weighted_normal: Weighted normal distribution

In RoBMA: Robust Bayesian Meta-Analyses

Weighted normal distribution

Description

Density, distribution function, quantile function and random generation for the weighted normal distribution with mean, standard deviation sd, steps steps (or critical values) crit_x), and weights omega.

Usage

dwnorm(
  x,
  mean,
  sd,
  steps = if (!is.null(crit_x)) NULL,
  omega,
  crit_x = if (!is.null(steps)) NULL,
  type = "two.sided",
  log = FALSE
)

pwnorm(
  q,
  mean,
  sd,
  steps = if (!is.null(crit_x)) NULL,
  omega,
  crit_x = if (!is.null(steps)) NULL,
  type = "two.sided",
  lower.tail = TRUE,
  log.p = FALSE
)

qwnorm(
  p,
  mean,
  sd,
  steps = if (!is.null(crit_x)) NULL,
  omega,
  crit_x = if (!is.null(steps)) NULL,
  type = "two.sided",
  lower.tail = TRUE,
  log.p = FALSE
)

rwnorm(
  n,
  mean,
  sd,
  steps = if (!is.null(crit_x)) NULL,
  omega,
  crit_x = if (!is.null(steps)) NULL,
  type = "two.sided"
)

Arguments

x, q	vector of quantiles.
mean	mean
sd	standard deviation.
steps	vector of steps for the weight function.
omega	vector of weights defining the probability of observing a t-statistics between each of the two steps.
crit_x	vector of critical values defining steps (if steps are not supplied).
type	type of weight function (defaults to "two.sided").
log, log.p	logical; if TRUE, probabilities p are given as log(p).
lower.tail	logical; if TRUE (default), probabilities are P[X \le x], otherwise, P[X \ge x].
p	vector of probabilities.
n	number of observations. If length(n) > 1, the length is taken to be the number required.

Details

The mean, sd, steps, omega can be supplied as a vectors (mean, sd) or matrices (steps, omega) with length / number of rows equal to x/q/ p. Otherwise, they are recycled to the length of the result.

Value

dwnorm gives the density, dwnorm gives the distribution function, qwnorm gives the quantile function, and rwnorm generates random deviates.

See Also

Normal

RoBMA documentation built on April 12, 2025, 1:55 a.m.