plot_simpdf_theory: Plot Simulated Probability Density Function and Target PDF by...

In SimCorrMix: Simulation of Correlated Data with Multiple Variable Types Including Continuous and Count Mixture Distributions

Description

This plots the PDF of simulated continuous or count (regular or zero-inflated, Poisson or Negative Binomial) data and overlays the target PDF (if overlay = TRUE), which is specified by distribution name (plus up to 4 parameters) or PDF function fx (plus support bounds). If a continuous target distribution is provided (cont_var = TRUE), the simulated data y is scaled and then transformed (i.e. y = sigma * scale(y) + mu) so that it has the same mean (mu) and variance (sigma^2) as the target distribution. The PDF's of continuous variables are shown as lines (using geom_density and ggplot2::geom_line). It works for valid or invalid power method PDF's. The PMF's of count variables are shown as vertical bar graphs (using ggplot2::geom_col). The function returns a ggplot2-package object so the user can save it or modify it as necessary. The graph parameters (i.e. title, sim_color, sim_lty, sim_size, target_color, target_lty, target_size, legend.position, legend.justification, legend.text.size, title.text.size, axis.text.size, and axis.title.size) are inputs to the ggplot2-package functions so information about valid inputs can be obtained from that package's documentation.

Usage

plot_simpdf_theory(sim_y, title = "Simulated Probability Density Function",
  ylower = NULL, yupper = NULL, sim_color = "dark blue", sim_lty = 1,
  sim_size = 1, col_width = 0.5, overlay = TRUE, cont_var = TRUE,
  target_color = "dark green", target_lty = 2, target_size = 1,
  Dist = c("Benini", "Beta", "Beta-Normal", "Birnbaum-Saunders", "Chisq",
  "Dagum", "Exponential", "Exp-Geometric", "Exp-Logarithmic", "Exp-Poisson",
  "F", "Fisk", "Frechet", "Gamma", "Gaussian", "Gompertz", "Gumbel",
  "Kumaraswamy", "Laplace", "Lindley", "Logistic", "Loggamma", "Lognormal",
  "Lomax", "Makeham", "Maxwell", "Nakagami", "Paralogistic", "Pareto", "Perks",
  "Rayleigh", "Rice", "Singh-Maddala", "Skewnormal", "t", "Topp-Leone",
  "Triangular", "Uniform", "Weibull", "Poisson", "Negative_Binomial"),
  params = NULL, fx = NULL, lower = NULL, upper = NULL,
  legend.position = c(0.975, 0.9), legend.justification = c(1, 1),
  legend.text.size = 10, title.text.size = 15, axis.text.size = 10,
  axis.title.size = 13)

Arguments

sim_y	a vector of simulated data
title	the title for the graph (default = "Simulated Probability Density Function")
ylower	the lower y value to use in the plot (default = NULL, uses minimum simulated y value) on the x-axis
yupper	the upper y value (default = NULL, uses maximum simulated y value) on the x-axis
sim_color	the line color for the simulated PDF (or column fill color in the case of Dist = "Poisson" or "Negative_Binomial")
sim_lty	the line type for the simulated PDF (default = 1, solid line)
sim_size	the line width for the simulated PDF
col_width	width of column for simulated/target PMF of count variables (default = 0.5)
overlay	if TRUE (default), the target distribution is also plotted given either a distribution name (and parameters) or PDF function fx (with bounds = ylower, yupper)
cont_var	TRUE (default) for continuous variables, FALSE for count variables
target_color	the line color for the target PDF (or column fill color in the case of Dist = "Poisson" or "Negative_Binomial")
target_lty	the line type for the target PDF (default = 2, dashed line)
target_size	the line width for the target PDF
Dist	name of the distribution. The possible values are: "Benini", "Beta", "Beta-Normal", "Birnbaum-Saunders", "Chisq", "Exponential", "Exp-Geometric", "Exp-Logarithmic", "Exp-Poisson", "F", "Fisk", "Frechet", "Gamma", "Gaussian", "Gompertz", "Gumbel", "Kumaraswamy", "Laplace", "Lindley", "Logistic", "Loggamma", "Lognormal", "Lomax", "Makeham", "Maxwell", "Nakagami", "Paralogistic", "Pareto", "Perks", "Rayleigh", "Rice", "Singh-Maddala", "Skewnormal", "t", "Topp-Leone", "Triangular", "Uniform", "Weibull", "Poisson", and "Negative_Binomial". Please refer to the documentation for each package (either stats-package, VGAM-package, or triangle) for information on appropriate parameter inputs.
params	a vector of parameters (up to 4) for the desired distribution (keep NULL if fx supplied instead); for Poisson variables, must be lambda (mean) and the probability of a structural zero (use 0 for regular Poisson variables); for Negative Binomial variables, must be size, mean and the probability of a structural zero (use 0 for regular NB variables)
fx	a PDF input as a function of x only, i.e. fx = function(x) 0.5 * (x - 1)^2; must return a scalar (keep NULL if Dist supplied instead)
lower	the lower support bound for fx
upper	the upper support bound for fx
legend.position	the position of the legend
legend.justification	the justification of the legend
legend.text.size	the size of the legend labels
title.text.size	the size of the plot title
axis.text.size	the size of the axes text (tick labels)
axis.title.size	the size of the axes titles

