BayesTools (version 0.2.18)

weightfunctions: Weight functions

Description

Marginal density, marginal distribution function, marginal quantile function and random generation for weight functions.

Usage

mdone.sided(x, alpha = NULL, alpha1 = NULL, alpha2 = NULL, log = FALSE)
mdtwo.sided(x, alpha, log = FALSE)
mdone.sided_fixed(x, omega, log = FALSE)
mdtwo.sided_fixed(x, omega, log = FALSE)
rone.sided(n, alpha = NULL, alpha1 = NULL, alpha2 = NULL)
rtwo.sided(n, alpha)
rone.sided_fixed(n, omega)
rtwo.sided_fixed(n, omega)
mpone.sided(
  q,
  alpha = NULL,
  alpha1 = NULL,
  alpha2 = NULL,
  lower.tail = TRUE,
  log.p = FALSE
)
mptwo.sided(q, alpha, lower.tail = TRUE, log.p = FALSE)
mpone.sided_fixed(q, omega, lower.tail = TRUE, log.p = FALSE)
mptwo.sided_fixed(q, omega, lower.tail = TRUE, log.p = FALSE)
mqone.sided(
  p,
  alpha = NULL,
  alpha1 = NULL,
  alpha2 = NULL,
  lower.tail = TRUE,
  log.p = FALSE
)
mqtwo.sided(p, alpha, lower.tail = TRUE, log.p = FALSE)
mqone.sided_fixed(p, omega, lower.tail = TRUE, log.p = FALSE)
mqtwo.sided_fixed(p, omega, lower.tail = TRUE, log.p = FALSE)

Value

mdone.sided, mdtwo.sided, mdone.sided_fixed, and mdtwo.sided_fixed give the marginal density, mpone.sided, mptwo.sided, mpone.sided_fixed, and mptwo.sided_fixed give the marginal distribution function, mqone.sided, mqtwo.sided, mqone.sided_fixed, and mqtwo.sided_fixed give the marginal quantile function, and rone.sided, rtwo.sided, rone.sided_fixed, and rtwo.sided_fixed generate random deviates.

Arguments

x, q

vector or matrix of quantiles.

alpha

vector or matrix with concentration parameters for the Dirichlet distribution for a monotonic one.sided or a two.sided weight function.

alpha1

vector or matrix with concentration parameters for the Dirichlet distribution for the expected direction of non-monotonic one.sided of weight function.

alpha2

vector or matrix with concentration parameters for the Dirichlet distribution for the unexpected direction of non-monotonic one.sided of weight function.

log, log.p

logical; if TRUE, probabilities p are given as log(p).

omega

vector or matrix of fixed probabilities for a one.sided or a two.sided weight function.

n

number of observations.

lower.tail

logical; if TRUE (default), probabilities are \(P[X \le x]\), otherwise, \(P[X \ge x]\).

p

vector of probabilities.

Examples

# draw samples from a two-sided weight function
rtwo.sided(10, alpha = c(1, 1))

# draw samples from a monotone one-sided weight function
rone.sided(10, alpha = c(1, 1, 1))

# draw samples from a non-monotone one-sided weight function
rone.sided(10, alpha1 = c(1, 1), alpha2 = c(1, 1))