Package 'dexisensitivity'

Dual Order AOV on a Decision Tree

Description

Conducts an Analysis of Variance (AOV) on a provided decision tree, computing both first-order and second-order effects.

Usage

aov_tree(tree)

Arguments

tree	Tree object to be analyzed.

Value

A list containing results for both first-order and second-order AOV analyses.

Examples

tree <- dexisensitivity::masc2
subtree <- create_sub_tree(tree, "Dimension sociale")
AOV_out <- aov_tree(subtree)

---

Compare Scenarios Using a Radial Plot

Description

Visualizes the comparison of node values across multiple scenarios with a radial plot. This representation provides an intuitive view of how different scenarios compare for the selected nodes.

Usage

compare_scenarios(tree, scenarios_results, nodes_list)

Arguments

tree	A Tree object.
scenarios_results	List of numeric vectors with scenario evaluation results.
nodes_list	List of character node names to be compared in the plot.

Value

No return value, called for side effects

See Also

evaluate_scenarios

Examples

tree <- dexisensitivity::masc2
option <- create_options(tree, num_options=3, seed = 42)
scenarios <- evaluate_scenarios(tree, option)
compare_scenarios(tree, scenarios,
 c("Dimension economique", "Dimension sociale", "Dimension environnementale"))

---

Create a List of Synoptic Plots Based on Different Options

Description

Produces a list of synoptic plots, one for each set of options specified in the columns of the 'options' matrix. Each plot visualizes nodes of a decision tree based on different evaluation options.

Usage

create_list_synoptique(tree, options, depth = NA)

Arguments

tree	Tree structure object representing the decision tree.
options	matrix where each column denotes a distinct set of options for evaluating the decision tree.
depth	Optional numeric specifying the depth of the tree. If provided, a sub-tree is produced up to the specified depth. By default, it is set to NA.

Value

A list of ggplot objects. Each item in the list is a synoptic plot corresponding to a column from the 'options' matrix.

See Also

create_synoptique

Examples

tree <- dexisensitivity::masc2
options <- create_options(tree, num_options=3, seed = 42)
create_list_synoptique(tree, options)

---

Generate Random Options Matrix for a Given Tree

Description

Creates random options based on the RangeScale and Probability attributes of tree nodes. This function is useful for generating random scenarios for simulations or analyses.

Usage

create_options(tree, num_options = 1, seed = NULL)

Arguments

tree	A Tree object.
num_options	A single numeric value specifying the number of options to generate. Defaults to 1.
seed	A single numeric value for random number generation seed. Default is NULL, which means no seed will be set.

Details

The function creates a matrix of random options based on the attributes of tree nodes. Specifically, it leverages the RangeScale and Probability attributes of tree nodes to generate random options.

Value

A matrix where rows represent tree leaves and columns represent sampled options.

Examples

tree <- dexisensitivity::masc2
option <- create_options(tree, num_options=3, seed = 42)

---

Create a Subtree Starting from a Specific Node

Description

Constructs a subtree from a specified Node within a given Tree. This subtree encompasses all descendant attributes of the Node.

Usage

create_sub_tree(tree, node_name, avoid_repetition = FALSE)

Arguments

tree	a Tree object from which the subtree is derived.
node_name	A character indicating the name of the starting Node for the subtree.
avoid_repetition	A logical indicating whether to avoid node repetitions. Default is FALSE.

Value

A new Tree object representing the subtree.

See Also

Relevant functions and classes that provide more context or might be of interest:

• Tree-class: For an in-depth understanding of the Tree class.

• Node-class: To get more details about the structure of a Node.

Examples

tree <- dexisensitivity::masc2
subtree <- create_sub_tree(masc2, masc2@Nodes[[2]]@Name)
subtree

# Equivalent to :
tree <- dexisensitivity::masc2
subtree <- create_sub_tree(masc2, "Dimension economique")
subtree

---

Create a Synoptic Plot from a Given Tree Structure

Description

Generates a synoptic plot based on a specified tree structure, providing visualization of its structure and evaluation options.

Usage

create_synoptique(tree, option, depth = NA)

Arguments

tree	Tree structure object.
option	list containing evaluation options for the tree.
depth	numeric representing the depth of the tree; if provided, produces a sub-tree.

Value

A ggplot object representing the synoptic plot.

Examples

tree <- dexisensitivity::masc2
option <- create_options(tree, num_options=1, seed = 42)
create_synoptique(tree, option)

---

Tree Construction from DEXi's XML Output

Description

Constructs a Tree objects using the XML representation from a DEXi model.

