Plot fcmconfr — plot.fcmconfr • fcmconfr

This plots the output of fcmconfr() using ggplot. Set shiny = TRUE to load plot in a Shiny app and toggle on/off results from different analyses.

Usage

# S3 method for class 'fcmconfr'
plot(
  x,
  include = "all",
  shiny = FALSE,
  filter_limit = 0.001,
  xlim = NA,
  coord_flip = FALSE,
  text_font_size = NA,
  mc_avg_and_CIs_color = "blue",
  mc_inferences_color = "blue",
  mc_inferences_size = 1,
  mc_inferences_alpha = 0.3,
  mc_inferences_shape = 3,
  ind_inferences_color = "black",
  ind_inferences_size = 1,
  ind_inferences_alpha = 1,
  ind_inferences_shape = 16,
  agg_inferences_color = "red",
  agg_inferences_size = 1,
  agg_inferences_alpha = 1,
  agg_inferences_shape = 17,
  mc_avg_and_CIs_linewidth = 0.1,
  ind_ivfn_and_tfn_linewidth = 0.1,
  agg_ivfn_and_tfn_linewidth = 0.6,
  ...
)

Arguments

x: A direct output of the fcmconfr function
include: [character()]
The concepts to include in the plot. By default, include = 'all' which does not exclude any concepts. Set to a vector of concept names to identify the only concepts to include in the plot.
shiny: [logical(1)]
If TRUE, launch plot in a Shiny app to toggle on/off results from different analyses.
filter_limit: [double(1)]
Only nodes with inferences above the filter_limit across any analysis will be plotted. This removes nodes with mostly 0-valued inferences indicating they were not impacted in the simulation.
xlim: [double(1)]
The x-axis plot limits. xlim = NA lets ggplot determine the x-axis limits. xlim = c(lower_limit, upper_limit) for manual input limits. See ?ggplot2::xlim for additional info.
coord_flip: [logical(1)]
Swap x- and y-axes (i.e. rotate plot). See ?ggplot2::coord_flip for additional info.
text_font_size: [double(1)]
The font size of axis labels. text_font_size = NA lets ggplot determine the axis label font size.
mc_avg_and_CIs_color: [character(1)]
Color of the crossbar (lines) indicating the avg inferences of empirical FCMs generated via Monte Carlo sampling and the confidence intervals about those averages.
mc_inferences_color: [character(1)]
Color of the points representing inferences of empirical FCMs generated via Monte Carlo sampling.
mc_inferences_size: [double(1) - Positive]
Size of the points for inferences of Monte Carlo FCMs. Must be greater than 0.
mc_inferences_alpha: [double(1) - Positive (between 0 and 1)]
Transparency of the points representing inferences of empirical FCMs generated via Monte Carlo sampling. Range from 0 to 1 (0: Transparent to 1: Opaque).
mc_inferences_shape: [integer(1) or character(1)]
Point shapes of the points representing inferences of empirical FCMs generated via Monte Carlo sampling. Accepts PCH point values and character strings.
ind_inferences_color: [character(1)]
Color of the points representing inferences of individual FCMs.
ind_inferences_size: [double(1) - Positive]
Size of the points for inferences of individual FCMs. Must be greater than 0.
ind_inferences_alpha: [double(1) - Positive (between 0 and 1)]
Transparency of the points representing inferences of individual FCMs. Range from 0 to 1 (0: Transparent to 1: Opaque).
ind_inferences_shape: [integer(1) or character(1)]
Point shapes of the points representing inferences of individual FCMs. Accepts PCH point values and character strings. Ignored for IVFN FCMs.
agg_inferences_color: [character(1)]
Color of the points representing inferences of the aggregate FCM
agg_inferences_size: [double(1) - Positive]
Size of the points for inferences of aggregate FCM. Must be greater than 0.
agg_inferences_alpha: [double(1) - Positive (between 0 and 1)]
Transparency of the points representing inferences of the aggregate FCM. Range from 0 to 1 (0: Transparent to 1: Opaque).
agg_inferences_shape: [integer(1) or character(1)]
Point shapes of the points representing inferences of the aggregate FCM. Accepts PCH point values and character strings. Ignored for IVFN FCMs.
mc_avg_and_CIs_linewidth: [double(1) - Positive]
Linewidth of lines representing the average (and confidence bounds the average) of the Monte Carlo FCMs inferences
ind_ivfn_and_tfn_linewidth: [double(1) - Positive]
Linewidth of lines representing inferences for analyses of individual IVFN- and TFN- FCMs.
agg_ivfn_and_tfn_linewidth: [double(1) - Positive]
Linewidth of lines representing inferences for analyses of aggregate IVFN- and TFN- FCMs
...: Additional inputs

Value

A plot of an fcmconfr object's results

Details

Generates a generic plot visualizing fcmconfr results.

