Type:	Package
Title:	Geostatistical Modeling of Spatially Referenced Data
Version:	1.0.0
Description:	Geostatistical analysis of continuous and count data. Implements stationary Gaussian processes with Matérn correlation for spatial prediction, as described in Diggle and Giorgi (2019, ISBN: 978-1-138-06102-7).
License:	MIT + file LICENSE
URL:	https://claudiofronterre.github.io/RiskMap/
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
Depends:	R (≥ 3.5.0)
Imports:	stats, methods, Matrix, terra, xtable, graphics, spatialEco, spatialsample, Deriv, numDeriv, dplyr, ggplot2, ggpubr, gridExtra, sf, sns, stars
NeedsCompilation:	no
Packaged:	2025-10-09 16:09:31 UTC; giorgi
Author:	Emanuele Giorgi [aut, cre], Claudio Fronterre [ctb]
Maintainer:	Emanuele Giorgi <e.giorgi@bham.ac.uk>
Repository:	CRAN
Date/Publication:	2025-10-09 16:30:02 UTC

Laplace-sampling MCMC for Generalized Linear Gaussian Process Models

Description

Runs Markov chain Monte Carlo (MCMC) sampling using a Laplace approximation for Generalized Linear Gaussian Process Models (GLGPMs). The latent Gaussian field is integrated via a second-order Taylor expansion around the mode, and a Gaussian proposal is used for Metropolis–Hastings updates with adaptive step-size tuning.

Usage

Laplace_sampling_MCMC(
  y,
  units_m,
  mu,
  Sigma,
  ID_coords,
  ID_re = NULL,
  sigma2_re = NULL,
  family,
  control_mcmc,
  invlink = NULL,
  Sigma_pd = NULL,
  mean_pd = NULL,
  messages = TRUE
)

Arguments

Details

The algorithm alternates between:

1. Locating the mode of the joint integrand for the latent variables (via maxim.integrand) when Sigma_pd and mean_pd are not provided, yielding a Gaussian approximation.

2. Metropolis–Hastings updates using a Gaussian proposal centered at the current approximate mean with proposal variance governed by h. The step size is adapted based on empirical acceptance probability.

Dimensions must be consistent: length(y) = n, nrow(Sigma) = ncol(Sigma) = n_loc, and length(ID_coords) = n with entries in 1,\dots,n_\mathrm{loc}. If ID_re is provided, each column must have length n; when sigma2_re is supplied, it must be named and match colnames(ID_re).

Value

An object of class "mcmc.RiskMap" with components:

samples

A list containing posterior draws. Always includes $S (latent spatial field). If ID_re is supplied, each unstructured RE is returned under $<re_name>.

tuning_par

Numeric vector of step sizes (h) used over iterations.

acceptance_prob

Numeric vector of Metropolis–Hastings acceptance probabilities.

Default links

The default inverse links are: identity (gaussian), logistic (binomial), and exponential (poisson). Supply invlink to override.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterre@lancaster.ac.uk

See Also

maxim.integrand

Female Culex pipiens abundance (collections) in the Sacramento Metropolitan Area

Description

A dataset of mosquito collection events used to quantify abundance of female Culex pipiens in the Sacramento Metropolitan Area (Sacramento, Placer, El Dorado counties, California, USA). Each row represents a single collection event at a specific location and date.

Usage

data(abund_sma)

Format

A data frame with one row per collection event (for a total of 1552 collection events) and 6 variables:

lon

Numeric. Longitude in decimal degrees (WGS84).

lat

Numeric. Latitude in decimal degrees (WGS84).

total_females

Integer. Total number of female Cx. pipiens captured in the event.

date

Date. Exact collection date (local).

trap_nights

Integer. Number of trap-nights for the event.

trap_type

Character. Acronym for trap type used to collect mosquitoes. These include: "NJLT" (New Jersey Light Trap), "GRVD" (Gravid Trap), and "MMT" (Mosquito Magnet Trap).

Details

Derived from the vectorsurvR sample datasets by:

1. Filtering to female Culex pipiens collection records within the Sacramento Metropolitan Area (SMA) using point-in-polygon against the union of Sacramento, Placer, and El Dorado county boundaries.

2. Summing counts and trap-nights per unique location–date–trap type.

3. Coordinates are kept as WGS84 (EPSG:4326).

This sample dataset is intended for examples, mapping, and vector-index workflows. It is not official surveillance output. Dates span the same period as the vectorsurvR sample (e.g., 2015–2021).

Source

Constructed from sample_collections in the vectorsurvR R package.

Anopheles mosquitoes in Southern Cameroon

Description

These data contain 116 georeferenced locations on the counts of Anopheles gambiae and Anopheles coluzzii in Southern Cameroon.

• web_x x-coordinate of the spatial locations.

• web_y y-coordinate of the spatial locations.

• Locality: name of the place of the sampled location.

• An.coluzzii: counts of Anopheles coluzzi.

• An.gambiae: counts of Anopheles gambiae.

• Total: total counts of Anopheles coluzzi and Anopheles gambiae.

• elevation: elevation in meters of the sampled location.

The coordinate reference system is 3857.

Usage

data(anopheles)

Format

A data frame with 116 rows and 7 variables

Source

Tene Fossog, B., Ayala, D., Acevedo, P., Kengne, P., Ngomo Abeso Mebuy, I., Makanga, B., et al. (2015) Habitat segregation and ecological character displacement in cryptic African malaria mosquitoes. Evolutionary Applications, 8 (4), 326-345.

Assess Predictive Performance via Spatial Cross-Validation

Description

This function evaluates the predictive performance of spatial models fitted to 'RiskMap' objects using cross-validation. It supports two classes of diagnostic tools:

- **Scoring rules**, including the Continuous Ranked Probability Score (CRPS) and its scaled version (SCRPS), which quantify the sharpness and calibration of probabilistic forecasts; - **Calibration diagnostics**, based on the Probability Integral Transform (PIT) for Gaussian outcomes and Aggregated nonparametric PIT (AnPIT) curves for discrete outcomes (e.g., Poisson or Binomial).