Usage

create_tree(main_tree, which_root = "all", correct = FALSE)

Arguments

main_tree	A character or an object of class XML. Either the path to the .dxi file containing the tree (character) or the XML object that represents the primary tree structure and should encompass the DEXi decision model.
which_root	an integer to indicate which tree to create among several in a .dxi or "all" (default) to create them all.
correct	A logical. If TRUE, use some correction function to avoid special character as " ' ".

Details

Iterates over every root attribute in the provided XML to create tree structures that depict the hierarchical decision model.

Value

A list of Tree objects, each corresponding to a unique root attribute in the DEXi XML layout. Each tree details the root attribute, nodes, leaves, depth, and other associated data regarding the decision model's configuration.

Examples

# With a Path
dxi_masc2_path <- system.file("extdata", "arborescence_MASC_2_0.dxi", package = "dexisensitivity")
tree <- create_tree(dxi_masc2_path)
tree

# With an XML
dxi_masc2_path <- system.file("extdata", "arborescence_MASC_2_0.dxi", package = "dexisensitivity")
masc2_xml <- XML::xmlDeserializeHook(dxi_masc2_path)
tree <- create_tree(masc2_xml)
tree

---

Generic Describe Function for Objects

Description

Provides a comprehensive description of an object.

Usage

describe(object)

Arguments

object

The object to be described.

Value

No return value, called for the side effect of a description of the object (description of each node in case of a Tree object)

See Also

• Tree-class: For the Tree class definition.

• print.Tree: For printing a Tree object.

• show.Tree: For showing a Tree object.

---

Describe Method for Tree Class Objects

Description

Outputs a detailed structure of a Tree object, with each node and its properties displayed distinctly.

Usage

## S4 method for signature 'Tree'
describe(object)

Arguments

object

The Tree object to be described.

Details

Each node within the Tree is presented separately. If the Tree lacks nodes, an error "Tree without any node!" is raised.

Value

This function is primarily executed for its side effect of presenting nodes from the Tree object and does not provide a meaningful return value.

See Also

• Tree-class: For the Tree class definition.

• print.Tree: For printing a Tree object.

• show.Tree: For showing a Tree object.

---

DEXiFruits_v1 model

Description

A Tree class object created by dexisensitivity::create_tree function, using the Multi-Attribute Assessment model of the sustainability of cropping systems in arboriculture (DEXiFruits) in its first and generic version.

Usage

dexifruits_v1

Format

An object of class Tree of length 1.

Source

https://means.inrae.fr/outils-emc/outils-dexi/dexifruits

See Also

Tree-class

---

Execution Time Estimation for Factorial Simulations

Description

Estimates the expected execution time for a given number of factorial simulations using the duration taken to run a sample subset of simulations.

Usage

estimate_aov_time(tree, test_runs = 50)

Arguments

tree	Tree object upon which the simulations are performed.
test_runs	numeric denoting the number of simulations utilized for estimating the execution time. Default value is set to 50.

Value

A character string, with estimated execution time (in minutes)

Examples

tree <- dexisensitivity::masc2
subtree <- create_sub_tree(tree, "Dimension sociale")
estimate_aov_time(subtree, test_runs = 50)

---

Estimates Execution Time for Monte Carlo Simulations

Description

Estimates the time required to run a set number of Monte Carlo simulations based on the time taken to run a smaller test set.

Usage

estimate_mc_time(tree, num_runs, num_test = 50)

Arguments

tree	A Tree object for simulations.
num_runs	A Numeric, total number of simulations desired.
num_test	A Numeric, number of test simulations for time estimation (default is 50).

Value

A character string with estimated execution time (in minutes)

Examples

tree <- dexisensitivity::masc2
estimate_mc_time(tree, num_runs = 1000, num_test = 50)

---

Evaluate Node Values in a Tree

Description

Calculates the values of the tree nodes using a bottom-up approach. The function starts by assigning values to the leaves, based on the provided option, and then aggregates these values up the tree to determine each node's value. This ensures each node's value considers the values of its child nodes.

Usage

evaluate_scenario(tree, option)

Arguments

tree	A Tree object.
option	A matrix representation of a scenario, providing values for the tree's leaves. Each column in the matrix corresponds to a tree leaf, and the rows provide different values for the scenario analysis.

Details

Begins by assigning values to the leaves of the tree based on the provided option. If the tree structure indicates leaf-aggregated scenarios, these values are aggregated accordingly. The function then continues to aggregate values up the tree, considering the tree's structure, to determine each node's value.

It's essential for the input option matrix to have columns that correspond to the leaves of the tree and for the tree object to have the appropriate attributes set.

Value

A named numeric vector representing the evaluated values for all nodes, progressing from the leaves to the root.

