This function takes prediction arrays from a model output and converts them into data frames. The data frames can be returned in either 'wide' or 'long' format. The function processes site-level, patch-level, and cohort-level predictions.
Arguments
- pred
A list containing prediction arrays for site-level, patch-level, and cohort-level data. Each element of
predshould have a structure similar to what is outlined in the details section.- format
A character string indicating the desired format of the output data frames. Must be either "wide" (default) or "long".
Value
A list of data frames. The list may contain up to three elements:
site, patch, and cohort, corresponding to the processed site-level,
patch-level, and cohort-level predictions, respectively.
Details
The pred argument should be a list containing at least a Predictions element,
which itself is a list of arrays. The arrays represent predictions for different
metrics such as dbh/ba, tree counts, AL (aboveground live biomass), growth rates,
mortality rates, and regeneration rates for sites, patches, or cohorts. The
dimensionality of the arrays should correspond to different factors, such as
siteID, year, species, and optionally patch or cohortID.
The function first converts each prediction array into a data frame, properly
naming and converting the relevant dimensions. It then merges these data frames
by common identifiers such as siteID, year, species, patch, and cohortID.
Depending on the format parameter, the data frames are returned in either a
wide format (one row per site/patch/cohort per year with multiple columns for
different metrics) or a long format (one row per site/patch/cohort per year
per metric).
Examples
if (FALSE) { # \dontrun{
# Assuming `model_output` is a list with the structure required by `pred2DF`
result <- pred2DF(model_output, format = "long")
print(result$site)
} # }