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PrevMap (version 1.5.4)

Geostatistical Modelling of Spatially Referenced Prevalence Data

Description

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) .

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Version

Install

install.packages('PrevMap')

Monthly Downloads

422

Version

1.5.4

License

GPL (>= 2)

Maintainer

Emanuele Giorgi

Last Published

October 7th, 2021

Functions in PrevMap (1.5.4)

binomial.logistic.MCML

Monte Carlo Maximum Likelihood estimation for the binomial logistic model

Laplace.sampling.SPDE

Independence sampler for conditional simulation of a Gaussian process using SPDE

Extract model coefficients

coef.PrevMap.ps

Extract model coefficients from geostatistical linear model with preferentially sampled locations

binomial.logistic.Bayes

Bayesian estimation for the binomial logistic model

Laplace.sampling

Langevin-Hastings MCMC for conditional simulation

Plot of the autocorrelgram for posterior samples

Adjustment factor for the variance of the convolution of Gaussian noise

Laplace.sampling.lr

Langevin-Hastings MCMC for conditional simulation (low-rank approximation)

binary.probit.Bayes

Bayesian estimation for the two-levels binary probit model

control.mcmc.Bayes

Control settings for the MCMC algorithm used for Bayesian inference

Priors specification

control.mcmc.MCML

Control settings for the MCMC algorithm used for classical inference on a binomial logistic model

control.mcmc.Bayes.SPDE

Control settings for the MCMC algorithm used for Bayesian inference using SPDE

linear.model.MLE

Maximum Likelihood estimation for the geostatistical linear Gaussian model

linear.model.Bayes

Bayesian estimation for the geostatistical linear Gaussian model

Monte Carlo Maximum Likelihood estimation of the geostatistical linear model with preferentially sampled locations

Loa loa prevalence data from 197 village surveys

Maximum Likelihood estimation for generalised linear geostatistical models via the Laplace approximation

galicia.boundary

Boundary of Galicia

contour.pred.PrevMap

Contour plot of a predicted surface

continuous.sample

Spatially continuous sampling

Profile likelihood for the shape parameter of the Matern covariance function

Define the model coefficients of a geostatistical linear model with preferentially sampled locations

create.ID.coords

ID spatial coordinates

Density plot for posterior samples

control.profile

Auxliary function for controlling profile log-likelihood in the linear Gaussian model

Simulated binomial data-set over the unit square

poisson.log.MCML

Monte Carlo Maximum Likelihood estimation for the Poisson model

spat.corr.diagnostic

Diagnostics for residual spatial correlation

discrete.sample

Spatially discrete sampling

variog.diagnostic.glgm

Variogram-based validation for generalized linear geostatistical model fits (Binomial and Poisson)

spatial.pred.binomial.Bayes

Bayesian spatial prediction for the binomial logistic and binary probit models

The empirical variogram

variog.diagnostic.lm

Variogram-based validation for linear geostatistical model fits

Heavy metal biomonitoring in Galicia

plot.shape.matern

Plot of the profile likelihood for the shape parameter of the Matern covariance function

spatial.pred.lm.ps

Spatial predictions for the geostatistical Linear Gaussian model using plug-in of ML estimates

spatial.pred.linear.MLE

Spatial predictions for the geostatistical Linear Gaussian model using plug-in of ML estimates

plot.profile.PrevMap

Plot of the profile log-likelihood for the covariance parameters of the Matern function

Profile likelihood confidence intervals

loglik.linear.model

Profile log-likelihood or fixed parameters likelihood evaluation for the covariance parameters in the geostatistical linear model

Trace-plots for posterior samples

trace.plot.MCML

Trace-plots of the importance sampling distribution samples from the MCML method

plot.pred.PrevMap

Plot of a predicted surface

summary.Bayes.PrevMap

Summarizing Bayesian model fits

spatial.pred.poisson.MCML

Spatial predictions for the Poisson model with log link function, using plug-in of MCML estimates

plot.PrevMap.diagnostic

Plot of the variogram-based diagnostics

spatial.pred.linear.Bayes

Bayesian spatial predictions for the geostatistical Linear Gaussian model

spatial.pred.binomial.MCML

Spatial predictions for the binomial logistic model using plug-in of MCML estimates

summary.PrevMap

Summarizing likelihood-based model fits

plot.pred.PrevMap.ps

Plot of a predicted surface of geostatistical linear fits with preferentially sampled locations

summary.PrevMap.ps

Summarizing fits of geostatistical linear models with preferentially sampled locations