Cross-validation can be performed using either spatial clustering or regularized subsampling with a minimum inter-point distance. For each fold or subset, models can be refitted or evaluated with fixed parameters, offering flexibility in model validation. The function also provides visualizations of the spatial distribution of test folds.

Usage

assess_pp(
  object,
  keep_par_fixed = TRUE,
  iter = 1,
  fold = NULL,
  n_size = NULL,
  control_sim = set_control_sim(),
  method,
  min_dist = NULL,
  plot_fold = TRUE,
  messages = TRUE,
  which_metric = c("AnPIT", "CRPS", "SCRPS"),
  user_split = NULL,
  ...
)

Arguments

Value

A list of class 'RiskMap.spatial.cv', containing:

test_set

A list of test sets used for validation, each of class ''sf''.

model

A named list, one per model, each containing:

score

A list with CRPS and/or SCRPS scores for each fold if requested.

PIT

(if 'family = "gaussian"' and 'which_metric' includes '"AnPIT"') A list of PIT values for test data.

AnPIT

(if 'family' is discrete and 'which_metric' includes '"AnPIT"') A list of AnPIT curves for test data.

Author(s)

Emanuele Giorgi

References

Bolin, D., & Wallin, J. (2023). Local scale invariance and robustness of proper scoring rules. *Statistical Science*, 38(1), 140–159. doi:10.1214/22-STS864.

See Also

plot_AnPIT

Assess Simulations

Description

This function evaluates the performance of models based on simulation results from the 'surf_sim' function.

Usage

assess_sim(
  obj_sim,
  models,
  control_mcmc = set_control_sim(),
  spatial_scale,
  messages = TRUE,
  f_grid_target = NULL,
  f_area_target = NULL,
  shp = NULL,
  col_names = NULL,
  pred_objective = c("mse", "classify"),
  categories = NULL
)

Arguments

Value

A list of class 'RiskMap.sim.res' containing model evaluation results.

Check MCMC Convergence for Spatial Random Effects

Description

This function checks the Markov Chain Monte Carlo (MCMC) convergence of spatial random effects for either a RiskMap or RiskMap.pred.re object. It plots the trace plot and autocorrelation function (ACF) for the MCMC chain and calculates the effective sample size (ESS).

Usage

check_mcmc(object, check_mean = TRUE, component = NULL, ...)

Arguments

Details

The function first checks that the input object is either of class RiskMap or RiskMap.pred.re. Depending on the value of check_mean, it either calculates the mean of the spatial random effects across all locations for each iteration or uses the specified component. It then generates two plots: - A trace plot of the selected spatial random effect over iterations. - An autocorrelation plot (ACF) with the effective sample size (ESS) displayed in the title.

The ESS is computed using the ess function, which provides a measure of the effective number of independent samples in the MCMC chain.

If check_mean = TRUE, the component argument is ignored, and a warning is issued. To specify a particular component of the random effects vector, set check_mean = FALSE and provide a valid component value.

Value

No return value, called for side effects (plots and warnings).

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Extract Parameter Estimates from a "RiskMap" Model Fit

Description

This coef method for the "RiskMap" class extracts the maximum likelihood estimates from model fits obtained from the glgpm function.

Usage

## S3 method for class 'RiskMap'
coef(object, ...)

Arguments

Details

The function processes the RiskMap object to extract and name the estimated parameters appropriately, transforming them if necessary.

Value

A list containing the maximum likelihood estimates:

Note

This function handles both Gaussian and non-Gaussian families, and accounts for fixed and random effects in the model.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

See Also

glgpm

Compute Unique Coordinate Identifiers

Description

This function identifies unique coordinates from a 'sf' (simple feature) object and assigns an identifier to each coordinate occurrence. It returns a list containing the identifiers for each row and a vector of unique identifiers.

Usage

compute_ID_coords(data_sf)

Arguments

Details

The function extracts the coordinate pairs from the 'sf' object and determines the unique coordinates. It then assigns each row in the input data an identifier corresponding to the unique coordinate it matches.

Value

A list with the following elements:

ID_coords

An integer vector where each element corresponds to a row in the input, indicating the index of the unique coordinate in the full set of unique coordinates.

s_unique

An integer vector containing the unique identifiers of all distinct coordinates.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Convex Hull of an sf Object

Description

Computes the convex hull of an 'sf' object, returning the boundaries of the smallest polygon that can enclose all geometries in the input.

Usage

convex_hull_sf(sf_object)

Arguments

Details

The convex hull is the smallest convex polygon that encloses all points in the input 'sf' object. This function computes the convex hull by first uniting all geometries in the input using 'st_union()', and then applying 'st_convex_hull()' to obtain the polygonal boundary. The result is returned as an 'sf' object containing the convex hull geometry.

Value

An 'sf' object representing the convex hull of the input geometries.

See Also

st_convex_hull, st_union

Examples

library(sf)

# Create example sf object
points <- st_sfc(st_point(c(0,0)), st_point(c(1,1)), st_point(c(2,2)), st_point(c(0,2)))
sf_points <- st_sf(geometry = points)

# Calculate the convex hull
convex_hull_result <- convex_hull_sf(sf_points)

# Plot the result
plot(sf_points, col = 'blue', pch = 19)
plot(convex_hull_result, add = TRUE, border = 'red')

Create Grid of Points Within Shapefile

Description

Generates a grid of points within a given shapefile. The grid points are created based on a specified spatial resolution.

Usage

create_grid(shp, spat_res, grid_crs = NULL)

Arguments

Details

This function creates a grid of points within the boundaries of the provided shapefile ('shp'). The grid points are generated using the specified spatial resolution ('spat_res'). If a coordinate reference system ('grid_crs') is provided, the shapefile is transformed to this CRS before creating the grid.

Value

An 'sf' object containing the generated grid points within the shapefile.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

See Also

st_make_grid, st_intersects, st_transform, st_crs

Examples

library(sf)

# Example shapefile data
nc <- st_read(system.file("shape/nc.shp", package="sf"))

# Create grid with 10 km spatial resolution
grid <- create_grid(nc, spat_res = 10)

# Plot the grid
plot(st_geometry(nc))
plot(grid, add = TRUE, col = 'red')

Fitting of decay-adjusted spatio-temporal (DAST) model

Description

The function fits a decay-adjusted spatio-temporal (DAST) model using Monte Carlo maximum likelihood. The DAST model allows for the incorporation of temporal decay in disease prevalence due to the impact of mass drug administration (MDA). The function requires the full MDA history as detailed in the arguments below.