Examples

tree <- dexisensitivity::masc2
option <- create_options(tree, num_options=1, seed = 42)
scenario <- evaluate_scenario(tree, option)
scenario

---

Evaluate Multiple Scenarios for a Given Tree

Description

Evaluates multiple scenarios simultaneously using the evaluate_scenario function. Each scenario is represented as a column in the options_matrix.

Usage

evaluate_scenarios(tree, options_matrix)

Arguments

tree	A Tree object for evaluation.
options_matrix	A matrix where each column represents a scenario.

Value

A list of numeric vectors with evaluation results for each scenario.

Examples

tree <- dexisensitivity::masc2
option <- create_options(tree, num_options=3, seed = 42)
scenarios <- evaluate_scenarios(tree, option)
scenarios

---

Compute Sensitivity Index for Decision Tree

Description

Computes the Sensitivity Index (SI) for each attribute within the specified tree.

Usage

get_sensitivity_index(tree, avoid_repetition = FALSE)

Arguments

tree	A Tree object on which the analysis is to be performed.
avoid_repetition	logical indicating if repeated nodes should be disregarded. Defaults to FALSE.

Value

A vector containing the Sensitivity Indices for each attribute in the tree.

---

Load Options Table from a File

Description

Retrieves a matrix of options saved in a file. This matrix can then be used for further analysis or processing.

Usage

load_options(file_name)

Arguments

file_name

A character string specifying the file from which to load the options matrix.

Value

A matrix representing the loaded options.

Examples

tree <- dexisensitivity::masc2
option <- create_options(tree, num_options=3, seed = 42)
save_options(option,paste0(tempdir(),"\\save_options.tab"))
loaded_option <- load_options(paste0(tempdir(),"\\save_options.tab"))
file.remove(paste0(tempdir(),"\\save_options.tab"))

---

MASC 2.0 model

Description

A Tree class object created by dexisensitivity::create_tree function, using the Multi-Attribute Assessment of the Sustainability of Cropping systems (MASC) model in it's 2.0 version.

Usage

masc2

Format

An object of class Tree of length 1.

Source

https://means.inrae.fr/outils-emc/masc/telecharger-masc

See Also

Tree-class

---

Monte Carlo Simulation on a Decision Tree

Description

Conducts a Monte Carlo simulation over a provided decision tree for a specified number of runs. Optionally, the function can save the random options selected for the analysis in a .csv file named "MC options.csv".

Usage

monte_carlo(tree, num_runs, write_to_file = NULL)

Arguments

tree	Tree object to be used in the simulation.
num_runs	numeric indicating the number of Monte Carlo simulations to be executed.
write_to_file	character Name of the file created to save the Monte Carlo's results. If NULL, don't write any file. Default is NULL

Value

A matrix containing the results of the Monte Carlo simulation.

Examples

tree <- dexisensitivity::masc2
MC <- monte_carlo(tree, 100)

---

Node Class Definition

Description

An S4 class to represent a node in a tree structure.

Details

A structured representation of a node, which encompasses various attributes such as name, depth, and relationships with other nodes in the tree structure. The class Node is primarily used in the creation, manipulation, and display of nodes within tree structures.

Value

An object of class Node.

Slots

Id

numeric - Unique sequential identifier for the node.

Name

character - Name of the node.

IsLeaf

logical - Flag indicating if the node is a leaf.

IsLeafAndAggregated

logical - Flag indicating if the node is both a leaf and an aggregated node.

Children

character - List of the node's children names.

Sisters

character - List of the node's sisters names.

Mother

character - Name of the node's mother.

Aggregation

matrix - Aggregation table if the node is aggregated.

Probability

numeric - Estimated weight for aggregation.

Depth

numeric - Depth of the node in the tree.

Twin

numeric - ID of the other leaves for nodes with multiple leaves.

ConditionalProbabilityList

list - List storing conditional probabilities.

RangeScale

numeric - Range scale for the node.

ScaleLabel

character - Labels corresponding to different scales.

NodePath

character - Path from the root to the leaf for the node.

See Also

• print.Node: For printing a Node object.

---

OFAT Sensitivity Analysis

Description

Executes a One-Factor-At-A-Time (OFAT) sensitivity assessment by undertaking simulations while varying individual factors. During each simulation, all factors are maintained constant save for one.

Usage

oat(tree, option)

Arguments

tree	Tree object designated for analysis.
option	Initial configuration for Tree parameters.

Value

A matrix depicting the assessment outcomes for every attribute in the Tree, corresponding to diverse parameter alterations.

