Lnorm {joker}R Documentation

Log-Normal Distribution

Description

The Lognormal distribution is an absolute continuous probability distribution of a random variable whose logarithm is normally distributed. It is defined by parameters \mu and \sigma > 0, which are the mean and standard deviation of the underlying normal distribution.

Usage

Lnorm(meanlog = 0, sdlog = 1)

## S4 method for signature 'Lnorm,numeric'
d(distr, x, log = FALSE)

## S4 method for signature 'Lnorm,numeric'
p(distr, q, lower.tail = TRUE, log.p = FALSE)

## S4 method for signature 'Lnorm,numeric'
qn(distr, p, lower.tail = TRUE, log.p = FALSE)

## S4 method for signature 'Lnorm,numeric'
r(distr, n)

## S4 method for signature 'Lnorm'
mean(x)

## S4 method for signature 'Lnorm'
median(x)

## S4 method for signature 'Lnorm'
mode(x)

## S4 method for signature 'Lnorm'
var(x)

## S4 method for signature 'Lnorm'
sd(x)

## S4 method for signature 'Lnorm'
skew(x)

## S4 method for signature 'Lnorm'
kurt(x)

## S4 method for signature 'Lnorm'
entro(x)

## S4 method for signature 'Lnorm'
finf(x)

lllnorm(x, meanlog, sdlog)

## S4 method for signature 'Lnorm,numeric'
ll(distr, x)

elnorm(x, type = "mle", ...)

## S4 method for signature 'Lnorm,numeric'
mle(distr, x, na.rm = FALSE)

## S4 method for signature 'Lnorm,numeric'
me(distr, x, na.rm = FALSE)

vlnorm(meanlog, sdlog, type = "mle")

## S4 method for signature 'Lnorm'
avar_mle(distr)

## S4 method for signature 'Lnorm'
avar_me(distr)

Arguments

meanlog, sdlog

numeric. The distribution parameters.

distr

an object of class Lnorm.

x

For the density function, x is a numeric vector of quantiles. For the moments functions, x is an object of class Lnorm. 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.

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 Lognormal distribution is:

f(x; \mu, \sigma) = \frac{1}{x \sigma \sqrt{2\pi}} e^{-\frac{(\log x - \mu)^2}{2 \sigma^2}}, \quad x > 0 .

Value

Each type of function returns a different type of object:

See Also

Functions from the stats package: dlnorm(), plnorm(), qlnorm(), rlnorm()

Examples

# -----------------------------------------------------
# Lnorm Distribution Example
# -----------------------------------------------------

# Create the distribution
m <- 3 ; s <- 5
D <- Lnorm(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
# ------------------

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

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

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

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

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

avar_mle(D)
avar_me(D)

v(D, type = "mle")

[Package joker version 0.14.2 Index]