plot_sim_theory: Plot Simulated Data and Target Distribution Data by Name or...
In SimMultiCorrData: Simulation of Correlated Data with Multiple Variable Types

Description Usage Arguments Value References See Also Examples

This plots simulated continuous or count data and overlays data (if overlay = TRUE) generated from the target distribution, which is specified by name (plus up to 4 parameters) or pdf function fx (plus support bounds). Due to the integration involved in evaluating the cdf using fx, only continuous fx may be supplied. Both are plotted as histograms. If a continuous target distribution is specified (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). It 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_theory(sim_y, title = "Simulated Data Values", ylower = NULL,
  yupper = NULL, power_color = "dark blue", overlay = TRUE,
  cont_var = TRUE, target_color = "dark green", nbins = 100,
  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, seed = 1234,
  sub = 1000, 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 Data Values")
`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 histogram fill color for the simulated variable (default = "dark blue")
`overlay`	if TRUE (default), the target distribution is also plotted given either a distribution name (and parameters) or pdf function fx (with support bounds = lower, upper)
`cont_var`	TRUE (default) for continuous variables, FALSE for count variables
`target_color`	the histogram fill color for the target distribution (default = "dark green")
`nbins`	the number of bins to use when creating the histograms (default = 100)
`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 a supplied fx, else keep NULL (note: if an error is thrown from `uniroot`, try a slightly higher lower bound; i.e., 0.0001 instead of 0)
`upper`	the upper support bound for a supplied fx, else keep NULL (note: if an error is thrown from `uniroot`, try a lower upper bound; i.e., 100000 instead of Inf)
`seed`	the seed value for random number generation (default = 1234)
`sub`	the number of subdivisions to use in the integration to calculate the cdf from fx; if no result, try increasing sub (requires longer computation time; default = 1000)
`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_histogram

## 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 simulated variable (invalid) and data from theoretical distribution
plot_sim_theory(sim_y = Logvar1$continuous_variable,
                title = "Invalid Logistic Simulated Data Values",
                overlay = TRUE, Dist = "Logistic", params = c(0, 1),
                seed = seed)

# 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 simulated variable (valid) and data from theoretical distribution
plot_sim_theory(sim_y = Logvar2$continuous_variable,
                title = "Valid Logistic Simulated Data Values",
                overlay = TRUE, Dist = "Logistic", params = c(0, 1),
                seed = seed)

# 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_theory(sim_y = NBvars$Neg_Bin_variable[, 1], overlay = TRUE,
                cont_var = FALSE, Dist = "Negative_Binomial",
                params = c(10, 0.4))


## End(Not run)