validate_metamodel: Validate metamodels

In Xa4P/pacheck: Probabilistic Analysis Check Package

Validate metamodels

Description

Validate metamodels

Usage

validate_metamodel(
  model = NULL,
  method = NULL,
  partition = 1,
  folds = 1,
  show_intercept = FALSE,
  seed_num = 1,
  df_validate = NULL
)

Arguments

model	model object. Built using a function from the PACHECK package.
method	character, validation method. Choices are: cross-validation ('cross_validation'), train-test split ('train_test_split'), or the user can input a new dataframe which will be used as the test-set ('new_test_set'). No default.
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).
folds	numeric. Number of folds for the cross-validation. Default is 1 (so an error occurs when not specifying this argument when cross-validation is chosen).
show_intercept	logical. Determine whether to show the intercept of the perfect prediction line (x = 0, y = 0). Default is FALSE.
seed_num	numeric. Determine which seed number to use to split the dataframe in fitting and validation sets.
validate_df	dataframe. The dataframe to be used for validating the model. By default the dataframe used when building the model is used.

Value

.........................

Examples

#Validating meta model with two variables using the probabilistic data, using cross-validation.
data(df_pa)
lm_fit = fit_lm_metamodel(df = df_pa,
                 y_var = "inc_qaly",
                 x_vars = c("p_pfsd", "p_pdd")
                 )

validate_metamodel(model = lm_fit,
                 method = "cross_validation",
                 folds = 5
                 )

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