Sensitivity Analysis.

Usage

sensitivity(task_dists, cor_mat = NULL)

Arguments

task_dists: A list of lists describing each task distribution.
cor_mat: The correlation matrix for the tasks (Optional).

Value

The function returns a vector of sensitivity results with respect to each task.

Examples

# Set the task distributions for a toy project.
task_dists <- list(
  list(type = "normal", mean = 10, sd = 2),  # Task A: Normal distribution
  list(type = "triangular", a = 5, b = 15, c = 10),  # Task B: Triangular distribution
  list(type = "uniform", min = 8, max = 12)  # Task C: Uniform distribution
)

# Set the correlation matrix between the tasks.
cor_mat <- matrix(c(
  1, 0.5, 0.3,
 0.5, 1, 0.4,
  0.3, 0.4, 1
), nrow = 3, byrow = TRUE)

# Calculate the sensitivity of each task and print the results
sensitivity_results <- sensitivity(task_dists, cor_mat)
cat("Sensitivity of the variance in total cost with respect to the variance in each task cost:\n")
#> Sensitivity of the variance in total cost with respect to the variance in each task cost:
print(sensitivity_results)
#> [1] 2.6 2.8 2.4

# Build a vertical barchart and display the results.
data <- data.frame(
  Tasks = c('A', 'B', 'C'),
  Sensitivity = sensitivity_results
)
barplot(height=data$Sensitivity, names=data$Tasks, col='skyblue',
        horiz=TRUE, xlab = 'Sensitivity', ylab = 'Tasks')