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INLAspacetime

CRAN Status check no-suggestions check pkgdown

This is a R package to implement certain spatial and spatio-temporal models taking use to the cgeneric interface in the INLA package. This interface is a way to implement models by writing C code to build the precision matrix compiling it so that INLA can use it internally.

Installation

The ‘INLA’ package is a suggested one, but you will need it for actually fitting a model. You can install it with

install.packages("INLA",repos=c(getOption("repos"),INLA="https://inla.r-inla-download.org/R/testing"), dep=TRUE)

You can install the current CRAN version of INLAspacetime:

install.packages("INLAspacetime")

You can install the latest version of INLAspacetime from GitHub with

## install.packages("remotes")
remotes::install_github("eliaskrainski/INLAspacetime",  build_vignettes=TRUE)

We have implemented

  1. some of the models presented in https://www.idescat.cat/sort/sort481/48.1.1.Lindgren-etal.pdf

  2. the barrier model proposed in https://doi.org/10.1016/j.spasta.2019.01.002

Example

This is a basic example which fit a spacetime model for some fake data. The model fitting using inlabru facilitates coding.

set.seed(1)
n <- 5
dataf <- data.frame(
    s1   = runif(n, -1, 1),
    s2   = runif(n, -1, 1),
    time = runif(n, 1, 4),
    y    = rnorm(n, 0, 1))
str(dataf)
#> 'data.frame':    5 obs. of  4 variables:
#>  $ s1  : num  -0.469 -0.256 0.146 0.816 -0.597
#>  $ s2  : num  0.797 0.889 0.322 0.258 -0.876
#>  $ time: num  1.62 1.53 3.06 2.15 3.31
#>  $ y   : num  -0.00577 2.40465 0.76359 -0.79901 -1.14766

Loading the packages:

library(INLA)
library(INLAspacetime)
#> Loading required package: fmesher
library(inlabru)

Define spatial and temporal discretization meshes

smesh <- inla.mesh.2d(
  loc = cbind(0,0), 
  max.edge = 5, 
  offset = 2)
tmesh <- inla.mesh.1d(
  loc = 0:5)

Define the spacetime model object to be used

stmodel <- stModel.define(
    smesh = smesh, ## spatial mesh
    tmesh = tmesh, ## temporal mesh
    model = '121', ## model, see the paper
    control.priors = list(
        prs = c(1, 0.1), ## P(spatial range < 1) = 0.1
        prt = c(5, 0), ## temporal range fixed to 5
        psigma = c(1, 0.1) ## P(sigma > 1) = 0.1
        )
    )

Define the data model: the linear predictor terms

linpred <- ~ 1 +
    field(list(space = cbind(s1, s2), 
               time = time),
          model = stmodel)

Setting the likelihood

ctrlf <- list(
  hyper = list(
    prec = list(
      initial = 10, 
      fixed = TRUE)    
  )
)
datalike <- like(
  formula = y ~ ., 
  family = "gaussian",
  control.family = ctrlf, 
  data=dataf)

Fitting

result <- 
  bru(
    components = linpred,
    datalike,
    options = list(
      control.inla = list(
        int.strategy = "eb"
        ),
      verbose = !TRUE)
    )

Summary of the model parameters

result$summary.fixed
#>                mean       sd 0.025quant  0.5quant 0.975quant      mode kld
#> Intercept 0.5264782 3.500849   -6.33506 0.5264782   7.388016 0.5264782   0
result$summary.hyperpar
#>                      mean        sd 0.025quant 0.5quant 0.975quant     mode
#> Theta1 for field 1.190361 0.4867876  0.3624381 1.153754   2.255724 0.972674
#> Theta2 for field 1.435282 0.1709783  1.1034628 1.433661   1.776664 1.426789

Vignettes

Please check it out at the Tutorials

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.