Spatial and spatio-temporal dependence is specified through the gp() term in the model formula:

• gp(x, y) fits a purely spatial Gaussian process.

• gp(x, y, t_gp) fits a spatio-temporal Gaussian process, where t_gp is used as the GP temporal index.

In all cases, the time argument must be specified separately and provides the observation-level survey times used in modelling MDA impact. These survey times may differ from the GP temporal index.

Usage

dast(
  formula,
  data,
  den = NULL,
  time,
  mda_times,
  int_mat,
  penalty = NULL,
  drop = NULL,
  power_val,
  crs = NULL,
  convert_to_crs = NULL,
  scale_to_km = TRUE,
  control_mcmc = set_control_sim(),
  par0 = NULL,
  S_samples = NULL,
  return_samples = TRUE,
  messages = TRUE,
  start_pars = list(beta = NULL, sigma2 = NULL, tau2 = NULL, phi = NULL, psi = NULL,
    sigma2_re = NULL, gamma = NULL, alpha = NULL)
)

Arguments

Value

A list containing model estimates, posterior samples, and metadata, including:

• y: Response variable values.

• D: Covariate matrix.

• coords: Unique spatial coordinates.

• mda_times: MDA time points.

• survey_times_data: Survey time data from the time argument.

• time: GP temporal index if specified in gp(x,y,t_gp).

• int_mat: Intervention matrix.

• ID_coords: Indices of spatial locations (and time if spatio-temporal GP).

• re: Random effects levels (if applicable).

• ID_re: Indices of random effects (if applicable).

• power_val: Power of the MDA impact function.

• fix_tau2: Fixed tau-squared value (if applicable).

• fix_alpha: Fixed alpha value (if applicable).

• formula: Model formula.

• crs: Coordinate reference system.

• scale_to_km: Indicator of distance scaling.

• data_sf: Processed spatial dataset.

• family: Model family (e.g., "binomial").

• sst: Logical indicator of whether a spatio-temporal GP was used.

• kappa: Smoothness parameter.

• units_m: Denominator for binomial models.

• cov_offset: Offset for covariates.

• call: Function call.

• penalty: Penalty function details (if applicable).

• posterior_samples: Posterior samples if return_samples = TRUE.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

See Also

set_control_sim, summary.RiskMap, to_table

Summaries of the distances

Description

Computes the distances between the locations in the data-set and returns summary statistics of these.

Usage

dist_summaries(data, convert_to_utm = TRUE, scale_to_km = FALSE)

Arguments

Value

a list containing the following components

min the minimum distance

max the maximum distance

mean the mean distance

median the minimum distance

Heavy metal biomonitoring in Galicia

Description

This data-set relates to two studies on lead concentration in moss samples, in micrograms per gram dry weight, collected in Galicia, norther Spain. The data are from two surveys, one conducted in July 2000. The variables are as follows:

• x x-coordinate of the spatial locations.

• y y-coordinate of the spatial locations.

• lead number of tested people for the presence nodules.

The coordinate reference system of the data is 32629.

Usage

data(galicia)

Format

A data frame with 195 rows and 4 variables

Source

Diggle, P.J., Menezes, R. and Su, T.-L. (2010). Geostatistical analysis under preferential sampling (with Discussion). Applied Statistics, 59, 191-232.

Estimation of Generalized Linear Gaussian Process Models

Description

Fits generalized linear Gaussian process models to spatial data, incorporating spatial Gaussian processes with a Matern correlation function. Supports Gaussian, binomial, and Poisson response families.

Usage

glgpm(
  formula,
  data,
  family,
  invlink = NULL,
  den = NULL,
  crs = NULL,
  convert_to_crs = NULL,
  scale_to_km = TRUE,
  control_mcmc = set_control_sim(),
  par0 = NULL,
  S_samples = NULL,
  return_samples = TRUE,
  messages = TRUE,
  fix_var_me = NULL,
  start_pars = list(beta = NULL, sigma2 = NULL, tau2 = NULL, phi = NULL, sigma2_me =
    NULL, sigma2_re = NULL)
)

Arguments

Details

Generalized linear Gaussian process models extend generalized linear models (GLMs) by incorporating spatial Gaussian processes to account for spatial correlation in the data. This function fits GLGPMs using maximum likelihood methods, allowing for Gaussian, binomial, and Poisson response families. In the case of the Binomial and Poisson families, a Monte Carlo maximum likelihood algorithm is used.

The spatial Gaussian process is modeled with a Matern correlation function, which is flexible and commonly used in geostatistical modeling. The function supports both spatial covariates and unstructured random effects, providing a comprehensive framework to analyze spatially correlated data across different response distributions.

Additionally, the function allows for the inclusion of unstructured random effects, specified through the re() term in the model formula. These random effects can capture unexplained variability at specific locations beyond the fixed and spatial covariate effects, enhancing the model's flexibility in capturing complex spatial patterns.

The convert_to_crs argument can be used to reproject the spatial coordinates to a different CRS. The scale_to_km argument scales the coordinates to kilometers if set to TRUE.

The control_mcmc argument specifies the control parameters for MCMC sampling. This argument must be an object returned by set_control_sim.

The start_pars argument allows for specifying starting values for the model parameters. If not provided, default starting values are used.

Value

An object of class "RiskMap" containing the fitted model and relevant information:

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

See Also

set_control_sim, summary.RiskMap, to_table

Simulation from Generalized Linear Gaussian Process Models

Description

Simulates data from a fitted Generalized Linear Gaussian Process Model (GLGPM) or a specified model formula and data.

