RoBMA (version 3.5.1)

weighted_normal: 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"
)

Value

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

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.

See Also

Normal

Examples

# generate random samples from weighted normal distribution
samples <- rwnorm(n = 10000, mean = 0.15, sd = 0.10, 
                  steps = c(0.025, 0.5), omega = c(0.1, 0.5, 1), 
                  type = "one.sided")
# hist(samples)

# compute density at specific values
density_vals <- dwnorm(x = c(-2, 0, 2), mean = 0, sd = 1, 
                       steps = c(0.05), omega = c(0.5, 1))

# compute cumulative probabilities  
prob_vals <- pwnorm(q = c(-1, 0, 1), mean = 0, sd = 1,
                    steps = c(0.05), omega = c(0.5, 1))