make_model_object {disaggregation}R Documentation

Create the TMB model object for the disaggregation model


make_model_object function takes a disag_data object created by prepare_data and creates a TMB model object to be used in fitting.


  priors = NULL,
  family = "gaussian",
  link = "identity",
  field = TRUE,
  iid = TRUE,
  silent = TRUE



disag_data object returned by prepare_data function that contains all the necessary objects for the model fitting


list of prior values


likelihood function: gaussian, binomial or poisson


link function: logit, log or identity


logical. Flag the spatial field on or off


logical. Flag the iid effect on or off


logical. Suppress verbose output.


The model definition

The disaggregation model make predictions at the pixel level:

link(pred_i) = \beta_0 + \beta X + GP(s_i) + u_i

And then aggregates these predictions to the polygon level using the weighted sum (via the aggregation raster, agg_i):

cases_j = \sum_{i \epsilon j} pred_i \times agg_i

rate_j = \frac{\sum_{i \epsilon j} pred_i \times agg_i}{\sum_{i \epsilon j} agg_i}

The different likelihood correspond to slightly different models (y_j is the response count data):

Specify priors for the regression parameters, field and iid effect as a single named list. Hyperpriors for the field are given as penalised complexity priors you specify \rho_{min} and \rho_{prob} for the range of the field where P(\rho < \rho_{min}) = \rho_{prob}, and \sigma_{min} and \sigma_{prob} for the variation of the field where P(\sigma > \sigma_{min}) = \sigma_{prob}. Also, specify pc priors for the iid effect.

The precise names and default values for these priors are:

The family and link arguments are used to specify the likelihood and link function respectively. The likelihood function can be one of gaussian, poisson or binomial. The link function can be one of logit, log or identity. These are specified as strings.

The field and iid effect can be turned on or off via the field and iid logical flags. Both are default TRUE.

The iterations argument specifies the maximum number of iterations the model can run for to find an optimal point.

The silent argument can be used to publish/supress verbose output. Default TRUE.


The TMB model object returned by MakeADFun.


## Not run: 
polygons <- list()
n_polygon_per_side <- 10
n_polygons <- n_polygon_per_side * n_polygon_per_side
n_pixels_per_side <- n_polygon_per_side * 2

for(i in 1:n_polygons) {
  row <- ceiling(i/n_polygon_per_side)
  col <- ifelse(i %% n_polygon_per_side != 0, i %% n_polygon_per_side, n_polygon_per_side)
  xmin = 2*(col - 1); xmax = 2*col; ymin = 2*(row - 1); ymax = 2*row
  polygons[[i]] <- list(cbind(c(xmin, xmax, xmax, xmin, xmin),
                              c(ymax, ymax, ymin, ymin, ymax)))

polys <- lapply(polygons,sf::st_polygon)
N <- floor(runif(n_polygons, min = 1, max = 100))
response_df <- data.frame(area_id = 1:n_polygons, response = runif(n_polygons, min = 0, max = 1000))

spdf <- sf::st_sf(response_df, geometry = polys)

# Create raster stack
r <- terra::rast(ncol=n_pixels_per_side, nrow=n_pixels_per_side)
terra::ext(r) <- terra::ext(spdf)
r[] <- sapply(1:terra::ncell(r), function(x){
rnorm(1, ifelse(x %% n_pixels_per_side != 0, x %% n_pixels_per_side, n_pixels_per_side), 3))}
r2 <- terra::rast(ncol=n_pixels_per_side, nrow=n_pixels_per_side)
terra::ext(r2) <- terra::ext(spdf)
r2[] <- sapply(1:terra::ncell(r), function(x) rnorm(1, ceiling(x/n_pixels_per_side), 3))
cov_stack <- c(r, r2)
names(cov_stack) <- c('layer1', 'layer2')

test_data <- prepare_data(polygon_shapefile = spdf,
                          covariate_rasters = cov_stack)

 result <- make_model_object(test_data)
## End(Not run)

[Package disaggregation version 0.3.0 Index]