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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distrMode.R
\name{distrMode}
\alias{distrMode}
\alias{betaMode}
\alias{cauchyMode}
\alias{chisqMode}
\alias{dagumMode}
\alias{expMode}
\alias{fMode}
\alias{fiskMode}
\alias{frechetMode}
\alias{gammaMode}
\alias{normMode}
\alias{gevMode}
\alias{ghMode}
\alias{ghtMode}
\alias{gldMode}
\alias{gompertzMode}
\alias{gpdMode}
\alias{gumbelMode}
\alias{hypMode}
\alias{koenkerMode}
\alias{kumarMode}
\alias{laplaceMode}
\alias{logisMode}
\alias{lnormMode}
\alias{lomaxMode}
\alias{maxwellMode}
\alias{mvnormMode}
\alias{nakaMode}
\alias{nigMode}
\alias{paralogisticMode}
\alias{paretoMode}
\alias{rayleighMode}
\alias{stableMode}
\alias{stableMode2}
\alias{tMode}
\alias{unifMode}
\alias{weibullMode}
\alias{yulesMode}
\alias{bernMode}
\alias{binomMode}
\alias{geomMode}
\alias{hyperMode}
\alias{nbinomMode}
\alias{poisMode}
\title{Mode of some continuous and discrete distributions}
\usage{
distrMode(x, ...)
betaMode(shape1, shape2, ncp = 0)
cauchyMode(location = 0, ...)
chisqMode(df, ncp = 0)
dagumMode(scale = 1, shape1.a, shape2.p)
expMode(...)
fMode(df1, df2)
fiskMode(scale = 1, shape1.a)
frechetMode(location = 0, scale = 1, shape = 1, ...)
gammaMode(shape, rate = 1, scale = 1/rate)
normMode(mean = 0, ...)
gevMode(location = 0, scale = 1, shape = 0, ...)
ghMode(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2)
ghtMode(beta = 0.1, delta = 1, mu = 0, nu = 10)
gldMode(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8)
gompertzMode(scale = 1, shape)
gpdMode(location = 0, scale = 1, shape = 0)
gumbelMode(location = 0, ...)
hypMode(alpha = 1, beta = 0, delta = 1, mu = 0, pm = c(1, 2, 3, 4))
koenkerMode(location = 0, ...)
kumarMode(shape1, shape2)
laplaceMode(location = 0, ...)
logisMode(location = 0, ...)
lnormMode(meanlog = 0, sdlog = 1)
lomaxMode(...)
maxwellMode(rate)
mvnormMode(mean, ...)
nakaMode(scale = 1, shape)
nigMode(alpha = 1, beta = 0, delta = 1, mu = 0)
paralogisticMode(scale = 1, shape1.a)
paretoMode(scale = 1, ...)
rayleighMode(scale = 1)
stableMode(alpha, beta, gamma = 1, delta = 0, pm = 0, ...)
stableMode2(loc, disp, skew, tail)
tMode(df, ncp)
unifMode(min = 0, max = 1)
weibullMode(shape, scale = 1)
yulesMode(...)
bernMode(prob)
binomMode(size, prob)
geomMode(...)
hyperMode(m, n, k, ...)
nbinomMode(size, prob, mu)
poisMode(lambda)
}
\arguments{
\item{x}{character. The name of the distribution to consider.}
\item{...}{Additional parameters.}
\item{shape1}{non-negative parameters of the Beta distribution.}
\item{shape2}{non-negative parameters of the Beta distribution.}
\item{ncp}{non-centrality parameter.}
\item{location}{location and scale parameters.}
\item{df}{degrees of freedom (non-negative, but can be non-integer).}
\item{scale}{location and scale parameters.}
\item{shape1.a}{shape parameters.}
\item{shape2.p}{shape parameters.}
\item{df1}{degrees of freedom. \code{Inf} is allowed.}
\item{df2}{degrees of freedom. \code{Inf} is allowed.}
\item{shape}{the location parameter \eqn{a},
scale parameter \eqn{b}, and shape parameter \eqn{s}.}
\item{rate}{vector of rates.}
\item{mean}{vector of means.}
\item{alpha}{
shape parameter \code{alpha};
skewness parameter \code{beta}, \code{abs(beta)} is in the
range (0, alpha);
scale parameter \code{delta}, \code{delta} must be zero or
positive;
location parameter \code{mu}, by default 0.
These is the meaning of the parameters in the first
parameterization \code{pm=1} which is the default
parameterization selection.
In the second parameterization, \code{pm=2} \code{alpha}
and \code{beta} take the meaning of the shape parameters
(usually named) \code{zeta} and \code{rho}.
In the third parameterization, \code{pm=3} \code{alpha}
and \code{beta} take the meaning of the shape parameters
(usually named) \code{xi} and \code{chi}.
In the fourth parameterization, \code{pm=4} \code{alpha}
and \code{beta} take the meaning of the shape parameters
(usually named) \code{a.bar} and \code{b.bar}.
}
\item{beta}{
shape parameter \code{alpha};
skewness parameter \code{beta}, \code{abs(beta)} is in the
range (0, alpha);
scale parameter \code{delta}, \code{delta} must be zero or
positive;
location parameter \code{mu}, by default 0.
These is the meaning of the parameters in the first
parameterization \code{pm=1} which is the default
parameterization selection.
