Processes multiple sites with progress indicators and optional parallelization. Handles large datasets efficiently by processing in chunks.
Usage
batch_predict(
data,
model = "auto",
chunk_size = 100,
parallel = FALSE,
n_cores = NULL,
progress = TRUE,
return_diagnostics = FALSE,
...
)Arguments
- data
Data frame containing all measurements
- model
Model name or "auto" for automatic selection
- chunk_size
Number of sites to process at once (default 100)
- parallel
Logical whether to use parallel processing
- n_cores
Number of cores for parallel processing (NULL for auto)
- progress
Logical whether to show progress bar
- return_diagnostics
Logical whether to return diagnostic information
- ...
Additional arguments passed to predict_d2h_precip
Examples
if (FALSE) { # \dontrun{
# Load a large dataset
large_data <- read.csv("sites.csv")
# Process with progress bar
results <- batch_predict(large_data, progress = TRUE)
# Process in parallel
results <- batch_predict(large_data, parallel = TRUE, n_cores = 4)
# Process with specific model
results <- batch_predict(large_data, model = "baseline_env_sp")
} # }