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PrevMap (version 1.4.1)

Geostatistical Modelling of Spatially Referenced Prevalence Data

Description

Provides functions for both likelihood-based and Bayesian analysis of spatially referenced prevalence data, and is also an extension of the 'geoR' package.

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Version

Install

install.packages('PrevMap')

Monthly Downloads

334

Version

1.4.1

License

GPL (>= 2)

Maintainer

Emanuele Giorgi

Last Published

June 6th, 2017

Functions in PrevMap (1.4.1)

Laplace.sampling.lr

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

Adjustment factor for the variance of the convolution of Gaussian noise

binomial.logistic.Bayes

Bayesian estimation for the binomial logistic model

binomial.logistic.MCML

Monte Carlo Maximum Likelihood estimation for the binomial logistic model

Extract model coefficients

coef.PrevMap.ps

Extract model coefficients from geostatistical linear model with preferentially sampled locations

Plot of the autocorrelgram for posterior samples

binary.probit.Bayes

Bayesian estimation for the two-levels binary probit model

Laplace.sampling

Langevin-Hastings MCMC for conditional simulation

Laplace.sampling.SPDE

Independence sampler for conditional simulation of a Gaussian process using SPDE

Simulated binomial data-set over the unit square

Density plot for posterior samples

control.mcmc.Bayes

Control settings for the MCMC algorithm used for Bayesian inference

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

continuous.sample

Spatially continuous sampling

contour.pred.PrevMap

Contour plot of a predicted surface

galicia.boundary

Boundary of Galicia

control.profile

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

create.ID.coords

ID spatial coordinates

discrete.sample

Spatially discrete sampling

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

plot.pred.PrevMap

Plot of a predicted surface

plot.pred.PrevMap.ps

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

Loa loa prevalence data from 197 village surveys

Profile likelihood confidence intervals

spatial.pred.poisson.MCML

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

summary.PrevMap

Summarizing likelihood-based model fits

summary.PrevMap.ps

Summarizing fits of geostatistical linear models with preferentially sampled locations

summary.Bayes.PrevMap

Summarizing Bayesian model fits

poisson.log.MCML

Monte Carlo Maximum Likelihood estimation for the Poisson model

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

linear.model.Bayes

Bayesian estimation for the geostatistical linear Gaussian model

plot.profile.PrevMap

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

plot.shape.matern

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

Trace-plots for posterior samples

control.mcmc.MCML

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

Priors specification

loglik.linear.model

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

Heavy metal biomonitoring in Galicia

spatial.pred.binomial.MCML

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

spatial.pred.linear.Bayes

Bayesian spatial predictions for the geostatistical Linear Gaussian model

spatial.pred.linear.MLE

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

trace.plot.MCML

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

Profile likelihood for the shape parameter of the Matern covariance function

spatial.pred.binomial.Bayes

Bayesian spatial prediction for the binomial logistic and binary probit models

spatial.pred.lm.ps

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