crosspred_inla: Generate DLNM Predictions from `GHRmodels` Objects

Description

This function takes an object of class GHRmodels, extracts the relevant coefficients and variance-covariance matrix, and then calls dlnm::crosspred to compute predictions over a range of covariate values (or at specified points).

Usage

crosspred_inla(
  models,
  basis,
  mod_id,
  at = NULL,
  from = NULL,
  to = NULL,
  by = NULL,
  lag,
  bylag = 1,
  cen = NULL,
  ci.level = 0.95,
  cumul = FALSE,
  ...
)

Value

An object of class "GHRcrosspred", inheriting from "crosspred", with fields for the predicted values, credible intervals, and optionally cumulative predictions, as determined by crosspred.

Arguments

models: An object of class GHRmodels, containing fitted model output (e.g., $fixed and $vcov lists).
basis: A cross-basis or one-basis object, typically created by crossbasis_inla or onebasis_inla.
mod_id: An integer or character string specifying which model within the input GHRmodels object to use (e.g., if model$fixed and model$vcov both have multiple entries).
at: A numeric vector of values at which to compute predictions (e.g.,seq(10,25, by=0.2))
from, to: Numeric values specifying the range of the prediction sequence if at is not specified (e.g., from = 10 and to = 25).
by: Numeric increment for the sequence if at is not specified (e.g., by = 0.2).
lag: A vector of two elements with min and max lag as declared in the crossbasis_inla function.
bylag: Numeric increment for lag steps (default is 1).
cen: A centering value (e.g., a reference exposure level).
ci.level: The credible interval level (default 0.95).
cumul: Logical; if TRUE, cumulative predictions are computed (default FALSE).
...: Additional arguments passed on to crosspred, such as bound, ci.arg, etc.

Details

The function identifies which coefficients in model$fixed[mod_id] and which rows/columns in model$vcov[mod_id] correspond to the one-basis or cross-basis terms (i.e., matching the column names in basis). Then it passes these slices to dlnm::crosspred to generate predictions. The centering value (cen), if specified, indicates the reference exposure (e.g., a mean temperature) at which to center the effect estimates (e.g., the effect a given temperature value on the outcome will be compared to the effect of the centering value on the outcome, in this case the mean temperature).

Examples

Run this code

# Load example GHRmodels object from the package
model_dlnm_file <- system.file("examples", "model_dlnm.rds", package = "GHRmodel")
model_dlnm <- readRDS(model_dlnm_file)

# Load example cross-basis matrix from the package: 2-dimensional cross-basis matrix of the 
# non-linear effect of dengue risk across tmin values and lags: 
cb_tmin_file <- system.file("examples","cb_tmin.rds", package = "GHRmodel")
cb_tmin <- readRDS(cb_tmin_file) # loads cross-basis matrix into the environment

# Generate predictions
pred_result <- crosspred_inla(
  models    = model_dlnm,
  basis    = cb_tmin,
  mod_id = "mod3",
  at       = seq(17, 24, by = 1),  # e.g., temperature sequence
  lag      = 2,
  cen      = 20,
  ci.level = 0.95
)

# Inspect predictions
pred_result$predvar  # the sequence of 'at' values
pred_result$allfit   # fitted values
pred_result$alllow   # lower CI
pred_result$allhigh  # upper CI