Examples


# Example using TFN FCMs fcmconfr
tfn_example_fcmconfr <- fcmconfr(
  adj_matrices = sample_fcms$simple_fcms$tfn_fcms,
  # adj_matrices = group_tfn_fcms,
  # Aggregation and Monte Carlo Sampling
  agg_function = 'mean',
  num_mc_fcms = 100,
  # Simulation
  initial_state_vector = c(1, 1, 1, 1, 1, 1, 1),
  clamping_vector = c(1, 0, 0, 0, 0, 0, 0),
  activation = 'rescale',
  squashing = 'sigmoid',
  lambda = 1,
  point_of_inference = "final",
  max_iter = 1000,
  min_error = 1e-05,
  # Inference Estimation (bootstrap)
  ci_centering_function = "mean",
  confidence_interval = 0.95,
  # Runtime Options
  show_progress = TRUE,
  parallel = FALSE,
  # Additional Options
  run_agg_calcs = TRUE,
  run_mc_calcs = TRUE,
  run_ci_calcs = TRUE,
  include_zeroes_in_sampling = TRUE,
  include_sims_in_output = TRUE
)
#> [1] Simulating Input FCMs
#> 
#> [1] Running Simulations
#> [1] Sampling from column vectors
#> Sampling from column vectors[1] Constructing monte carlo fcms from samples
#> Constructing monte carlo fcms from samples
#> [1] Running Simulations
#> [1] Performing bootstrap simulations
#> [1] Done


# Plot Defaults
plot(tfn_example_fcmconfr,
     interactive = FALSE, # Set to TRUE to open shiny app
     # Plot Formatting Parameters
     filter_limit = 1e-4,
     xlim = c(-1, 1),
     coord_flip = FALSE,
     text_font_size = NA,
     # Plot Aesthetic Parameters
     mc_avg_and_CIs_color = "blue",
     mc_inferences_color = "blue",
     mc_inferences_alpha = 0.3,
     mc_inferences_shape = 3,
     ind_inferences_color = "black",
     ind_inferences_alpha = 1,
     ind_inferences_shape = 16,
     agg_inferences_color = "red",
     agg_inferences_alpha = 1,
     agg_inferences_shape = 17,
     ind_ivfn_and_tfn_linewidth = 0.1,
     agg_ivfn_and_tfn_linewidth = 0.6
)
#> Warning: ! Warning: Additional Inputs given as ... are ignored
#> ~~~~~ Ignoring additional inputs: interactive


# Different from Plot Defaults
plot(tfn_example_fcmconfr,
     interactive = FALSE, # Set to TRUE to open shiny app
     # Plot Formatting Parameters
     filter_limit = 1e-4,
     xlim = c(-0.6, 0.6),
     coord_flip = FALSE,
     # Plot Aesthetic Parameters
     mc_avg_and_CIs_color = "darkblue",
     mc_inferences_color = "darkblue",
     mc_inferences_shape = 3,
     ind_inferences_color = "maroon",
     ind_inferences_shape = 16,
     agg_inferences_color = "grey",
     agg_inferences_shape = 17,
     ind_ivfn_and_tfn_linewidth = 0.1,
     agg_ivfn_and_tfn_linewidth = 0.6
)
#> Warning: ! Warning: Additional Inputs given as ... are ignored
#> ~~~~~ Ignoring additional inputs: interactive



# Plot Defaults w/ Shiny App
# plot(tfn_example_fcmconfr,
#      shiny = TRUE, # Set to TRUE to open shiny app
#      # Plot Formatting Parameters
#      filter_limit = 1e-4,
#      coord_flip = FALSE,
#      text_font_size = 12,
#      # Plot Aesthetic Parameters
#      mc_avg_and_CIs_color = "blue",
#      mc_inferences_color = "blue",
#      mc_inferences_shape = 3,
#      ind_inferences_color = "black",
#      ind_inferences_shape = 16,
#      agg_inferences_color = "red",
#      agg_inferences_shape = 17,
#      ind_ivfn_and_tfn_linewidth = 0.1,
#      agg_ivfn_and_tfn_linewidth = 0.6
# )