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#' Kernel density sampling function
#'
#' This function simulates from a density. There are 4 density options (1 =
#' Gaussian, 2 = Gamma, 3 = Beta, 4 = double exponential, 5 = lognormal). All
#' densities are parameterized in terms of mean and standard deviation.
#'
#' For internal use.
#'
#' @keywords internal
#' @examples
#'
#' ## The function is currently defined as
#' function(n, distr = NULL, mu = NULL, sigma = NULL) {
#' if (is.null(distr)) {
#' stop("Argument \"distr\" should be defined numeric with possible values 1,2,3,4 or 5")
#' }
#' else if (distr == 1) {
#' a <- ifelse(is.null(mu), 0, mu)
#' b <- ifelse(is.null(sigma), 1, sigma)
#' rk <- rnorm(n, mean = a, sd = b)
#' }
#' else if (distr == 2) {
#' a <- ifelse(is.null(mu), 0, mu)
#' b <- ifelse(is.null(sigma), 1 / sqrt(2), sigma / sqrt(2))
#' rk <- a + b * sample(c(-1, +1), size = n, replace = TRUE) *
#' rexp(n)
#' }
#' else if (distr == 3) {
#' a <- ifelse(is.null(mu), exp(1 / 2), log(mu / sqrt(1 + (sigma / mu)^2)))
#' b <- ifelse(is.null(sigma), exp(1) * (exp(1) - 1), sqrt(log(1 +
#' (sigma / y)^2)))
#' rk <- rlnorm(n, meanlog = a, sdlog = b)
#' }
#' else if (distr == 4) {
#' a <- ifelse(is.null(mu), 1, mu^2 / sigma^2)
#' b <- ifelse(is.null(sigma), 1, mu / sigma^2)
#' rk <- rgamma(n, shape = a, rate = b)
#' }
#' else if (distr == 5) {
#' a <- ifelse(is.null(mu), 0.5, (1 - mu) * (mu / sigma)^2 -
#' mu)
#' b <- ifelse(is.null(sigma), 1 / sqrt(12), (mu * (1 - mu) / sigma^2 -
#' 1) * (1 - mu))
#' if (any(c(a, b) <= 0)) {
#' stop(paste(
#' "\nNegative Beta parameters:\n a =", a,
#' ";\t b =", b
#' ))
#' }
#' rk <- rbeta(n, shape1 = a, shape2 = b)
#' }
#' else {
#' stop("Argument \"distr\" should be defined numeric with possible values 1,2,3,4 or 5")
#' }
#' return(rk)
#' }
rk <-
function(n, distr = NULL, mu = NULL, sigma = NULL) {
msg <- "Argument \"distr\" should be defined numeric with possible values 1 (normal), 2 (gamma), 3 (beta), 4 (exponential), 5 (lognormal), 6 (half-Cauchy), 7 (half-normal), 8 (half-student), 9 (uniform) and 10 (truncated normal)"
if (is.null(distr)) {
stop(msg)
}
else if (distr == 1) {
rk <- rnorm(n, mean = mu, sd = sigma)
}
else if (distr == 2) {
a <- ifelse(is.null(mu), 1, mu^2 / sigma^2)
b <- ifelse(is.null(sigma), 1, mu / sigma^2)
rk <- rgamma(n, shape = a, rate = b)
}
else if (distr == 3) {
a <- ifelse(is.null(mu), 0.5, (1 - mu) * (mu / sigma)^2 -
mu)
b <- ifelse(is.null(sigma), 1 / sqrt(12), (mu * (1 - mu) / sigma^2 -
1) * (1 - mu))
if (any(c(a, b) <= 0)) {
stop(paste(
"\nNegative Beta parameters:\n a =", a,
";\t b =", b
))
}
rk <- rbeta(n, shape1 = a, shape2 = b)
}
else if (distr == 4) {
a <- ifelse(is.null(mu), 0, mu)
b <- ifelse(is.null(sigma), 1 / sqrt(2), sigma / sqrt(2))
rk <- a + b * sample(c(-1, +1), size = n, replace = TRUE) *
rexp(n)
}
else if (distr == 5) {
a <- ifelse(is.null(mu), exp(1 / 2), log(mu / sqrt(1 + (sigma / mu)^2)))
b <- ifelse(is.null(sigma), exp(1) * (exp(1) - 1), sqrt(log(1 +
(sigma / mu)^2)))
rk <- rlnorm(n, meanlog = a, sdlog = b)
}
else if (distr == 6) {
rk <- rhalfcauchy(n, location = ifelse(is.null(mu), 0,
mu
), scale = ifelse(is.null(sigma), 1, sigma))
}
else if (distr == 7) {
rk <- rhalfnorm(n,
mean = ifelse(is.null(mu), 0, mu),
sd = ifelse(is.null(sigma), 1, sigma)
)
}
else if (distr == 8) {
rk <- rhalft(n, df = 10, mean = ifelse(is.null(mu), 0,
mu
), sd = ifelse(is.null(sigma), 1, sigma))
}
else if (distr == 9) {
rk <- runif(n, min = ifelse(is.null(mu), 0, mu), max = ifelse(is.null(sigma),
1, sigma
))
}
else if (distr == 10) {
rk <- rtnorm(n, mean = ifelse(is.null(mu), 0, mu), sd = ifelse(is.null(sigma),
1, sigma
), lower = 0.1)
}
else {
stop(msg)
}
return(rk)
}
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