Usage

glgpm_sim(
  n_sim,
  model_fit = NULL,
  formula = NULL,
  data = NULL,
  family = NULL,
  den = NULL,
  cov_offset = NULL,
  crs = NULL,
  convert_to_crs = NULL,
  scale_to_km = TRUE,
  sim_pars = list(beta = NULL, sigma2 = NULL, tau2 = NULL, phi = NULL, sigma2_me = NULL,
    sigma2_re = NULL),
  messages = TRUE
)

Arguments

Details

Generalized Linear Gaussian Process Models (GLGPMs) extend generalized linear models (GLMs) by incorporating spatial Gaussian processes to model spatial correlation. This function simulates data from GLGPMs using Markov Chain Monte Carlo (MCMC) methods. It supports Gaussian, binomial, and Poisson response families, utilizing a Matern correlation function to model spatial dependence.

The simulation process involves generating spatially correlated random effects and simulating responses based on the fitted or specified model parameters. For 'gaussian' family, the function simulates response values by adding measurement error.

Additionally, GLGPMs can incorporate unstructured random effects specified through the re() term in the model formula, allowing for capturing additional variability beyond fixed and spatial covariate effects.

Value

A list containing simulated data, simulated spatial random effects (if applicable), and other simulation parameters.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

Gaussian Process Model Specification

Description

Specifies the terms, smoothness, and nugget effect for a Gaussian Process (GP) model.

Usage

gp(..., kappa = 0.5, nugget = 0)

Arguments

Details

The function constructs a list that includes the specified terms (spatial coordinates or covariates), the smoothness parameter \kappa, and the nugget effect. This list can be used as a specification for a Gaussian Process model.

Value

A list of class gp.spec containing the following elements:

Note

The nugget effect must be a positive real number if specified.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

West Nile virus pool tests for female *Culex pipiens* in the Sacramento Metropolitan Area

Description

A dataset of PCR-tested mosquito pools used to summarize infection for female *Culex pipiens* in the Sacramento Metropolitan Area (Sacramento, Placer, El Dorado counties, California, USA). Each row represents a tested pool at a specific location and date, with an estimated pool size.

Usage

data(infect_sma)

Format

A data frame with one row per tested pool (for a total of 596 pools) and 5 variables:

lon

Numeric. Longitude in decimal degrees (WGS84).

lat

Numeric. Latitude in decimal degrees (WGS84).

est_pool_n

Integer. Estimated number of mosquitoes in the pool (see Details).

wnv_pos

Logical. Whether the pool tested positive for West Nile virus (TRUE/FALSE).

date

Date. Pool collection date (local).

Details

Derived from the vectorsurvR sample datasets by:

1. Filtering to female Culex pipiens pools with WNV testing within the SMA using point-in-polygon against the union of Sacramento, Placer, and El Dorado counties.

2. Estimating pool size est_pool_n for each pool by:

   • Summing total female counts from nearby collection points within the same week and within a spatial radius (e.g., 2 km) to obtain T_{\mathrm{near}}.

   • Counting nearby pools within the same week/radius to obtain m_{\mathrm{near}}.

   • Setting est_pool_n = round(max(1, T_near / m_near)); if no nearby collections are found, a conservative fallback (default 25) is used.

3. Coordinates are kept as WGS84 (EPSG:4326).

Important: est_pool_n is an estimate for demonstration and pooled-modelling examples; it is not the recorded laboratory pool size.

Source

Constructed from sample_collections in the vectorsurvR R package.

Simulated data-set on the Italian peninsula

Description

This is a simulated data-set over Italy for a continuous outcome. The data-set contains 10 repeated observations for each of the 200 geo-referenced locations. The variables are as follows:

• x1 ordinate of the spatial locations.

• x2 abscissa of the spatial locations.

• y simulated continuous outcome.

• region the name of the region within which a given observation falls.

• province the name of the province within which a given observation falls.

• pop_dens the population density at the location of the observation.

• ID_loc an ID identifying the location to which the observation belong.

The coordinate reference system of the data is 32634.

Usage

data(italy_sim)

Format

A data frame with 2000 rows and 7 variables

River-blindness in Liberia

Description

This data-set contains counts of reported onchocerciasis (or riverblindess) cases from 91 villages sampled across Liberia. The variables are as follows:

• lat latitude of the of sampled villages.

• long longitude of the sampled villages.

• ntest number of tested people for the presence nodules.

• npos number of people that tested positive for the presence of nodules.

• elevation the elevation in meters of the sampled village.

• log_elevation the log-transformed elevation in meters of the sampled village.

Usage

data(liberia)

Format

A data frame with 90 rows and 6 variables

Source

Zouré, H. G. M., Noma, M., Tekle, Afework, H., Amazigo, U. V., Diggle, P. J., Giorgi, E., and Remme, J. H. F. (2014). The Geographic Distribution of Onchocerciasis in the 20 Participating Countries of the African Programme for Onchocerciasis Control: (2) Pre-Control Endemicity Levels and Estimated Number Infected. Parasites & Vectors, 7, 326.

Loa loa prevalence data from 197 village surveys

Description

This data-set relates to a study of the prevalence of Loa loa (eyeworm) in a series of surveys undertaken in 197 villages in west Africa (Cameroon and southern Nigeria). The variables are as follows:

• ROW row id: 1 to 197.

• VILLCODE village id.

• LONGITUDE Longitude in degrees.

• LATITUDE Latitude in degrees.

• NO_EXAM Number of people tested.

• NO_INF Number of positive test results.

• ELEVATION Height above sea-level in metres.

• MEAN9901 Mean of all NDVI values recorded at village location, 1999-2001

• MAX9901 Maximum of all NDVI values recorded at village location, 1999-2001

• MIN9901 Minimum of all NDVI values recorded at village location, 1999-2001

• MIN9901 Minimum of all NDVI values recorded at village location, 1999-2001

• STDEV9901 standard deviation of all NDVI values recorded at village location, 1999-2001

Usage

data(loaloa)

Format

A data frame with 197 rows and 11 variables

References

Diggle, P.J., Thomson, M.C., Christensen, O.F., Rowlingson, B., Obsomer, V., Gardon, J., Wanji, S., Takougang, I., Enyong, P., Kamgno, J., Remme, H., Boussinesq, M. and Molyneux, D.H. (2007). Spatial modelling and prediction of Loa loa risk: decision making under uncertainty. Annals of Tropical Medicine and Parasitology, 101, 499-509.

