knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "README-" )
The MAUT decision models are defined with aid of utility functions $u_1,\ldots,u_n$ which are evaluated over indexes $x_1,\ldots,x_n$ and those utilities are aggregated considering additional weights $w_1,\ldots,w_n$, the whole final utility is given by the sum
[u(x_1,\ldots,x_n) = \sum_{1\leq i \leq n} w_i u_i\ ( x_i )]
With mau you can build and test decision models based in Multi Attribute Utility Theory (MAUT). The utilities of any level of the decision model can be easily evaluated.
To install mau you can proceed in the following way making use of the devtools library
library( devtools ) install_github( "pedroguarderas/mau" )
The utility functions for a MAUT model could be defined in a practical format when those are are piecewise defined like constant risk averse functions, in such case it is only necessary to define the parameters of the function for each part of the domain of definition. This is because, the constant risk averse functions are of the form $u(x) = a \cdot x + b$ or $u(x) = a \cdot e^{b \cdot x} + c$.
File format for the piecewise definition of utilities, is specified as follows.
Header
Function name
min1 max1 a1 b1 c1
min2 max2 a2 b2 c2
min3 max3 a3 b3 c3
...
Function name
min1 max1 a1 b1 c1
min2 max2 a2 b2 c2
min3 max3 a3 b3 c3
...
If $c_i$ is $0$ then the utility is linear, otherwise is an exponential function. For example:
library( mau ) file <- system.file("extdata", "utilities.txt", package = "mau" ) lines <- readLines( file ) for ( i in 1:length( lines ) ) { cat( lines[i], '\n' ) }
In the sources below is developed a complete example of a decision model, the package mau is
employed to load utilities defined in the file utilities.txt
, provided in the package itself,
automatically the script with utilities is built and saved in the local working directory, after that
with eval_utilities
every function is evaluated over the columns of the index table, the names
for utilities were previously standardized with stand_string
. With another file tree.csv
the
decision tree associated to the MAUT model is built and every weight and relative weight assigned
with the make_decision_tree
function, in addition the whole model with utilities of every criteria
is obtained with compute_model
. The simulation of constrained weights is made with
sim_const_weights
, the result could be employed for a sensitivity test of the decision model
under a variation of weights.
# Loading packages -------------------------------------------------------------------------------- library( mau ) library( data.table ) library( igraph ) library( ggplot2 ) # Table of indexes -------------------------------------------------------------------------------- index <- data.table( cod = paste( 'A', 1:10, sep = '' ), i1 = c( 0.34, 1, 1, 1, 1, 0.2, 0.7, 0.5, 0.11, 0.8 ), i2 = c( 0.5, 0.5, 1, 0.5, 0.3, 0.1, 0.4, 0.13, 1, 0.74 ), i3 = c( 0.5, 1.0, 0.75, 0.25, 0.1, 0.38, 0.57, 0.97, 0.3, 0.76 ), i4 = c( 0, 0.26, 0.67, 0.74, 0.84, 0.85, 0.74, 0.65, 0.37, 0.92 ) ) # Loading utilities ------------------------------------------------------------------------------- file <- system.file("extdata", "utilities.txt", package = "mau" ) script <- 'utilities.R' lines <- 17 skip <- 2 encoding <- 'utf-8' functions <- read_utilities( file, script, lines, skip, encoding ) source( 'utilities.R' ) # Index positions --------------------------------------------------------------------------------- columns <- c( 2, 3, 4, 5 ) # Function names functions <- sapply( c( 'Project', 'Self implementation', 'External and local relations', 'Scope of capabilities' ), FUN = stand_string ) names( functions ) <- NULL # Evaluation of utilities ------------------------------------------------------------------------- utilities <- eval_utilities( index, columns, functions ) # Tree creation ----------------------------------------------------------------------------------- file <- system.file("extdata", "tree.csv", package = "mau" ) tree.data <- read_tree( file, skip = 0, nrow = 8 ) tree <- make_decision_tree( tree.data ) # Compute the decision model ---------------------------------------------------------------------- weights <- tree.data[ !is.na( weight ) ]$weight model <- compute_model( tree, utilities, weights ) # Weights simulation ------------------------------------------------------------------------------ n <- 200 alpha <- c( 0.2, 0.5, 0.1, 0.2 ) constraints <- list( list( c(1,2), 0.7 ), list( c(3,4), 0.3 ) ) S <- sim_const_weights( n, utilities, alpha, constraints ) plot.S <- plot_sim_weight( S$simulation, title = 'Simulations', xlab = 'ID', ylab = 'Utility' ) plot( plot.S )
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