BAI_prediction {MLFS}

BAI_prediction

Description

The Basal Area Increment BAI sub model that is run within the MLFS

Usage

BAI_prediction(
  df_fit,
  df_predict,
  species_n_threshold = 100,
  site_vars,
  include_climate,
  eval_model_BAI = TRUE,
  rf_mtry = NULL,
  k = 10,
  blocked_cv = TRUE,
  measurement_thresholds = NULL,
  area_correction = NULL
)

Arguments

df_fit

a data frame with Basal Area Increments (BAI) and all independent variables as specified with the formula

df_predict

data frame which will be used for BAI predictions

species_n_threshold

a positive integer defining the minimum number of observations required to treat a species as an independent group

site_vars

a character vector of variable names which are used as site descriptors

include_climate

logical, should climate variables be included as predictors

eval_model_BAI

logical, should the the BAI model be evaluated and returned as the output

rf_mtry

a number of variables randomly sampled as candidates at each split of a random forest model for predicting basal area increments (BAI). If NULL, default settings are applied.

k

the number of folds to be used in the k fold cross-validation

blocked_cv

logical, should the blocked cross-validation be used in the evaluation phase?

measurement_thresholds

data frame with two variables: 1) DBH_threshold and 2) weight. This information is used to assign the correct weights in BAI and increment sub-model; and to upscale plot-level data to hectares.

area_correction

an optional data frame with three variables: 1) plotID and 2) DBH_threshold and 3) the correction factor to be multiplied by weight for this particular category

Value

a list with four elements:

1. $predicted_BAI - a data frame with calculated basal area increments (BAI)

2. $eval_BAI - a data frame with predicted and observed basal area increments (BAI), or a character string indicating that BAI model was not evaluated

3. $rf_model_species - the output model for BAI (species level)

4. $rf_model_speciesGroups - the output model for BAI (species group level)

# add BA to measurement thresholds measurement_thresholds$BA_threshold <- ((measurement_thresholds$DBH_threshold/2)^2 * pi)/10000

BAI_outputs <- BAI_prediction(df_fit = data_BAI, df_predict = data_v6, site_vars = c("slope", "elevation", "northness", "siteIndex"), rf_mtry = 3, species_n_threshold = 100, include_climate = TRUE, eval_model_BAI = FALSE, k = 10, blocked_cv = TRUE, measurement_thresholds = measurement_thresholds)

# get the ranger objects BAI_outputs_model_species <- BAI_outputs$rf_model_species BAI_outputs_model_groups <- BAI_outputs$rf_model_speciesGroups

Examples

library(MLFS)
data(data_BAI)
data(data_v6)
data(measurement_thresholds)