Declare the sources of a multi-source integrated model

Usage

isdm_sources(...)

Arguments

...

Two or more named arguments; each name is a source label and each value is a bare admitted observation law or an isdm_source() declaration. At least one source must be a count stream: the detection arm's offset is admitted only alongside a count arm sharing the same intensity, so an all-detection declaration is refused here rather than failing later.

Value

A mixed-family list understood by gllvmTMB(), with family_var = "isdm_source". (The isdm_source_laws attribute is informational only: internal validation rebuilds the declaration from the list's names and laws, which survive reordering; the attribute does not.)

Details

Builds the family argument for an integrated species-distribution fit in which any number of named observation sources – an opportunistic portal stream, digitised literature records, checklists, a structured survey – share one ecological linear predictor while each source keeps its own observation law.

Each argument is named for a source and gives either a bare observation law or isdm_source() with its source-specific observation formula. Two laws are admitted, because both express the observation as a thinning of one shared intensity: poisson() (a count stream; the offset is log effort) and binomial("cloglog") (detection/non-detection; the offset is log support, and the complementary log-log link is what makes a detection consistent with the same intensity that generates the counts). Other links and families are refused: a logit detection model, for instance, does not share a scale with a count arm merely because both converge.

The data must carry an isdm_source column whose values are exactly the declared source names, and – when the declaration mixes the two laws – every trait must be observed by every declared source. Everything such a fit reports is relative intensity: presence-only data cannot identify absolute abundance, occupancy, or detectability, and none are estimated. This interface is experimental and may change.

Examples

fam <- isdm_sources(
  gbif = isdm_source(poisson(), observation = ~ access + popdens),
  survey = isdm_source(poisson(), observation = ~ observer + method)
)
names(fam)
#> [1] "gbif"   "survey"
if (FALSE) { # \dontrun{
## `dat` is long, one row per (cell, species, source), with an isdm_source
## column naming each row's source and log_support its known effort/area.
fit <- gllvmTMB(
  value ~ 0 + trait + trait:env +
    offset(log_support) + latent(0 + trait | cell_id, d = 1),
  data = dat, trait = "trait", unit = "cell_id", family = fam
)
} # }