Fit models and compute the log marginal likelihood for all clusters

Fit the specified INLA model to each cluster and compute the log marginal likelihood for each cluster specified in the membership vector.

Usage

log_mlik_all(
  membership,
  stdata,
  stnames = c("geometry", "time"),
  correction = TRUE,
  detailed = FALSE,
  ...
)

Arguments

membership: Integer, character or factor vector indicating the cluster membership for each spatial unit.
stdata: A stars object with spatial-temporal dimensions defined in stnames, and including predictors and response variables.
stnames: The names of the spatial and temporal dimensions of the stdata object.
correction: Logical value indicating whether a correction for dispersion.
detailed: Logical value indicating whether to return the INLA model instead of the log marginal likelihood. The argument correction is not applied in this case.
...: Arguments passed to the inla function (eg. family, formula and E).

Value

A numeric vector containing the log marginal likelihood for each cluster or the the fitted INLA model for each cluster when detailed = TRUE.

Examples



# \donttest{
library(sfclust)
library(stars)

dims <- st_dimensions(
  geometry = st_sfc(lapply(1:5, function(i) st_point(c(i, i)))),
  time = seq(as.Date("2024-01-01"), by = "1 day", length.out = 3)
)
stdata <- st_as_stars(
  cases = array(rpois(15, 100 * exp(1)), dim = c(5, 3)),
  temperature = array(runif(15, 15, 20), dim = c(5, 3)),
  expected = array(100, dim = c(5, 3)),
  dimensions = dims
)

log_mlik_all(c(1, 1, 1, 2, 2), stdata,
  formula = cases ~ temperature, family = "poisson", E = expected)
#> [1] -46.58291 -33.48540

models = log_mlik_all(c(1, 1, 1, 2, 2), stdata, detailed = TRUE,
  formula = cases ~ temperature, family = "poisson", E = expected)
lapply(models, summary)
#> [[1]]
#> Time used:
#>     Pre = 0.211, Running = 0.235, Post = 0.00979, Total = 0.455 
#> Fixed effects:
#>               mean    sd 0.025quant 0.5quant 0.975quant   mode kld
#> (Intercept)  1.174 0.280      0.625    1.174      1.723  1.174   0
#> temperature -0.009 0.016     -0.040   -0.009      0.022 -0.009   0
#> 
#> Marginal log-Likelihood:  -46.58 
#>  is computed 
#> Posterior summaries for the linear predictor and the fitted values are computed
#> (Posterior marginals needs also 'control.compute=list(return.marginals.predictor=TRUE)')
#> 
#> 
#> [[2]]
#> Time used:
#>     Pre = 0.209, Running = 0.268, Post = 0.0157, Total = 0.493 
#> Fixed effects:
#>               mean    sd 0.025quant 0.5quant 0.975quant   mode kld
#> (Intercept)  1.092 0.404      0.299    1.092      1.884  1.092   0
#> temperature -0.006 0.023     -0.052   -0.006      0.040 -0.006   0
#> 
#> Marginal log-Likelihood:  -33.48 
#>  is computed 
#> Posterior summaries for the linear predictor and the fitted values are computed
#> (Posterior marginals needs also 'control.compute=list(return.marginals.predictor=TRUE)')
#> 
#> 
# }