Elementary prior related functions

Density (pdf / lpdf), distribution function (cdf / ccdf), quantile function (quant), random generation (rng), mean, standard deviation (sd), and marginal variants of the functions (mpdf, mlpf, mcdf, mccdf, mquant) for prior distributions.

# S3 method for class 'prior'
rng(x, n, ...)

# S3 method for class 'prior'
cdf(x, q, ...)

# S3 method for class 'prior'
ccdf(x, q, ...)

# S3 method for class 'prior'
lpdf(x, y, ...)

# S3 method for class 'prior'
pdf(x, y, ...)

# S3 method for class 'prior'
quant(x, p, ...)

# S3 method for class 'prior'
mcdf(x, q, ...)

# S3 method for class 'prior'
mccdf(x, q, ...)

# S3 method for class 'prior'
mlpdf(x, y, ...)

# S3 method for class 'prior'
mpdf(x, y, ...)

# S3 method for class 'prior'
mquant(x, p, ...)

Arguments

x

prior distribution

n

number of observations

...

unused arguments

q

vector or matrix of quantiles

y

vector of observations

p

vector of probabilities

Value

pdf (mpdf) and lpdf (mlpdf) give the (marginal) density and the log of (marginal) density, cdf (mcdf) and ccdf (mccdf) give the (marginal) distribution and the complement of (marginal) distribution function, quant (mquant) give the (marginal) quantile function, and rng generates random deviates for an object of class 'prior'.

Examples

# create a standard normal prior distribution
p1 <- prior(distribution = "normal", parameters = list(mean = 1, sd = 1))

# generate a random sample from the prior
rng(p1, 10)
#>  [1]  2.8692906  1.4251004  0.7613529  2.0584830  1.8864227  0.3807570
#>  [7]  3.2061025  0.7449730 -0.4244947  0.8556004

# compute cumulative density function
cdf(p1, 0)
#> [1] 0.1586553

# obtain quantile
quant(p1, .5)
#> [1] 1

# compute probability density
pdf(p1, c(0, 1, 2))
#> [1] 0.2419707 0.3989423 0.2419707