Examples

tree <- dexisensitivity::masc2
optionOAT <- create_options(tree, 1)
results <- oat(tree, optionOAT)

---

Show Sensitivity Index (SI)

Description

Visualizes the Sensitivity Index (SI) of the leaves of a specified decision tree using a bar plot.

Usage

plot_sensitivity_index(tree, sensitivity_indices)

Arguments

tree	Tree object for analysis.
sensitivity_indices	A numeric vector representing the Sensitivity Indices for each leaf in the decision tree.

Value

No return value; a bar plot is displayed.

Examples

tree <- dexisensitivity::masc2
sensitivity_index <- si_dexi(tree)
plot_sensitivity_index(tree, sensitivity_index[[1]])

---

Print Method for Node Class Object

Description

Provides a comprehensive display of a Node's properties, such as its name, ID, depth, path, and more. This method is intended for better readability and understanding of a Node's structure and relationships.

Usage

## S4 method for signature 'Node'
print(x, ...)

Arguments

x	An object of class Node that you want to print.
...	Additional arguments to be passed to the underlying print function, though they might not have any effect in this custom print method.

Value

This function is invoked for its side effect of printing. It does not return anything.

See Also

• Node-class: For more details on the Node class.

---

Print Method for Tree Class Objects

Description

Custom print method for objects of class Tree. This method prints out basic information about the tree, such as the root name, number of attributes, number of leaves, depth, and details about specific nodes.

Usage

## S4 method for signature 'Tree'
print(x, ...)

Arguments

x	An object of class Tree that you want to print.
...	Additional arguments to be passed to the underlying print function, though they might not have any effect in this custom print method.

Value

This function is invoked for its side effect of printing. It does not return anything.

See Also

• Tree-class: For the Tree class definition.

• show.Tree: For showing a Tree object.

• describe.Tree: For describing a Tree object.

---

Save Options Table

Description

Stores a matrix of options into a file, primarily for archival or subsequent analysis.

Usage

save_options(options_table, file_name)

Arguments

options_table	A matrix containing option values.
file_name	A character string specifying the desired file name for saving the options.

Value

No return value, called for side effects

Examples

tree <- dexisensitivity::masc2
option <- create_options(tree, num_options=3, seed = 42)
save_options(option,paste0(tempdir(),"\\save_options.tab"))
file.remove(paste0(tempdir(),"\\save_options.tab"))

---

Save Evaluation Results of Scenarios to a File

Description

Stores the results of scenario evaluations into a file for later analysis.

Usage

save_scenarios(scenarios_results, file_name)

Arguments

scenarios_results	List of numeric vectors with scenario evaluation results.
file_name	A character, to name the file for saving the scenario results.

Value

No return value, called for side effects

Examples

tree <- dexisensitivity::masc2
option <- create_options(tree, num_options=3, seed = 42)
scenarios <- evaluate_scenarios(tree, option)
save_scenarios(scenarios,paste0(tempdir(),"\\save_scenarios.tab"))
file.remove(paste0(tempdir(),"\\save_scenarios.tab"))

---

Visualization of Monte Carlo Simulation Results

Description

Displays the outcomes of the Monte Carlo simulation for a specific Node as a bar chart, showcasing the frequency of each result. Furthermore, the lengths of the bars are saved in a .csv file titled "MC bar lengths.csv".

Usage

show_mc_results(node, mc_results, num_runs, save = NULL)

Arguments

node	Node object representing the node of interest in the simulation.
mc_results	matrix containing the Monte Carlo simulation results.
num_runs	numeric indicating the number of Monte Carlo simulations that were executed.
save	character indicating where to save the graphic. By default, save is NULL and don't save the graphic.

Value

A vector depicting the data used in the bar chart.

Examples

tree <- dexisensitivity::masc2
MC <- monte_carlo(tree, 100)
show_mc_results(tree@Nodes[[2]], MC, 100)

---

Visualize OFAT Sensitivity Analysis Outcomes

Description

Renders a visualization representing the One-Factor-At-A-Time (OFAT) sensitivity analysis results.

Usage

show_oat_results(node_name, results, tree)

Arguments

node_name	character specifying the name of the node intended for visualization.
results	Matrix of evaluation results, typically derived from ofat_sensitivity_analysis.
tree	A Tree object that was employed in the OFAT analysis.

Value

This function does not return a value; instead, it exhibits a plot.

Examples

tree <- dexisensitivity::masc2
optionOAT <- create_options(tree, 1)
results <- oat(tree, optionOAT)
show_oat_results("Dimension economique", results, tree)

---

Plot a Bar Chart for a Single Scenario

Description

Visualizes the attribute values of a provided scenario. For each attribute, a bar is plotted, and the maximum possible value is highlighted.

