utilityFunctionTools (version 0.1.0)

compute_function: Computes a continuous and smooth utility function from the given utility points

Description

Computes a continuous and smooth utility function from the given utility points

Usage

compute_function(
  x,
  y,
  ids = NULL,
  mode = 1,
  penalty_order = 4,
  lambda_max = 10000,
  current_lambda = 1,
  ndx = 20,
  deg = 6,
  verbose = 0
)

Arguments

x

a matrix or dataframe containing the certainty equivalents (x-values of utility points) for a given participant in each use case.

y

can be a vector or a matrix representing the corresponding utility values (y-values of utility points).

ids

a list containing the IDs of the participants. If not given, a list with IDs from 1 to n_observations will be created.

mode

an integer between 0, 1, 2 representing the three possible modes: multiple imputation, optimal classification or 'weak' classification. Default is optimal classification (1).

penalty_order

highest dimension (i.e., derivative) to penalize. Must be lower than deg.

lambda_max

maximum lambda used for computing the optimal lambda. It is used only in multiple imputation (mode = 0) and optimal (mode = 1). The default value is 10000.

current_lambda

lambda considered in the current iteration. Only used in multiple imputation (mode = 0) to create the combinations and as actual lambda value in 'weak' classification mode (mode = 2). The default value is 1.

ndx

number of intervals to partition the distance between the lowest and highest x-values of the utility points.

deg

degree of the B-spline basis. Determines the degree of the function to be estimated. If deg = 2, the estimated utility function will consist of quadratic functions.

verbose

shows some information while the program is running.

Value

A smooth and continuous utility function.

Examples

# NOT RUN {
x <- matrix(c(24.60938,34.76074,78.75,81.86035,128.5156, 
              7.109375,80.4248,113.75,115.083,135.0781, 
              3.828125,7.211914,8.75,124.1064,131.7969, 
              1.640625,2.084961,8.75,36.94824,98.98438), nrow = 4, ncol = 5, byrow = TRUE)
y <- c(0.25, 0.375, 0.5, 0.625, 0.75)
compute_function(x, y, verbose = 1)
# }