Jointly invert multiple observations from one record

Sends all rows through one joint Bayesian inversion so they coherently reweight the shared calibration draws. Chunked and parallel processing are deliberately disabled because splitting a record changes that joint target.

Usage

batch_predict(
  data,
  model = "auto",
  chunk_size = 100,
  parallel = FALSE,
  n_cores = NULL,
  progress = TRUE,
  return_diagnostics = FALSE,
  ...
)

Arguments

data

Data frame containing observations from one same-site record. A record_id column is required when there is more than one row.

model

Model name or "auto" for automatic selection

chunk_size

Retained for compatibility and recorded in diagnostics; it does not split the joint inversion.

parallel

Must be FALSE; parallel chunks would change the target.

n_cores

Retained for compatibility and diagnostics.

progress

Logical whether to show progress bar

return_diagnostics

Logical whether to return diagnostic information

...

Additional arguments passed to predict_d2h_precip

Value

A leafwax_inverse object for the jointly inverted rows.

Examples

if (FALSE) { # \dontrun{
local({
  old <- options(leafwax.suppress_preview_warning = TRUE)
  on.exit(options(old))

  data(example_data)
  prior <- d2h_prior_normal(mean = -70, sd = 30)
  large_data <- example_data[rep(seq_len(nrow(example_data)), length.out = 12), ]
  row.names(large_data) <- NULL
  large_data$longitude <- large_data$longitude[[1]]
  large_data$latitude <- large_data$latitude[[1]]
  large_data$record_id <- "example_record"

  # Process in chunks
  results <- batch_predict(
    large_data,
    chunk_size = 6,
    progress = FALSE,
    prior = prior,
    verbose = FALSE
  )

  # Process with a specific model
  results <- batch_predict(
    large_data,
    model = "baseline_sp",
    chunk_size = 6,
    progress = FALSE,
    prior = prior,
    verbose = FALSE
  )
})
} # }