joker (version 0.14.2)

Laplace: Laplace Distribution

Description

The Laplace distribution, also known as the double exponential distribution, is a continuous probability distribution that is often used to model data with sharp peaks and heavy tails. It is parameterized by a location parameter \(\mu\) and a scale parameter \(b > 0\).

Usage

Laplace(mu = 0, sigma = 1)
dlaplace(x, mu, sigma, log = FALSE)
plaplace(q, mu, sigma, lower.tail = TRUE, log.p = FALSE)
qlaplace(p, mu, sigma, lower.tail = TRUE, log.p = FALSE)
rlaplace(n, mu, sigma)
# S4 method for Laplace,numeric
d(distr, x, log = FALSE)
# S4 method for Laplace,numeric
p(distr, q, lower.tail = TRUE, log.p = FALSE)
# S4 method for Laplace,numeric
qn(distr, p, lower.tail = TRUE, log.p = FALSE)
# S4 method for Laplace,numeric
r(distr, n)
# S4 method for Laplace
mean(x)
# S4 method for Laplace
median(x)
# S4 method for Laplace
mode(x)
# S4 method for Laplace
var(x)
# S4 method for Laplace
sd(x)
# S4 method for Laplace
skew(x)
# S4 method for Laplace
kurt(x)
# S4 method for Laplace
entro(x)
# S4 method for Laplace
finf(x)
lllaplace(x, mu, sigma)
# S4 method for Laplace,numeric
ll(distr, x)
elaplace(x, type = "mle", ...)
# S4 method for Laplace,numeric
mle(distr, x, na.rm = FALSE)
# S4 method for Laplace,numeric
me(distr, x, na.rm = FALSE)
vlaplace(mu, sigma, type = "mle")
# S4 method for Laplace
avar_mle(distr)
# S4 method for Laplace
avar_me(distr)

Value

Each type of function returns a different type of object:

• Distribution Functions: When supplied with one argument (distr), the d(), p(), q(), r(), ll() functions return the density, cumulative probability, quantile, random sample generator, and log-likelihood functions, respectively. When supplied with both arguments (distr and x), they evaluate the aforementioned functions directly.

• Moments: Returns a numeric, either vector or matrix depending on the moment and the distribution. The moments() function returns a list with all the available methods.

• Estimation: Returns a list, the estimators of the unknown parameters. Note that in distribution families like the binomial, multinomial, and negative binomial, the size is not returned, since it is considered known.

• Variance: Returns a named matrix. The asymptotic covariance matrix of the estimator.

Arguments

mu, sigma

numeric. The distribution parameters.

x

For the density function, x is a numeric vector of quantiles. For the moments functions, x is an object of class Laplace. For the log-likelihood and the estimation functions, x is the sample of observations.

log, log.p

logical. Should the logarithm of the probability be returned?

q

numeric. Vector of quantiles.

lower.tail

logical. If TRUE (default), probabilities are \(P(X \leq x)\), otherwise \(P(X > x)\).

p

numeric. Vector of probabilities.

n

number of observations. If length(n) > 1, the length is taken to be the number required.

distr

an object of class Laplace.

type

character, case ignored. The estimator type (mle or me).

...

extra arguments.

na.rm

logical. Should the NA values be removed?

Details

The probability density function (PDF) of the Laplace distribution is: $$ f(x; \mu, b) = \frac{1}{2b} \exp\left(-\frac{|x - \mu|}{b}\right) .$$

Examples

# -----------------------------------------------------
# Laplace Distribution Example
# -----------------------------------------------------

# Create the distribution
m <- 3 ; s <- 5
D <- Laplace(m, s)

# ------------------
# dpqr Functions
# ------------------

d(D, c(0.3, 2, 10)) # density function
p(D, c(0.3, 2, 10)) # distribution function
qn(D, c(0.4, 0.8)) # inverse distribution function
x <- r(D, 100) # random generator function

# alternative way to use the function
df <- d(D) ; df(x) # df is a function itself

# ------------------
# Moments
# ------------------

mean(D) # Expectation
median(D) # Median
mode(D) # Mode
var(D) # Variance
sd(D) # Standard Deviation
skew(D) # Skewness
kurt(D) # Excess Kurtosis
entro(D) # Entropy
finf(D) # Fisher Information Matrix

# List of all available moments
mom <- moments(D)
mom$mean # expectation

# ------------------
# Point Estimation
# ------------------

elaplace(x, type = "mle")
elaplace(x, type = "me")

mle(D, x)
me(D, x)
e(D, x, type = "mle")

mle("laplace", x) # the distr argument can be a character

# ------------------
# Estimator Variance
# ------------------

vlaplace(m, s, type = "mle")
vlaplace(m, s, type = "me")

avar_mle(D)
avar_me(D)

v(D, type = "mle")