plot_sim_pdf_theory: Plot Simulated Probability Density Function and Target PDF by...
In SimMultiCorrData: Simulation of Correlated Data with Multiple Variable Types

Description Usage Arguments Value References See Also Examples

This plots the pdf of simulated continuous or count 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. If the variable is Negative Binomial, the parameters must be size and success probability (not mu). The function returns a ggplot2-package object so the user can modify as necessary. The graph parameters (i.e. title, power_color, target_color, target_lty) are ggplot2-package parameters. It works for valid or invalid power method pdfs.

plot_sim_pdf_theory(sim_y, title = "Simulated Probability Density Function",
  ylower = NULL, yupper = NULL, power_color = "dark blue",
  overlay = TRUE, cont_var = TRUE, target_color = "dark green",
  target_lty = 2, 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)

`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)
`yupper`	the upper y value (default = NULL, uses maximum simulated y value)
`power_color`	the line color for the simulated variable
`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
`target_lty`	the line type for the target pdf (default = 2, dashed line)
`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)
`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

A ggplot2-package object.

Please see the references for plot_cdf.

Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2009.

calc_theory, ggplot2-package, geom_path, geom_density

## Not run: 
# Logistic Distribution: mean = 0, variance = 1
seed = 1234

# Find standardized cumulants
stcum <- calc_theory(Dist = "Logistic", params = c(0, 1))

# Simulate without the sixth cumulant correction
# (invalid power method pdf)
Logvar1 <- nonnormvar1(method = "Polynomial", means = 0, vars = 1,
                       skews = stcum[3], skurts = stcum[4],
                       fifths = stcum[5], sixths = stcum[6],
                       n = 10000, seed = seed)

# Plot pdfs of simulated variable (invalid) and theoretical distribution
plot_sim_pdf_theory(sim_y = Logvar1$continuous_variable,
                    title = "Invalid Logistic Simulated PDF",
                    overlay = TRUE, Dist = "Logistic", params = c(0, 1))

# Simulate with the sixth cumulant correction
# (valid power method pdf)
Logvar2 <- nonnormvar1(method = "Polynomial", means = 0, vars = 1,
                       skews = stcum[3], skurts = stcum[4],
                       fifths = stcum[5], sixths = stcum[6],
                       Six = seq(1.5, 2, 0.05), n = 10000, seed = seed)

# Plot pdfs of simulated variable (invalid) and theoretical distribution
plot_sim_pdf_theory(sim_y = Logvar2$continuous_variable,
                    title = "Valid Logistic Simulated PDF",
                    overlay = TRUE, Dist = "Logistic", params = c(0, 1))

# Simulate 2 Negative Binomial distributions and correlation 0.3
# using Method 1
NBvars <- rcorrvar(k_nb = 2, size = c(10, 15), prob = c(0.4, 0.3),
                  rho = matrix(c(1, 0.3, 0.3, 1), 2, 2), seed = seed)

# Plot pdfs of 1st simulated variable and theoretical distribution
plot_sim_pdf_theory(sim_y = NBvars$Neg_Bin_variable[, 1], overlay = TRUE,
                    cont_var = FALSE, Dist = "Negative_Binomial",
                    params = c(10, 0.4))


## End(Not run)