RiskMap

The goal of RiskMap is to provide a set of functions for visualisation, processing and likelihood-based analysis of geostatistical data.

Installation

You can install the development version of RiskMap from GitHub with:

# install.packages("devtools")
devtools::install_github("claudiofronterre/RiskMap")

Example

This is a basic example which shows you how to solve a common problem:

library(RiskMap)
## basic example code

What is special about using README.Rmd instead of just README.md? You can include R chunks like so:

summary(cars)
#>      speed           dist       
#>  Min.   : 4.0   Min.   :  2.00  
#>  1st Qu.:12.0   1st Qu.: 26.00  
#>  Median :15.0   Median : 36.00  
#>  Mean   :15.4   Mean   : 42.98  
#>  3rd Qu.:19.0   3rd Qu.: 56.00  
#>  Max.   :25.0   Max.   :120.00

You’ll still need to render README.Rmd regularly, to keep README.md up-to-date. devtools::build_readme() is handy for this. You could also use GitHub Actions to re-render README.Rmd every time you push. An example workflow can be found here: https://github.com/r-lib/actions/tree/v1/examples.

You can also embed plots, for example:

In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub and CRAN.

Estimation function design

How should the user specify the model?

# OPTION 1
y ~ rainfall + gp(x, y) + re(id_school) + re(id_region)

# Arguments for gp function
gp(long = NULL, lat = NULL, kappa = (numeric_value, default = 0.5), nugget = c(T = default, F, fixed_numeric_value), ...)

# Arguments for re function
re(numeric or categorical variable, ...) only needs an index in the dataset

glgm <- function(formula,
                 distr_offset = NULL,
                 cov_offset = NULL,
                 data,
                 family,
                 convert_to_crs = NULL,
                 scale_to_km = TRUE,
                 control_MCMC = NULL,
                 S_samples = NULL,
                 save_samples = F,
                 messages = TRUE)

My solution to incorporate “gp” into the formula

(tf <- terms(y ~ x + x:z + gp(kappa = 0.5, nugget = TRUE)+w, specials = "gp"))
attr(tf, "specials")    # index 's' variable(s)
gp <- rownames(attr(tf, "factors"))[[attr(tf, "specials")$gp]]
gp_list <- eval(parse(text = gsub("gp","list",gp)))
gp_list