| distribMode {modeest} | R Documentation |
These functions return the mode of the main distributions implemented in R.
## Continuous distributions
betaMode(shape1, shape2, ncp = 0) # Beta
cauchyMode(location = 0, ...) # Cauchy
chisqMode(df, ncp = 0) # Chisquare
expMode(...) # Exponentiel
fMode(df1, df2) # F
frechetMode(loc = 0, scale = 1, shape = 1, ...) # Fréchet (package 'evd')
gammaMode(shape, rate = 1, scale = 1/rate) # Gamma
normMode(mean = 0, ...) # Normal (Gaussian)
gevMode(loc = 1, scale = 1, shape = 1, ...) # Generalised Extreme Value (package 'evd')
ghMode(alpha = 1, beta = 0, delta = 1, mu = 0,
lambda = 1, ...) # Generalised Hyperbolic (package 'fBasics')
gpdMode(loc = 0, scale = 1, shape = 0, ...) # Generalised Pareto (package 'evd')
gumbelMode(loc = 0, ...) # Gumbel (package 'evd')
hypMode(alpha = 1, beta = 0, delta = 1, mu = 0,
pm = c(1, 2, 3, 4)) # Hyperbolic (package 'fBasics')
logisMode(location = 0, ...) # Logistic
lnormMode(meanlog = 0, sdlog = 1) # Lognormal
nigMode(alpha = 1, beta = 0, delta = 1,
mu = 0, ...) # Normal Inverse Gaussian (package 'fBasics')
stableMode(alpha, beta, gamma = 1, delta = 0, pm = 0, ...) # Stable (package 'fBasics')
symstbMode(...) # Symmetric stable (package 'fBasics')
rweibullMode(loc = 0, scale = 1, shape = 1, ...) # Negative Weibull (package 'evd')
tMode(df, ncp = 0) # T (Student)
unifMode(min = 0, max = 1) # Uniform
weibullMode(shape, scale = 1, ...) # Weibull
## Discrete distributions
bernMode(prob) # Bernoulli
binomMode(size, prob) # Binomial
geomMode(...) # Geometric
hyperMode(m, n, k, ...) # Hypergeometric
nbinomMode(size, prob, mu) # Negative Binomial
poisMode(lambda) # Poisson
shape1, shape2, ncp, location, df, df1, df2, loc, scale, shape, |
|
rate, mean, alpha, beta, delta, mu, lambda, pm, meanlog, sdlog, |
|
gamma, min, max, prob, size, m, n, k |
|
... |
further arguments, which will be ignored. |
A numeric value is returned, the (true) mode of the distribution.
Some functions like normMode or cauchyMode, which are related
to symmetric distributions, are trivial, but are implemented for exhaustivity.
Paul Poncet paulponcet@yahoo.fr,
except for hypMode and stableMode written by Diethelm Wuertz, see package fBasics.
mlv for the estimation of the mode;
the documentation of the related distributions Beta, GammaDist, etc.
layout(mat = matrix(1:2,1,2))
## Beta distribution
curve(dbeta(x, shape1 = 2, shape2 = 3.1), xlim = c(0,1), ylab = "Beta density")
M <- betaMode(shape1 = 2, shape2 = 3.1)
abline(v = M, col = 2)
mlv("beta", shape1 = 2, shape2 = 3.1)
## Lognormal distribution
curve(dlnorm(x, meanlog = 3, sdlog = 1.1), xlim = c(0, 10), ylab = "Lognormal density")
M <- lnormMode(meanlog = 3, sdlog = 1.1)
abline(v = M, col = 2)
mlv("lnorm", meanlog = 3, sdlog = 1.1)
## Poisson distribution
poisMode(lambda = 6)
poisMode(lambda = 6.1)
mlv("poisson", lambda = 6.1)
layout(mat = matrix(1,1,1))