Malaria Transmission in the Western Kenyan Highlands

Description

The dataset contains information on 82014 individuals enrolled in concurrent school and community cross-sectional surveys, conducted in 46 school clusters in the western Kenyan highlands. Malaria was assessed by rapid diagnostic test (RDT).

The variables are as follows:

• Cluster: unique ID for each of the 46 school clusters.

• Long: longitude coordinate of the household location.

• Lat: latitude coordinate of the household location.

• RDT: binary variable indicating the outcome of the RDT: 1, if positive, and 0, if negative.

• Gender: factor variable indicating the gender of the sampled individual.

• Age: age in years of the sampled individual.

• NetUse: binary variable indicating whether the sampled individual slept under a bed net the previous night: 1, if yes, 0, if no.

• MosqCntl: binary variable indicating whether the household has used some kind of mosquito control, such as sprays and coils: 1, if yes, 0, if no.

• IRS: binary variables in indicating whether there has been indoor residual spraying (IRS) in the house in the last 12 months: 1, if yes, 0, if no.

• Travel: binary variable indicating whether the sampled individual has traveled outside the village in the last three months: 1, if yes, 0, if no.

• SES: ordinal variable indicating the socio-economic status (SES) of the household. The variables is an integer score from 1(=poor) to 5(=rich).

• District: factor variable indicating the village of the sampled individual, "Kisii Central" or "Rachuonyo".

• Survey: factor variable indicating the survey in which the participant was enrolled, "community" or "school".

• elevation: elevation, in meters, of the recorded household location

Usage

data(malkenya)

Format

A data frame with 82014 rows and 13 variables

Source

Stevenson, J.C., Stresman, G.H., Gitonga, C.W., Gillig, J., Owaga, C., et al. (2013). Reliability of School Surveys in Estimating Geographic Variation in Malaria Transmission in the Western Kenyan Highlands. PLOS ONE 8(10): e77641. doi: 10.1371/journal.pone.0077641

Malnutrition in Ghana

Description

This geostatistical dataset was extracted from the Demographic and Health Survey 2014 conducted in Ghana.

• lng Longitude of the sampling cluster.

• lat Latitude of the sampling cluster.

• age age in months of the child.

• sex sex of the child.

• HAZ height-for-age Z-score.

• WAZ weight-for-age Z-score

• urb binary indicator: urban area=1; rural area=0.

• etn ethnic group.

• edu level of education of the mother, which takes integer values from 1="Poorly educated" to 3="Highly educated".

• wealth wealth score of the household, which takes integer values from 1="Poor" to 3="Rich".

The coordinate reference system is 3857.

Usage

data(malnutrition)

Format

A data frame with 2671 rows and 10 variables

Source

Demographic and Health Survey, dhsprogram.com

First Derivative with Respect to \phi

Description

Computes the first derivative of the Matern correlation function with respect to \phi.

Usage

matern.grad.phi(U, phi, kappa)

Arguments

Value

A matrix with the values of the first derivative of the Matern function with respect to \phi for the given distances.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

Second Derivative with Respect to \phi

Description

Computes the second derivative of the Matern correlation function with respect to \phi.

Usage

matern.hessian.phi(U, phi, kappa)

Arguments

Value

A matrix with the values of the second derivative of the Matern function with respect to \phi for the given distances.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

Matern Correlation Function

Description

Computes the Matern correlation function.

Usage

matern_cor(u, phi, kappa, return_sym_matrix = FALSE)

Arguments

Details

The Matern correlation function is defined as

Value

A vector of the same length as u with the values of the Matern correlation function for the given distances, if return_sym_matrix=FALSE. If return_sym_matrix=TRUE, a symmetric correlation matrix is returned.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

\rho(u; \phi; \kappa) = (2^{\kappa-1})^{-1}(u/\phi)^\kappa K_{\kappa}(u/\phi)

where \phi and \kappa are the scale and smoothness parameters, and K_{\kappa}(\cdot) denotes the modified Bessel function of the third kind of order \kappa. The parameters \phi and \kappa must be positive.

Maximization of the Integrand for Generalized Linear Gaussian Process Models

Description

Maximizes the integrand function for Generalized Linear Gaussian Process Models (GLGPMs), which involves the evaluation of likelihood functions with spatially correlated random effects.

Usage

maxim.integrand(
  y,
  units_m,
  mu,
  Sigma,
  ID_coords,
  ID_re = NULL,
  family,
  sigma2_re = NULL,
  hessian = FALSE,
  gradient = FALSE,
  invlink = NULL
)

Arguments

Details

This function maximizes the integrand for GLGPMs using the Nelder-Mead optimization algorithm. It computes the likelihood function incorporating spatial covariance and unstructured random effects, if provided.

The integrand includes terms for the spatial process (Sigma), unstructured random effects (sigma2_re), and the likelihood function (llik) based on the specified distribution family ('gaussian', 'binomial', or 'poisson').

Value

A list containing the mode estimate, and optionally, the Hessian matrix and gradient vector.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

Plot Method for RiskMap_pred_target_grid Objects

Description

Generates a plot of the predicted values or summaries over the regular spatial grid from an object of class 'RiskMap_pred_target_grid'.

Usage

## S3 method for class 'RiskMap_pred_target_grid'
plot(x, which_target = "linear_target", which_summary = "mean", ...)

Arguments

Details

This function requires the 'terra' package for spatial data manipulation and plotting. It plots the values or summaries over a regular spatial grid, allowing for visual examination of spatial patterns.

Value

A ggplot object representing the specified prediction target or summary statistic over the spatial grid.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

See Also

pred_target_grid

Plot Method for RiskMap_pred_target_shp Objects

Description

Generates a plot of predictive target values or summaries over a shapefile.

Usage

## S3 method for class 'RiskMap_pred_target_shp'
plot(x, which_target = "linear_target", which_summary = "mean", ...)

Arguments

Details

This function plots the predictive target values or summaries over a shapefile. It requires the 'ggplot2' package for plotting and 'sf' objects for spatial data.

