MLFS (version 0.4.2)

crownHeight_prediction: crownHeight_prediction

Description

Model for predicting crown height

Usage

crownHeight_prediction(
  df_fit,
  df_predict,
  site_vars = site_vars,
  species_n_threshold = 100,
  k = 10,
  eval_model_crownHeight = TRUE,
  crownHeight_model = "lm",
  BRNN_neurons = 3,
  blocked_cv = TRUE
)

Value

a list with four elements:

1. $predicted_crownHeight - a data frame with imputed crown heights

2. $eval_crownHeight - a data frame with predicted and observed crown heights, or a character string indicating that crown height model was not evaluated

3. $model_species - the output model for crown heights (species level)

4. $model_speciesGroups - the output model for crown heights (species group level)

Arguments

df_fit

data frame with tree heights and basal areas for individual trees

df_predict

data frame which will be used for predictions

site_vars

optional, character vector with names of site variables

species_n_threshold

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

k

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

eval_model_crownHeight

logical, should the crown height model be evaluated and returned as the output

crownHeight_model

character string defining the model to be used for crown heights. Available are ANN with Bayesian regularization (brnn) or linear regression (lm)

BRNN_neurons

positive integer defining the number of neurons to be used in the brnn method.

blocked_cv

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

Examples

library(MLFS)
data(data_tree_heights)
data(data_v3)

# A) Example with linear model
Crown_h_predictions <- crownHeight_prediction(df_fit = data_tree_heights,
    df_predict = data_v3,
    crownHeight_model = "lm",
    site_vars = c(),
    species_n_threshold = 100,
    k = 10, blocked_cv = TRUE,
    eval_model_crownHeight = TRUE)

predicted_df <- Crown_h_predictions$predicted_crownHeight # df with imputed heights
evaluation_df <- Crown_h_predictions$eval_crownHeight # df with evaluation results

# B) Example with non-linear BRNN model
Crown_h_predictions <- crownHeight_prediction(df_fit = data_tree_heights,
    df_predict = data_v3,
    crownHeight_model = "brnn",
    BRNN_neurons = 3,
    site_vars = c(),
    species_n_threshold = 100,
    k = 10, blocked_cv = TRUE,
    eval_model_crownHeight = TRUE)