inst/doc/variable_types.R In SimCorrMix: Simulation of Correlated Data with Multiple Variable Types Including Continuous and Count Mixture Distributions

## ----setup, echo = FALSE-------------------------------------------------
knitr::opts_chunkset(echo = TRUE, message = FALSE, warning = FALSE, fig.align = 'center', fig.width = 4.65, fig.height = 4) ## ---- include=FALSE------------------------------------------------------ library("bookdown") ## ---- fig.show='hold', fig.cap = "Contaminated normal distribution Y", include=FALSE, eval=FALSE---- # #Setting\pi = 0.3$and$\sigma^2 = 1$produces the following distribution for$n = 10000$: # library("SimCorrMix") # Nstcum <- calc_mixmoments(mix_pis = c(0.3, 0.7), mix_mus = c(0, 0), # mix_sigmas = c(3, 1), mix_skews = c(0, 0), mix_skurts = c(0, 0), # mix_fifths = c(0, 0), mix_sixths = c(0, 0)) # Nmix <- contmixvar1(n = 10000, "Polynomial", Nstcum[1], Nstcum[2]^2, # mix_pis = c(0.3, 0.7), mix_mus = c(0, 0), mix_sigmas = c(3, 1), # mix_skews = c(0, 0), mix_skurts = c(0, 0), mix_fifths = c(0, 0), # mix_sixths = c(0, 0)) # plot_simpdf_theory(sim_y = Nmix$Y_mix[, 1], overlay = FALSE,
#    legend.position = "none", title = "PDF of Y")
#  qqnorm(Nmix$Y_mix[, 1], main = "Q-Q plot for Y") # qqline(Nmix$Y_mix[, 1], col = 2)
#  #The Q-Q plot shows that the assumption of homogeneous variance for the error distribution would not be valid in this situation


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SimCorrMix documentation built on May 2, 2019, 1:24 p.m.