Value

A ggplot object showing the plot of the specified predictive target or summary.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

See Also

pred_target_shp, ggplot, geom_sf, aes, scale_fill_distiller

Plot Calibration Curves (AnPIT / PIT) from Spatial Cross-Validation

Description

Produce calibration plots from a RiskMap.spatial.cv object returned by assess_pp. * For Binomial or Poisson models the function visualises the Aggregated normalised Probability Integral Transform (AnPIT) curves stored in $AnPIT. * For Gaussian models it detects the list $PIT and instead plots the empirical Probability Integral Transform curve (ECDF of PIT values) on the same u-grid.

A 45° dashed red line indicates perfect calibration.

Usage

plot_AnPIT(
  object,
  mode = "average",
  test_set = NULL,
  model_name = NULL,
  combine_panels = FALSE
)

Arguments

Value

A ggplot2 object (single plot) or a ggpubr grid.

Plot the estimated MDA impact function

Description

Generate a plot of the estimated impact of mass drug administration (MDA) on infection prevalence, based on a fitted decay-adjusted spatio-temporal (DAST) model. The function simulates draws from the posterior distribution of model parameters, propagates them through the MDA effect function, and produces uncertainty bands around the estimated impact curve.

Usage

plot_mda(
  object,
  mda_history = NULL,
  n_sim = 1000,
  x_min = 1e-06,
  x_max = 10,
  conf_level = 0.95,
  lower_f = NULL,
  upper_f = NULL,
  mc_cores = 1,
  parallel_backend = c("none", "fork", "psock"),
  ...
)

Arguments

Details

The time axis is assumed to start at 0 and increase in integer steps of 1 year. The argument mda_history allows the user to specify when MDAs occurred either by listing the years directly or by giving a binary indicator on the yearly grid. The function then evaluates the cumulative relative reduction 1 - \mathrm{effect}(t) at a dense grid of time points between x_min and x_max, using the fitted parameters from the supplied DAST model.

Value

A ggplot2 object showing the median estimated MDA impact function and the pointwise uncertainty band at the chosen confidence level.

Plotting the empirical variogram

Description

Plots the empirical variogram generated by s_variogram

Usage

plot_s_variogram(variog_output, plot_envelope = FALSE, color = "royalblue1")

Arguments

Details

This function plots the empirical variogram, which shows the spatial dependence structure of the data. If plot_envelope is set to TRUE, the plot will also include an envelope indicating the range of values under spatial independence, based on a permutation test.

Value

A ggplot object representing the empirical variogram plot, optionally including the envelope of spatial independence.

See Also

s_variogram

Plot Spatial Scores for a Specific Model and Metric

Description

This function visualizes spatial scores for a specified model and metric. It combines test set data, handles duplicate locations by averaging scores, and creates a customizable map using ggplot2.

Usage

plot_score(object, which_score, which_model, ...)

Arguments

Value

A ggplot object visualizing the spatial distribution of the specified score.

Plot simulated surface data for a given simulation

Description

This function plots the simulated surface data for a specific simulation from the result of 'surf_sim'. It visualizes the linear predictor values on a raster grid along with the actual data points.

Usage

plot_sim_surf(surf_obj, sim, ...)

Arguments

Value

A plot of the simulation results.

Prediction of the random effects components and covariates effects over a spatial grid using a fitted generalized linear Gaussian process model

Description

This function computes predictions over a spatial grid using a fitted model obtained from the glgpm function. It provides point predictions and uncertainty estimates for the specified locations for each component of the model separately: the spatial random effects; the unstructured random effects (if included); and the covariates effects.

Usage

pred_over_grid(
  object,
  grid_pred = NULL,
  predictors = NULL,
  re_predictors = NULL,
  pred_cov_offset = NULL,
  control_sim = set_control_sim(),
  type = "marginal",
  messages = TRUE
)

Arguments

Value

An object of class 'RiskMap.pred.re' containing predicted values, uncertainty estimates, and additional information.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

Predictive Target Over a Regular Spatial Grid

Description

Computes predictions over a regular spatial grid using outputs from the pred_over_grid function. This function allows for incorporating covariates, offsets, MDA effects, and optional unstructured random effects into the predictive target.

Usage

pred_target_grid(
  object,
  include_covariates = TRUE,
  include_nugget = FALSE,
  include_cov_offset = FALSE,
  include_mda_effect = TRUE,
  mda_grid = NULL,
  time_pred = NULL,
  include_re = FALSE,
  f_target = NULL,
  pd_summary = NULL
)

Arguments

Value

An object of class 'RiskMap_pred_target_grid' containing predicted values and summaries over the regular spatial grid.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

See Also

pred_over_grid

Predictive Targets over a Shapefile (grid-aggregated)

Description

Computes predictive targets over polygon features using joint prediction samples from pred_over_grid. Targets can incorporate covariates, offsets, optional unstructured random effects, and (if fitted) mass drug administration (MDA) effects from a DAST model.

Usage

pred_target_shp(
  object,
  shp,
  shp_target = mean,
  weights = NULL,
  standardize_weights = FALSE,
  col_names = NULL,
  include_covariates = TRUE,
  include_nugget = FALSE,
  include_cov_offset = FALSE,
  include_mda_effect = TRUE,
  return_shp = TRUE,
  time_pred = NULL,
  mda_grid = NULL,
  include_re = FALSE,
  f_target = NULL,
  pd_summary = NULL,
  messages = TRUE,
  return_target_samples = FALSE
)

Arguments

Details

For each polygon in shp, grid-cell samples of the linear predictor are transformed with f_target, optionally adjusted for covariates, offset, nugget, MDA effects and/or REs, and then aggregated via shp_target (optionally weighted). The list pd_summary is applied to each region's target samples to produce summary statistics.

Value

An object of class RiskMap_pred_target_shp with components:

• target: data.frame of region-level summaries (one row per region).

• target_samples: (optional) list with one element per region; each contains a data.frame/matrix of raw samples for each named target in f_target, if return_target_samples = TRUE.

• shp: (optional) the input sf object with appended summary columns, included if return_shp = TRUE.

• f_target, pd_summary, grid_pred: inputs echoed for reproducibility.

