plot_distributions {decisionSupport}

Probability distribution plots for various types of Monte Carlo simulation results

Description

Several plotting options for distribution outputs

Usage

plot_distributions(
  mcSimulation_object,
  vars,
  method = "smooth_simple_overlay",
  bins = 150,
  binwidth = NULL,
  old_names = NULL,
  new_names = NULL,
  colors = NULL,
  outlier_shape = ".",
  x_axis_name = "Outcome distribution",
  y_axis_name = NULL,
  base_size = 11,
  ...
)

Arguments

mcSimulation_object

is an object of Monte Carlo simulation outputs from the mcSimulation function

vars

is a vector containing variable names from the mcSimulation_object. This can also be a single variable name

method

is the plot option to be used in link{ggplot2}: "smooth_simple_overlay" creates a density plot with geom_density, "hist_simple_overlay" creates a histogram with geom_histogram, "boxplot" creates a boxplot with geom_boxplot and "boxplot_density" creates a density plot with a boxplot using geom_density and geom_boxplot

bins

are the number of bins to use for the geom_histogram. Default number of bins is 150

binwidth

is the width of the bins to use for the geom_histogram. Default number is 1000. When both bins and binwidth are defined, the later overrides bins

old_names

are the variable names from the MC simulation outputs that refer to the distribution values. This should be a vector of character strings. This is set to NULL with the assumption that the existing names for variables are preferred

new_names

are the variable names to replace the MC simulation outputs that refer to the distribution values. This should be a vector of character strings. This is set to NULL with the assumption that the existing names for variables are preferred

colors

is the color palette to be used for the fill of distribution shapes and boxplots. The default is c("#009999", "#0000FF", "#56B4E9", "#009E73","#F0E442", "#0072B2", "#D55E00", "#CC79A7") assuming a maximum of eight variables to be compared

outlier_shape

is the optional shape to replace the outliers in the boxplot. To show no outliers use NA. See shape for shape options

x_axis_name

is the name (character string) to be passed to the x-axis title. Default is "Outcome distribution" and allows allow the user to add a customized axis title

y_axis_name

is the name (character string) to be passed to the y-axis title. Default is NULL to allow the user to add a customized axis title. If a name is not provided the title will be "Number of points in bin" for the hist_simple_overlay method and "Density estimate" for all other plot options

base_size

is the base text size to be used for the plot. The default is 11, this is the ggplot2::ggtheme default

...

accepts arguments to be passed to ggplot2::ggtheme

Value

This function returns a plot of classes 'gg', and 'ggplot'. This allows the user to continue editing some features of the plots through the syntax '+'.

Author(s)

Eduardo Fernandez ([e-mail])

Cory Whitney ([e-mail])

References

Do, Hoa, Eike Luedeling, and Cory Whitney. “Decision Analysis of Agroforestry Options Reveals Adoption Risks for Resource-Poor Farmers.” Agronomy for Sustainable Development 40, no. 3 (June 2020): 20. doi:10.1007/s13593-020-00624-5. Lanzanova, Denis, Cory Whitney, Keith Shepherd, and Eike Luedeling. “Improving Development Efficiency through Decision Analysis: Reservoir Protection in Burkina Faso.” Environmental Modelling & Software 115 (May 1, 2019): 164–75. doi:10.1016/j.envsoft.2019.01.016. Ruett, Marius, Cory Whitney, and Eike Luedeling. “Model-Based Evaluation of Management Options in Ornamental Plant Nurseries.” Journal of Cleaner Production 271 (June 2020): 122653. doi:10.1016/j.jclepro.2020.122653.

Examples

##############################################################
# Example 1 (Creating the estimate from the command line):
#############################################################
# Create the estimate object:

variable = c("revenue", "costs")
distribution = c("norm", "norm")
lower = c(10000,  5000)
upper = c(100000, 50000)
costBenefitEstimate <- as.estimate(variable, distribution, lower, upper)

# (a) Define the model function without name for the return value:

profit1 <- function(x) {
  x$revenue - x$costs
  return(list(Revenues = x$revenue,
              Costs = x$costs))
}

# Perform the Monte Carlo simulation:

predictionProfit1 <- mcSimulation(estimate = costBenefitEstimate,
                                  model_function = profit1,
                                  numberOfModelRuns = 10000,
                                  functionSyntax = "data.frameNames")


# Plot the distributions

plot_distributions(mcSimulation_object = predictionProfit1, vars = c("Revenues", "Costs"),
         method = "smooth_simple_overlay")

plot_distributions(mcSimulation_object = predictionProfit1, vars = c("Revenues", "Costs"),
         method = "hist_simple_overlay", bins = 30)

plot_distributions(mcSimulation_object = predictionProfit1, vars = c("Costs"),
         method = "hist_simple_overlay", binwidth = 1000)

plot_distributions(mcSimulation_object = predictionProfit1, vars = c("Revenues", "Costs"),
         method = "boxplot_density", outlier_shape = 3)