predict_mortality {MLFS}

predict_mortality

Description

This sub model first fits a binary model to derive the effects of individual tree, site and climate variables on mortality; and afterwards predict the probability of dying for each tree from df_predict

Usage

predict_mortality(
  df_fit,
  df_predict,
  df_climate,
  mortality_share = NA,
  mortality_share_type = "volume",
  include_climate,
  site_vars,
  select_months_climate = c(6, 8),
  mortality_model = "rf",
  nb_laplace = 0,
  sim_crownHeight = FALSE,
  k = 10,
  eval_model_mortality = TRUE,
  blocked_cv = TRUE,
  sim_mortality = TRUE,
  sim_step_years = 5,
  rf_mtry = NULL,
  df_max_size = NULL,
  ingrowth_codes = 3,
  include_mortality_BAI = TRUE,
  intermediate_print = FALSE
)

Arguments

df_fit

a data frame with individual tree data and site descriptors where code is used to specify a status of each tree

df_predict

data frame which will be used for mortality predictions

df_climate

data frame with monthly climate data

mortality_share

a value defining the proportion of the volume which is to be the subject of mortality

mortality_share_type

character, it can be 'volume' or 'n_trees'. If 'volume' then the mortality share relates to total standing volume, if 'n_trees' then mortality share relates to the total number of standing trees

include_climate

logical, should climate variables be included as predictors

site_vars

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

select_months_climate

vector of subset months to be considered. Default is c(1,12), which uses all months.

mortality_model

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

nb_laplace

value used for Laplace smoothing (additive smoothing) in naive Bayes algorithm. Defaults to 0 (no Laplace smoothing).

sim_crownHeight

logical, should crown heights be considered as a predictor variable? If TRUE, a crownHeight column is expected in data_NFI

k

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

eval_model_mortality

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

blocked_cv

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

sim_mortality

logical, should mortality be simulated?

sim_step_years

the simulation step in years

rf_mtry

number of variables randomly sampled as candidates at each split of a random forest model. If NULL, default settings are applied.

df_max_size

a data frame with the maximum BA values for each species. If a tree exceeds this value, it dies.

ingrowth_codes

numeric value or a vector of codes which refer to ingrowth trees

include_mortality_BAI

logical, should basal area increments (BAI) be used as independent variable for predicting individual tree morality?

intermediate_print

logical, if TRUE intermediate steps will be printed while the mortality sub model is running

Value

a list with three elements:

1. $predicted_mortality - a data frame with updated tree status (code) based on the predicted mortality

2. $eval_mortality - a data frame with predicted and observed probabilities of dying for all individual trees, or character string indicating that mortality sub-model was not evaluated

3. $model_output - the output model for mortality

Examples

data("data_v4")
data("data_mortality")
data("max_size_data")

mortality_outputs <- predict_mortality(
 df_fit = data_mortality,
 df_predict = data_v4,
 mortality_share_type = 'volume',
 df_climate = data_climate,
 site_vars = c("slope", "elevation", "northness", "siteIndex"),
 sim_mortality = TRUE,
 mortality_model = 'naiveBayes',
 nb_laplace = 0,
 sim_crownHeight = TRUE,
 mortality_share = 0.02,
 include_climate = TRUE,
 select_months_climate = c(6,7,8),
 eval_model_mortality = TRUE,
 k = 10, blocked_cv = TRUE,
 sim_step_years = 6,
 df_max_size = max_size_data,
 ingrowth_codes = c(3,15),
 include_mortality_BAI = TRUE)

 df_predicted <- mortality_outputs$predicted_mortality
 df_evaluation <- mortality_outputs$eval_mortality

 # confusion matrix
 table(df_evaluation$mortality, round(df_evaluation$mortality_pred, 0))