See Also

pred_over_grid, pred_target_grid

Print Summary of RiskMap Model

Description

Provides a print method for the summary of "RiskMap" objects, detailing the model type, parameter estimates, and other relevant statistics.

Usage

## S3 method for class 'summary.RiskMap'
print(x, ...)

Arguments

Details

This function prints a detailed summary of a fitted "RiskMap" model, including:

• The type of geostatistical model (e.g., Gaussian, Binomial, Poisson).

• Confidence intervals for parameter estimates.

• Regression coefficients with their standard errors and p-values.

• Measurement error variance, if applicable.

• Spatial process parameters, including the Matern covariance parameters.

• Variance of the nugget effect, if applicable.

• Unstructured random effects variances, if applicable.

• Log-likelihood of the model.

• Akaike Information Criterion (AIC) for Gaussian models.

Value

This function is used for its side effect of printing to the console. It does not return a value.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

Print Simulation Results

Description

Prints a concise summary of simulation results from a 'RiskMap.sim.res' object, including average metrics by category and a summary of overall correct classification (CC).

Usage

## S3 method for class 'summary.RiskMap.sim.res'
print(x, ...)

Arguments

Value

Invisibly returns 'x'.

Print Simulation Results

Prints a concise summary of simulation results from a 'summary.RiskMap.sim.res' object, including average metrics by category and a summary of overall correct classification (CC).

Invisibly returns 'x'.

Print Summary of RiskMap Spatial Cross-Validation Scores

Description

This function prints the matrix of cross-validation scores produced by 'summary.RiskMap.spatial.cv' in a readable format.

Usage

## S3 method for class 'summary.RiskMap.spatial.cv'
print(x, ...)

Arguments

Details

This method is primarily used to format and display the summary score matrix, printing it to the console. It provides a clear view of the cross-validation performance metrics across different spatial models.

Value

This function is used for its side effect of printing to the console. It does not return a value.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

EPSG of the UTM Zone

Description

Suggests the EPSG code for the UTM zone where the majority of the data falls.

Usage

propose_utm(data)

Arguments

Details

The function determines the UTM zone and hemisphere where the majority of the data points are located and proposes the corresponding EPSG code.

Value

An integer indicating the EPSG code of the UTM zone.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk Claudio Fronterre c.fronterr@lancaster.ac.uk

Random Effect Model Specification

Description

Specifies the terms for a random effect model.

Usage

re(...)

Arguments

Details

The function constructs a list that includes the specified terms for the random effects. This list can be used as a specification for a random effect model.

Value

A list of class re.spec containing the following elements:

Note

At least one variable must be provided as input.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

Empirical variogram

Description

Computes the empirical variogram using “bins” of distance provided by the user.

Usage

s_variogram(
  data,
  variable,
  bins = NULL,
  n_permutation = 0,
  convert_to_utm = TRUE,
  scale_to_km = FALSE
)

Arguments

Value

an object of class 'variogram' which is a list containing the following components

variogram a data-frame containing the following columns: mid_points, the middle points of the classes of distance provided by bins; obs_vari the values of the observed variogram; obs_vari the number of pairs. If n_permutation > 0, the data-frame also contains lower_bound and upper_bound corresponding to the lower and upper bounds of the 95 used to assess the departure of the observed variogram from the assumption of spatial independence.

scale_to_km the value passed to scale_to_km

n_permutation the number of permutations

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

Set Control Parameters for Simulation

Description

This function sets control parameters for running simulations, particularly for MCMC methods. It allows users to specify the number of simulations, burn-in period, thinning interval, and various other parameters necessary for the simulation.

Usage

set_control_sim(
  n_sim = 12000,
  burnin = 2000,
  thin = 10,
  h = NULL,
  c1.h = 0.01,
  c2.h = 1e-04,
  linear_model = FALSE
)

Arguments

Details

The function validates the input parameters and ensures they are appropriate for the simulation that is used in the glgpm fitting function. For non-linear models, it checks that n_sim is greater than burnin, that thin is positive and a divisor of (n_sim - burnin), and that h, c1.h, and c2.h are within their respective valid ranges.

If linear_model is TRUE, only n_sim and linear_model are required, and the function returns a list containing these parameters.

If linear_model is FALSE, the function returns a list containing n_sim, burnin, thin, h, c1.h, c2.h, and linear_model.

Value

A list of control parameters for the simulation with class attribute "mcmc.RiskMap".

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

See Also

Matrix, forceSymmetric

Examples

# Example with default parameters
control_params <- set_control_sim()

# Example with custom parameters
control_params <- set_control_sim(n_sim = 15000, burnin = 3000, thin = 20)

Summarize Model Fits

Description

Provides a summary method for the "RiskMap" class that computes the standard errors and p-values for likelihood-based model fits.

Usage

## S3 method for class 'RiskMap'
summary(object, ..., conf_level = 0.95)

Arguments

Details

This function computes the standard errors and p-values for the parameters of a "RiskMap" model, adjusting for the covariance structure if needed.

Value

A list containing:

Note

Handles both Gaussian and non-Gaussian families, and accounts for fixed and random effects in the model.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

See Also

glgpm, coef.RiskMap

Summarize Simulation Results

Description

Summarizes the results of model evaluations from a 'RiskMap.sim.res' object. Provides average metrics for classification by category and overall correct classification (CC) summary.

Usage

## S3 method for class 'RiskMap.sim.res'
summary(object, ...)

Arguments

Value

A list containing summary data for each model: - 'by_cat_summary': A data frame with average sensitivity, specificity, PPV, NPV, and CC by category. - 'CC_summary': A numeric vector with mean, 2.5th percentile, and 97.5th percentile for CC across simulations.

Summarize Cross-Validation Scores for Spatial RiskMap Models

Description

This function summarizes cross-validation scores for different spatial models obtained from assess_pp.

Usage

## S3 method for class 'RiskMap.spatial.cv'
summary(object, view_all = TRUE, ...)

Arguments

Details

The function computes and returns a matrix where rows correspond to models and columns correspond to performance metrics (e.g., CRPS, SCRPS). Scores are weighted by subset sizes to compute averages. Attributes of the returned object include:

• 'test_set_means': A list of average scores for each test set and model.

