# Plot Simulated Probability Density Function and Target PDF by Distribution Name or Function for Continuous or Count Variables 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. ```r 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 ## Returns A `ggplot2-package` object. ## Examples ```r # 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) ``` ## References Please see the references for `plot_simtheory`. ## See Also `calc_theory`, `ggplot`

plot_simpdf_theory function