R/dk.R

In BNPdensity: Ferguson-Klass Type Algorithm for Posterior Normalized Random Measures

#' Kernel density function
#'
#' This functions evaluates a density at a certain data point.  There are 4
#' density options (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)). All densities are parameterized in terms of mean and standard
#' deviation.
#'
#' For internal use.
#'
#' @keywords internal
#'
dk <-
  function(x, 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) {
      a <- ifelse(is.null(mu), 0, mu)
      b <- ifelse(is.null(sigma), 1, sigma)
      dk <- dnorm(x, mean = a, sd = b)
    }
    else if (distr == 2) {
      a <- ifelse(is.null(mu), 1, mu^2 / sigma^2)
      b <- ifelse(is.null(sigma), 1, mu / sigma^2)
      dk <- dgamma(x, 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
        ))
      }
      dk <- dbeta(x, 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))
      dk <- exp(-abs(x - a) / b) / (2 * b)
    }
    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)))
      dk <- dlnorm(x, meanlog = a, sdlog = b)
    }
    else if (distr == 6) {
      dk <- dhalfcauchy(x, location = ifelse(is.null(mu), 0,
        mu
      ), scale = ifelse(is.null(sigma), 1, sigma))
    }
    else if (distr == 7) {
      dk <- dhalfnorm(x,
        mean = ifelse(is.null(mu), 0, mu),
        sd = ifelse(is.null(sigma), 1, sigma)
      )
    }
    else if (distr == 8) {
      dk <- dhalft(x, df = 10, mean = ifelse(is.null(mu), 0,
        mu
      ), sd = ifelse(is.null(sigma), 1, sigma))
    }
    else if (distr == 9) {
      dk <- dunif(x, min = ifelse(is.null(mu), 0, mu), max = ifelse(is.null(sigma),
        1, sigma
      ))
    }
    else if (distr == 10) {
      dk <- dtnorm(x, mean = ifelse(is.null(mu), 0, mu), sd = ifelse(is.null(sigma),
        1, sigma
      ), lower = 0.1)
    }
    else {
      stop(msg)
    }
    return(dk)
  }