• 'overall_averages': Overall averages for each metric across all models.

• 'view_all': Indicates whether averages across test sets are available for visualization.

Value

A matrix of summary scores with models as rows and metrics as columns, with class '"summary.RiskMap.spatial.cv"'.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

See Also

assess_pp

Simulate surface data based on a spatial model

Description

This function simulates surface data based on a user-defined formula and other parameters. It allows for simulation of spatial data with various model families (Gaussian, Binomial, or Poisson). The simulation involves creating spatially correlated random fields and generating outcomes for data points in a given prediction grid.

Usage

surf_sim(
  n_sim,
  pred_grid,
  formula,
  sampling_f,
  family,
  scale_to_km = TRUE,
  control_mcmc = set_control_sim(),
  par0,
  nugget_over_grid = FALSE,
  include_covariates = TRUE,
  fix_var_me = NULL,
  messages = TRUE
)

Arguments

Value

A list containing the simulated data (data_sim), the linear predictors (lp_grid_sim), a logical value indicating if covariates have been included in the linear predictor (include_covariates), a logical value indicating if the nugget has been included into the simulations of the linear predictor over the grid (nugget_over_grid), a logical indicating if a covariate offset has been included in the linear predictor (include_cov_offset), the model parameters set for the simulation (par0) and the family used in the model (family).

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Generate LaTeX Tables from RiskMap Model Fits and Validation

Description

Converts a fitted "RiskMap" model or cross-validation results into an xtable object, formatted for easy export to LaTeX or HTML.

Usage

to_table(object, ...)

Arguments

Details

This function creates a summary table from a fitted "RiskMap" model or cross-validation results for multiple models, returning it as an xtable object.

When the input is a "RiskMap" model object, the table includes:

• Regression coefficients with their estimates, confidence intervals, and p-values.

• Parameters for the spatial process.

• Random effect variances.

• Measurement error variance, if applicable.

When the input is a cross-validation summary object ("summary.RiskMap.spatial.cv"), the table includes:

• A row for each model being compared.

• Performance metrics such as CRPS and SCRPS for each model.

The resulting xtable object can be further customized with additional formatting options and printed as a LaTeX or HTML table for reports or publications.

Value

An object of class "xtable", which contains the formatted table as a data.frame and several attributes specifying table formatting options.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk

See Also

glgpm, xtable, summary.RiskMap.spatial.cv

Covariates Dataset for Malaria Prediction in Tanzania

Description

This dataset provides covariates over a 10 by 10 km regular grid covering Tanzania. It is intended to be used together with the 'tz_malaria' dataset for spatial prediction of malaria prevalence.

Population

Population density in the area (in thousands).

ITN

Percentage of households with at least one insecticide-treated net (ITN).

EVI

Enhanced Vegetation Index, indicating vegetation density.

Temperature

Average temperature in degrees Celsius.

NTL

Nighttime light intensity, indicating urbanization and infrastructure.

Precipitation

Total precipitation in millimeters.

utm_x

UTM (Universal Transverse Mercator) x-coordinate of the grid point.

utm_y

UTM (Universal Transverse Mercator) y-coordinate of the grid point.

Usage

data(tz_covariates)

Format

A data frame with 8740 observations of 8 variables. The CRS of the UTM coordinates is 32736.

Source

Giorgi E, Fronterrè C, Macharia PM, Alegana VA, Snow RW, Diggle PJ. 2021 Model building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict. J. R. Soc. Interface 18: 20210104. doi:10.1098/rsif.2021.0104

Malaria Dataset from Tanzania Demographic Health Surveys 2015

Description

This dataset contains information on malaria prevalence and associated variables from the 2015 Tanzania Demographic Health Surveys. The data includes geographical, demographic, environmental, and health-related variables.

cluster.number

Cluster number, identifying the survey cluster.

Lat

Latitude of the survey cluster.

Long

Longitude of the survey cluster.

MM

Month of the survey (in two-digit format).

YY

Year of the survey.

UpAge

Upper age limit of the surveyed individuals in years.

LoAge

Lower age limit of the surveyed individuals in years.

Ex

Number of individuals examined for malaria.

Pf

Number of individuals tested positive for Plasmodium falciparum (malaria parasite).

PfPR2.10

Plasmodium falciparum parasite rate in the population (aged 2-10 years).

Method

Method used for malaria diagnosis (e.g., Rapid Diagnostic Test (RDT)).

EVI

Enhanced Vegetation Index, indicating vegetation density.

Temperature

Average temperature in degrees Celsius.

Precipitation

Total precipitation in millimeters.

Population

Population density in the area (in thousands).

ITN

Percentage of households with at least one insecticide-treated net (ITN).

NTL

Nighttime light intensity, indicating urbanization and infrastructure.

Urban.Rural

Indicator of whether the area is urban ('U') or rural ('R').

utm_x

UTM (Universal Transverse Mercator) x-coordinate of the survey cluster.

utm_y

UTM (Universal Transverse Mercator) y-coordinate of the survey cluster.

Usage

data(tz_malaria)

Format

A data frame with 387 rows and 20 columns, containing the following variables: The CRS of the UTM coordinates is 32736.

Source

Tanzania Demographic Health Surveys 2015, Giorgi E, Fronterrè C, Macharia PM, Alegana VA, Snow RW, Diggle PJ. (2021) Model building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict. J. R. Soc. Interface 18: 20210104. doi:10.1098/rsif.2021.0104

Update Predictors for a RiskMap Prediction Object

Description

This function updates the predictors of a given RiskMap prediction object. It ensures that the new predictors match the original prediction grid and updates the relevant components of the object accordingly.

Usage

update_predictors(object, predictors)

Arguments

Details

The function performs several checks and updates:

• Ensures that 'object' is of class 'RiskMap.pred.re'.

• Ensures that the number of rows in 'predictors' matches the prediction grid in 'object'.

• Removes any rows with missing values in 'predictors' and updates the corresponding components of the 'object'.

• Updates the prediction locations, the predictive samples for the random effects, and the linear predictor.

Value

The updated 'RiskMap.pred.re' object.

Author(s)

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Claudio Fronterre c.fronterr@lancaster.ac.uk