| plot_simpdf_theory {SimCorrMix} | R Documentation |
Plot Simulated Probability Density Function and Target PDF by Distribution Name or Function for Continuous or Count Variables
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
|
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
|
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", |
params |
a vector of parameters (up to 4) for the desired distribution (keep NULL if |
fx |
a PDF input as a function of x only, i.e. |
lower |
the lower support bound for |
upper |
the upper support bound for |
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
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)