likelihood confidence intervalsloglik.ci
Profile log-likelihood or fixed parameters likelihood evaluation for the covariance parameters in the geostatistical linear modelloglik.linear.model
Matern kernelmatern.kernel
Plot of a predicted surfaceplot.pred.PrevMap
Plot of a predicted surface of geostatistical linear fits with preferentially sampled locationsplot.pred.PrevMap.ps
Plot of the variogram-based diagnosticsplot.PrevMap.diagnostic
Plot of the profile log-likelihood for the covariance parameters of the Matern functionplot.profile.PrevMap
Plot of the profile likelihood for the shape parameter of the Matern covariance functionplot.shape.matern
Point mappoint.map
Monte Carlo Maximum Likelihood estimation for the Poisson modelpoisson.log.MCML
Empirical variograms_variogram
Define the model coefficients of a geostatistical linear model with preferentially sampled locationsset.par.ps
Profile likelihood for the shape parameter of the Matern covariance functionshape.matern
Diagnostics for residual spatial correlationspat.corr.diagnostic
Bayesian spatial prediction for the binomial logistic and binary probit modelsspatial.pred.binomial.Bayes
Spatial predictions for the binomial logistic model using plug-in of MCML estimatesspatial.pred.binomial.MCML
Bayesian spatial predictions for the geostatistical Linear Gaussian modelspatial.pred.linear.Bayes
Spatial predictions for the geostatistical Linear Gaussian model using plug-in of ML estimatesspatial.pred.linear.MLE
Spatial predictions for the geostatistical Linear Gaussian model using plug-in of ML estimatesspatial.pred.lm.ps
Spatial predictions for the Poisson model with log link function, using plug-in of MCML estimatesspatial.pred.poisson.MCML
Summarizing Bayesian model fitssummary.Bayes.PrevMap
Summarizing likelihood-based model fitssummary.PrevMap
Summarizing fits of geostatistical linear models with preferentially sampled locationssummary.PrevMap.ps
Trace-plots for posterior samplestrace.plot
Trace-plots of the importance sampling distribution samples from the MCML methodtrace.plot.MCML
Plot of trendstrend.plot
Variogram-based validation for generalized linear geostatistical model fits (Binomial and Poisson)variog.diagnostic.glgm
Variogram-based validation for linear geostatistical model fitsvariog.diagnostic.lm
The empirical variogramvariogram