## ---- echo = FALSE------------------------------------------------------- knitr::opts_chunk$set(fig.width = 6, fig.height = 4.5) ## ---- warning = FALSE, message = FALSE----------------------------------- library("SimMultiCorrData") library("printr") stcums <- calc_theory(Dist = "Exponential", params = 0.5) ## ---- warning = FALSE, message = FALSE----------------------------------- H_exp <- nonnormvar1("Polynomial", means = stcums[1], vars = stcums[2]^2, skews = stcums[3], skurts = stcums[4], fifths = stcums[5], sixths = stcums[6], n = 10000, seed = 1234) ## ------------------------------------------------------------------------ as.matrix(H_exp$constants, nrow = 1, ncol = 6, byrow = TRUE) ## ------------------------------------------------------------------------ as.matrix(round(H_exp$summary_targetcont[, c("Distribution", "mean", "sd", "skew", "skurtosis", "fifth", "sixth")], 5), nrow = 1, ncol = 7, byrow = TRUE) ## ------------------------------------------------------------------------ as.matrix(round(H_exp$summary_continuous[, c("Distribution", "mean", "sd", "skew", "skurtosis", "fifth", "sixth")], 5), nrow = 1, ncol = 7, byrow = TRUE) ## ------------------------------------------------------------------------ H_exp$valid.pdf ## ------------------------------------------------------------------------ y_star <- qexp(1 - 0.05, rate = 0.5) # note that rate = 1/mean y_star ## ------------------------------------------------------------------------ f_exp <- function(z, c, y) { return(2 * (c[1] + c[2] * z + c[3] * z^2 + c[4] * z^3 + c[5] * z^4 + c[6] * z^5) + 2 - y) } z_prime <- uniroot(f_exp, interval = c(-1e06, 1e06), c = as.numeric(H_exp$constants), y = y_star)$root z_prime ## ------------------------------------------------------------------------ 1 - pnorm(z_prime) ## ---- warning = FALSE, message = FALSE----------------------------------- plot_sim_pdf_theory(sim_y = H_exp$continuous_variable[, 1], Dist = "Exponential", params = 0.5) ## ---- warning = FALSE, message = FALSE----------------------------------- plot_sim_cdf(sim_y = H_exp$continuous_variable[, 1], calc_cprob = TRUE, delta = y_star) ## ---- warning = FALSE, message = FALSE----------------------------------- as.matrix(t(stats_pdf(c = H_exp$constants[1, ], method = "Polynomial", alpha = 0.025, mu = stcums[1], sigma = stcums[2])))