GHRmodel (version 0.1.1)

cov_varying: Create Spatially or Temporally Varying Effects for INLA

Description

This function transforms covariates identified by pattern or name into varying effect terms of the form:f(unit, covariate, model = 'iid'), which allows covariates to have varying slopes across spatial or temporal units. The output can be used directly in the covariates argument of write_inla_formulas.

Usage

cov_varying(
  covariates,
  unit,
  pattern = NULL,
  name = NULL,
  model = "iid",
  constr = FALSE,
  add = FALSE
)

Value

A list of character vectors, each including covariates with varying effects. The output is suitable as input for write_inla_formulas.

Arguments

covariates

A character vector or a list of character vectors of covariate names. Typically output from cov_multi, cov_uni, or extract_names.

unit

Character string specifying the unit of variation (e.g., "spat_id", "year").

pattern

A character vector specifying the prefix pattern(s) to match (e.g., "tmin" matches "tmin", "tmin.l1", etc.) for transformation.

name

Character vector of exact variable names to be transformed.

model

Character string specifying the INLA model for the varying effect. Currently, only "iid" is supported.

constr

Logical. If TRUE it will impose a sum-to-zero constraint to the random effect. Default is FALSE.

add

Logical; if TRUE, appends the transformed covariates to the original ones. Default is FALSE.

Details

• Use pattern or name (or both) to specify which covariates to transform.

• The resulting terms use INLA’s f() syntax: f(unit, covariate, model = "iid").

• Currently only supports "iid" models for varying effects.

Examples

data <- data.frame(tmin.l1 = rnorm(10), pdsi.l1 = rnorm(10))

covs <- extract_names(data, pattern = c("tmin", "pdsi"))
covlist <- cov_multi(covs, pattern = c("tmin", "pdsi"))

# Apply varying effect to tmin
cov_varying(covlist, pattern = "tmin", unit = "spat_id")

# Keep original and add varying effect terms
cov_varying(covlist, pattern = "tmin", unit = "spat_id", add = TRUE)