Value

A ggplot2-package object.

References

Please see the references for plot_simtheory.

See Also

calc_theory, ggplot

Examples

# Using normal mixture variable from contmixvar1 example
Nmix <- contmixvar1(n = 1000, "Polynomial", means = 0, vars = 1,
  mix_pis = c(0.4, 0.6), mix_mus = c(-2, 2), mix_sigmas = c(1, 1),
  mix_skews = c(0, 0), mix_skurts = c(0, 0), mix_fifths = c(0, 0),
  mix_sixths = c(0, 0))
plot_simpdf_theory(Nmix$Y_mix[, 1],
  title = "Mixture of Normal Distributions",
  fx = function(x) 0.4 * dnorm(x, -2, 1) + 0.6 * dnorm(x, 2, 1),
  lower = -5, upper = 5)
## Not run: 
# Mixture of Beta(6, 3), Beta(4, 1.5), and Beta(10, 20)
Stcum1 <- calc_theory("Beta", c(6, 3))
Stcum2 <- calc_theory("Beta", c(4, 1.5))
Stcum3 <- calc_theory("Beta", c(10, 20))
mix_pis <- c(0.5, 0.2, 0.3)
mix_mus <- c(Stcum1[1], Stcum2[1], Stcum3[1])
mix_sigmas <- c(Stcum1[2], Stcum2[2], Stcum3[2])
mix_skews <- c(Stcum1[3], Stcum2[3], Stcum3[3])
mix_skurts <- c(Stcum1[4], Stcum2[4], Stcum3[4])
mix_fifths <- c(Stcum1[5], Stcum2[5], Stcum3[5])
mix_sixths <- c(Stcum1[6], Stcum2[6], Stcum3[6])
mix_Six <- list(seq(0.01, 10, 0.01), c(0.01, 0.02, 0.03),
  seq(0.01, 10, 0.01))
Bstcum <- calc_mixmoments(mix_pis, mix_mus, mix_sigmas, mix_skews,
  mix_skurts, mix_fifths, mix_sixths)
Bmix <- contmixvar1(n = 10000, "Polynomial", Bstcum[1], Bstcum[2]^2,
  mix_pis, mix_mus, mix_sigmas, mix_skews, mix_skurts, mix_fifths,
  mix_sixths, mix_Six)
plot_simpdf_theory(Bmix$Y_mix[, 1], title = "Mixture of Beta Distributions",
  fx = function(x) mix_pis[1] * dbeta(x, 6, 3) + mix_pis[2] *
    dbeta(x, 4, 1.5) + mix_pis[3] * dbeta(x, 10, 20), lower = 0, upper = 1)

## End(Not run)

SimCorrMix documentation built on May 2, 2019, 1:24 p.m.