Skip to contents

Multi-chain fit with convergence diagnostics (Gelman-Rubin R-hat, ESS)

Source: R/env_prediction.R
kernel_model_mc.Rd

Runs independent chains with different seeds and reports R-hat and a crude ESS for the residual and each kernel-scale parameter. R-hat above 1.01 or a tiny ESS means the chain has not mixed: increase `iterations`.

Usage

kernel_model_mc(..., n_chains = 3, seed = 1, tol = 1e-10, scale_kernels = TRUE)

Arguments

...

arguments passed to kernel_model (e.g. y, data, random, env, gid).

n_chains

integer. Number of independent chains. Default 3.

seed

integer. Base seed; chain c uses seed + c. Default 1.

tol

numeric. Eigenvalue tolerance passed to the kernel decomposition.

scale_kernels

logical. If TRUE (default) kernels are scaled before fitting.

Value

An object of class "kernel_model" (the fit of the first chain; see kernel_model) with two added elements: diagnostics, a data.frame giving the Gelman-Rubin Rhat and effective sample size ESS for the residual variance and each kernel-scale parameter, and all_chains, the list of individual per-chain fits.

Details

The decomposition is shared across chains.