fit_rf_metamodel: Fit random forest metamodel

In Xa4P/pacheck: Probabilistic Analysis Check Package

Fit random forest metamodel

Description

This function fits a random forest metamodel using the randomForestSRC package.

Usage

fit_rf_metamodel(
  df,
  y_var = NULL,
  x_vars = NULL,
  seed_num = 1,
  tune = FALSE,
  var_importance = FALSE,
  pm_plot = FALSE,
  pm_vars = x_vars[1],
  validation = FALSE,
  folds = 5,
  show_intercept = FALSE,
  partition = 1,
  fit_complete_model = TRUE
)

Arguments

df	a dataframe.
y_var	character. Name of the output variable in the dataframe. This will be the dependent variable of the metamodel.
x_vars	character or a vector for characters. Name of the input variable(s) in the dataframe. This will be the independent variable of the metamodel.
seed_num	numeric. Determine which seed number to use to split the dataframe in fitting an validation sets.
tune	logical. Determine whether nodesize and mtry should be tuned. Nodesize is the minimum size of terminal nodes, mtry is number of variables to possibly split at each node. If FALSE, nodesize = 15 (for regression), and mtry = number of x-variables / 3 (for regression). Default is FALSE.
var_importance	logical or character. Determine whether to compute variable importance (TRUE/FALSE), or how to compute variable importance (permute/random/anti). Default is FALSE. TRUE corresponds to "anti".
pm_plot	logical or character. Determine whether to plot the partial ("partial") or marginal ("marginal") effect or both ("both") of an x-variable (which is denoted by pm_vars). Default is FALSE. TRUE corresponds to "both".
pm_vars	character. Name of the input variable(s) for the partial/marginal plot. Default is the first variable from the x_vars.
validation	logical or character. Determine whether to validate the RF model. Choices are "test_train_split" and "cross-validation". TRUE corresponds to "cross-validation", default is FALSE.
folds	numeric. Number of folds for the cross-validation. Default is 5.
show_intercept	logical. Determine whether to show the intercept of the perfect prediction line (x = 0, y = 0). Default is FALSE.
partition	numeric. Value between 0 and 1 to determine the proportion of the observations to use to fit the metamodel. Default is 1 (fitting the metamodel using all observations).
fit_complete_model	logical. Determine whether to fit the (final) full model. So the model trained on all available data (as opposed to the model used in validation which is trained on the test data).

Value

A list containing the following elements:

• fit: a list, see randomForestSRC::rfsrc() for a description of the outputs contained in this list.

• model_info: a list containing the following elements:

  • x_vars: vector of names of parameters included in the metamodel;

  • y_var: name outcome variable;

  • form: formula of the metamodel based on 'x_vars' and 'y_var';

  • data: dataframe containing the inputs and output values used to fit (and fit) the metamodel;

  • type: "rf" for "random forest".

• (if 'tune' = TRUE) tune_fit: a list containing the results of the tuning process, see randomForestSRC::tune() for a description of the elements containd in this list.

• (if ‘tune' = TRUE) tune_plot: plot showing the out-of-bag error for each tested combination of ’mtry' and 'nodesize'.

• (if validation != FALSE) stats_validation: data frame containing the R-squared, Mean absolute error, Mean relative error, Mean squared error in the test validation set.

• (if validation = "test_train_split") calibration_plot: plot showing the rf-predicted versus observed output values in the test validation set.

If 'var_importance' is set to TRUE, the variable importance plot is printed in the console. If 'pm_plot'is used, the marginal/ partial importance plot(s) - drawn using randomForestSRC::plot.variable.rfsrc() - is (are) printed in the console.

Examples

# Fitting and tuning a random forest meta model with two variables using the example data
data(df_pa)
fit_rf_metamodel(df = df_pa,
                 y_var = "inc_qaly",
                 x_vars = c("p_pfsd", "p_pdd"),
                 tune = TRUE
                 )

Xa4P/pacheck documentation built on April 14, 2025, 1:51 p.m.