Predict dense SWRC curves for a set of soil profiles — predict_swrc

For each unique (PEDON_ID × depth) profile in newdata, predicts theta across a dense grid of pF values and returns a tidy long-format tibble.

Usage

predict_swrc_dense(
  object,
  newdata,
  n_points = 1000L,
  pf_range = NULL,
  id_cols = c("PEDON_ID", "Depth_num", "Depth_label", "Texture")
)

Arguments

object: A swrc_fit object.
newdata: A data frame with covariate columns plus (optionally) PEDON_ID, Depth_num, Depth_label, and Texture.
n_points: Number of equally spaced pF points (default 1000).
pf_range: Numeric vector of length 2: min and max pF values for the output grid (default c(-2, 7.6)).
id_cols: Character vector of columns used to identify profiles (default c("PEDON_ID","Depth_num","Depth_label","Texture")).

Value

A tibble with columns: all id_cols present in newdata, pF, matric_head, and theta (predicted volumetric water content in m3/m3).

Examples

if (FALSE) { # \dontrun{
if (reticulate::py_module_available("tensorflow")) {
  df    <- prepare_swrc_data(swrc_example, depth_col = "depth")
  fit   <- fit_swrc(df,
                    x_inputs = c("clay", "silt", "bd_gcm3", "soc", "Depth_num"),
                    epochs = 2L, verbose = FALSE)
  dense <- predict_swrc_dense(fit, newdata = df, n_points = 50)
}
} # }