pacheck (version 0.2.2)

fit_lasso_metamodel: Fit LASSO metamodel

Description

This function fits a lasso metamodel using the glmnet package.

Usage

fit_lasso_metamodel(
  df,
  y_var = NULL,
  x_vars = NULL,
  seed_num = 1,
  standardise = FALSE,
  tune_plot = TRUE,
  x_poly_2 = NULL,
  x_poly_3 = NULL,
  x_exp = NULL,
  x_log = NULL,
  x_inter = NULL
)

Value

A list contaning the following elements:

• An object of the fitted metamodel,

• The coefficient of the fitted metamodel,

• information on the data used to fit the metamodel and its form.

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.

standardise

logical. Determine whether the parameter of the linear regression should be standardised. Default is FALSE.

tune_plot

logical. Determine whether the plot of the results of tuning the lambda should be shown.

x_poly_2

character. character or a vector for characters. Name of the input variable in the dataframe. These variables will be exponentiated by factor 2.

x_poly_3

character. character or a vector for characters. Name of the input variable in the dataframe. These variables will be exponentiated by factor 3.

x_exp

character. character or a vector for characters. Name of the input variable in the dataframe. The exponential of these variables will be included in the metamodel.

x_log

character. character or a vector for characters. Name of the input variable in the dataframe. The logarithm of these variables will be included in the metamodel.

x_inter

character. character or a vector for characters. Name of the input variables in the dataframe. This vector contains the variables for which the interaction should be considered. The interaction terms of two consecutive variables will be considered in the linear model; hence, the length of this vector should be even.

Examples

#Fit lasso metamodel with two variables using the probabilistic data
data(df_pa)
fit_lasso_metamodel(df = df_pa,
                 y_var = "inc_qaly",
                 x_vars = c("p_pfsd", "p_pdd"),
                 tune_plot = TRUE
                 )