Usage

show_scenario(scenario, tree, label_y = TRUE, modify_par = TRUE)

Arguments

scenario	Scenario data to visualize.
tree	Associated Tree object providing attribute details.
label_y	Logical value indicating whether to label the Y-axis (default is TRUE).
modify_par	Logical value to decide if graphical parameters should be modified (default is TRUE).

Value

No return value, called for side effects

See Also

evaluate_scenario

Examples

tree <- dexisensitivity::masc2
option <- create_options(tree, num_options=1, seed = 42)
scenario <- evaluate_scenario(tree, option)
show_scenario(as.matrix(scenario), tree = tree, label_y = TRUE)

---

Show Method for Tree Class Objects

Description

Custom show method for objects of class Tree. It presents a structured representation of the tree using a specific format.

Usage

## S4 method for signature 'Tree'
show(object)

Arguments

object

An object of class Tree to be shown.

Details

Each node of the tree is displayed with its depth, name, and associated twin attributes. Node presentation differs based on its type and position within the tree structure:

• Prefix "Z : " is used for the first node of the tree.

• Prefix "X : " denotes a leaf node.

• Prefix "Y : " indicates a non-leaf node.

If a tree has no attributes, it displays "*** Tree without attributes ***".

Value

This function is invoked for its side effect of showing a structured display of the tree. It does not return anything explicitly.

See Also

• print.Tree: For printing a Tree object.

• Tree-class: For the Tree class definition.

---

Compute Sensitivity Index (SI) for Decision Tree

Description

Computes the Sensitivity Index (SI) for each node within a specified decision tree.

Usage

si_dexi(
  tree,
  file_name = "SI_out.csv",
  is_file = FALSE,
  avoid_repetition = FALSE
)

Arguments

tree	A Tree object on which the analysis will be executed.
file_name	character designating the filename to store the SI. Default is "SI_out.csv".
is_file	logical determining whether to save the SI results to a file. Defaults to FALSE
avoid_repetition	logical indicating if repeated nodes should be disregarded. Defaults to FALSE.

Value

A list containing the Sensitivity Indices for every node in the tree.

Examples

tree <- dexisensitivity::masc2
sensitivity_index <- si_dexi(tree)
sensitivity_index

---

Tree Class Definition

Description

An S4 class to represent a tree structure.

Details

A structured representation of a tree, which includes several slots to store attributes, leaves, nodes, path, and additional information related to the tree structure. The class Tree is primarily used in the creation, manipulation, and display of tree structures.

Value

An object of class Tree.

Slots

NumberOfAttributes

numeric - Number of attributes in the tree.

NumberOfLeaves

numeric - Number of leaves in the tree.

Depth

numeric - Maximum depth of the tree.

Attributes

character - Names of attributes in the tree.

Leaves

character - Names of leaves in the tree.

Aggregated

character - Names of aggregated nodes in the tree.

IsMultiple

logical - Flag indicating if multiple leaves are present in the tree.

Multiple

data.frame - List of multiple leaves and their count.

IsLeafAggregated

logical - Flag indicating if leaf-aggregated nodes are present in the tree.

LeafAggregated

character - Names of leaf-aggregated nodes in the tree.

Paths

list - Paths from the root to the leaves.

Nodes

list - Nodes present in the tree.

EvaluationOrder

numeric - Evaluation order for LeafAggregated nodes.

RootName

character - Name of the root node.

See Also

• print.Tree: For printing a Tree object.

• show.Tree: For showing a Tree object.

• describe.Tree: For describing a Tree object.

---

Visualize AOV Outcomes

Description

Renders the outcomes of an Analysis of Variance (AOV) through bar plots, allowing a comprehensive display of both total sums and specific effects.

Usage

visualize_aov(
  aov_results,
  show_main = TRUE,
  num_plots = 8,
  horizontal = TRUE,
  axis_label_style = 1,
  ...
)

Arguments

aov_results	A list containing the AOV results.
show_main	logical indicating if the main effects and total sums of squares should be included in the visualization. Default is TRUE.
num_plots	numeric specifying the count of effects to be showcased in the display.
horizontal	logical determining if the bar plots should be oriented horizontally. Default is TRUE.
axis_label_style	numeric designating the label styling for the plot's axes. Default is 1.
...	Additional arguments affecting the bar plot's aesthetics.

Value

A data.frame containing proportions derived from the sum of squares.

Examples

tree <- dexisensitivity::masc2
subtree <- create_sub_tree(tree, "Dimension sociale")
AOV_out <- aov_tree(subtree)
visualize_aov(AOV_out)

---