fitted.stLMM: Posterior fitted values for an stLMM fit

In stLMM: Bayesian Spatial and Space-Time Linear Mixed Models

Posterior fitted values for an stLMM fit

Description

Computes posterior fitted values for the original data used to fit an stLMM model. These fitted values include all model components that were fit: formula offsets, fixed effects, explicit grouped random effects, and saved or recovered structured latent process effects. Binomial and probit fitted values default to posterior fitted probabilities, and negative-binomial fitted values default to posterior fitted mean counts.

Usage

## S3 method for class 'stLMM'
fitted(object, summary = TRUE,
  sub_sample = list(start = 1L, thin = 1L), scale = c("response", "link"),
  ...)

Arguments

object	An object returned by stLMM or recover.
summary	Logical; if TRUE, return posterior mean fitted values. If FALSE, return the posterior fitted-value sample matrix.
sub_sample	List with optional start and thin entries used to subset posterior draws. For models with structured process terms, this subsets the saved or recovered latent-process draw indices in object$recover_iter.
scale	For family = "binomial", return fitted probabilities on the "response" scale or the linear predictor on the "link" scale. For family = "probit", return fitted probabilities on the "response" scale or the probit linear predictor on the "link" scale. For family = "negative_binomial", return fitted mean counts on the "response" scale or the log mean on the "link" scale. For Gaussian models these scales are identical.
...	Currently unused.

Details

This method does not predict at new locations. For models with structured process terms, fitted values require saved or recovered latent process samples. For augmented non-Gaussian process models, stLMM() saves in-chain process draws by default as save_process = list(start = 1, thin = 1) and recover() subsets them. For Gaussian process models, call recover() on the fitted object to make those samples available.

Value

If summary = TRUE, a numeric vector of posterior mean fitted values for the original fitted data rows, including rows whose response was missing at fit time. If summary = FALSE, a matrix with posterior draws in rows and original data rows in columns. The matrix has a draw_index attribute giving the original MCMC iteration indices used and, for summary = FALSE non-Gaussian fits, a scale attribute.

stLMM documentation built on Nov. 12, 2026, 1:07 a.m.