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))