In the second parameterization, \code{pm=2} \code{alpha}
and \code{beta} take the meaning of the shape parameters
(usually named) \code{zeta} and \code{rho}.
In the third parameterization, \code{pm=3} \code{alpha}
and \code{beta} take the meaning of the shape parameters
(usually named) \code{xi} and \code{chi}.
In the fourth parameterization, \code{pm=4} \code{alpha}
and \code{beta} take the meaning of the shape parameters
(usually named) \code{a.bar} and \code{b.bar}.
}
\item{delta}{
shape parameter \code{alpha};
skewness parameter \code{beta}, \code{abs(beta)} is in the
range (0, alpha);
scale parameter \code{delta}, \code{delta} must be zero or
positive;
location parameter \code{mu}, by default 0.
These is the meaning of the parameters in the first
parameterization \code{pm=1} which is the default
parameterization selection.
In the second parameterization, \code{pm=2} \code{alpha}
and \code{beta} take the meaning of the shape parameters
(usually named) \code{zeta} and \code{rho}.
In the third parameterization, \code{pm=3} \code{alpha}
and \code{beta} take the meaning of the shape parameters
(usually named) \code{xi} and \code{chi}.
In the fourth parameterization, \code{pm=4} \code{alpha}
and \code{beta} take the meaning of the shape parameters
(usually named) \code{a.bar} and \code{b.bar}.
}
\item{mu}{
shape parameter \code{alpha};
skewness parameter \code{beta}, \code{abs(beta)} is in the
range (0, alpha);
scale parameter \code{delta}, \code{delta} must be zero or
positive;
location parameter \code{mu}, by default 0.
These is the meaning of the parameters in the first
parameterization \code{pm=1} which is the default
parameterization selection.
In the second parameterization, \code{pm=2} \code{alpha}
and \code{beta} take the meaning of the shape parameters
(usually named) \code{zeta} and \code{rho}.
In the third parameterization, \code{pm=3} \code{alpha}
and \code{beta} take the meaning of the shape parameters
(usually named) \code{xi} and \code{chi}.
In the fourth parameterization, \code{pm=4} \code{alpha}
and \code{beta} take the meaning of the shape parameters
(usually named) \code{a.bar} and \code{b.bar}.
}
\item{lambda}{
shape parameter \code{alpha};
skewness parameter \code{beta}, \code{abs(beta)} is in the
range (0, alpha);
scale parameter \code{delta}, \code{delta} must be zero or
positive;
location parameter \code{mu}, by default 0.
These is the meaning of the parameters in the first
parameterization \code{pm=1} which is the default
parameterization selection.
In the second parameterization, \code{pm=2} \code{alpha}
and \code{beta} take the meaning of the shape parameters
(usually named) \code{zeta} and \code{rho}.
In the third parameterization, \code{pm=3} \code{alpha}
and \code{beta} take the meaning of the shape parameters
(usually named) \code{xi} and \code{chi}.
In the fourth parameterization, \code{pm=4} \code{alpha}
and \code{beta} take the meaning of the shape parameters
(usually named) \code{a.bar} and \code{b.bar}.
}
\item{nu}{
a numeric value, the number of degrees of freedom.
Note, \code{alpha} takes the limit of \code{abs(beta)},
and \code{lambda=-nu/2}.
}
\item{lambda1}{
are numeric values where
\code{lambda1} is the location parameter,
\code{lambda2} is the location parameter,
\code{lambda3} is the first shape parameter, and
\code{lambda4} is the second shape parameter.
}
\item{lambda2}{
are numeric values where
\code{lambda1} is the location parameter,
\code{lambda2} is the location parameter,
\code{lambda3} is the first shape parameter, and
\code{lambda4} is the second shape parameter.
}
\item{lambda3}{
are numeric values where
\code{lambda1} is the location parameter,
\code{lambda2} is the location parameter,
\code{lambda3} is the first shape parameter, and
\code{lambda4} is the second shape parameter.
}
\item{lambda4}{
are numeric values where
\code{lambda1} is the location parameter,
\code{lambda2} is the location parameter,
\code{lambda3} is the first shape parameter, and
\code{lambda4} is the second shape parameter.
}
\item{pm}{
an integer value between \code{1} and \code{4} for the
selection of the parameterization. The default takes the
first parameterization.
}
\item{meanlog}{mean and standard deviation of the distribution
on the log scale with default values of \code{0} and \code{1} respectively.}
\item{sdlog}{mean and standard deviation of the distribution
on the log scale with default values of \code{0} and \code{1} respectively.}
\item{gamma}{
value of the index parameter \code{alpha} in the interval= \eqn{(0, 2]};
skewness parameter \code{beta}, in the range \eqn{[-1, 1]};
scale parameter \code{gamma}; and location (or \sQuote{shift})
parameter \code{delta}.
}
\item{loc}{vector of (real) location parameters.}
\item{disp}{vector of (positive) dispersion parameters.}
\item{skew}{vector of skewness parameters (in [-1,1]).}
\item{tail}{vector of parameters (in [1,2]) related to the tail thickness.}
\item{min}{lower and upper limits of the distribution. Must be finite.}
\item{max}{lower and upper limits of the distribution. Must be finite.}
\item{prob}{Probability of success on each trial.}
\item{size}{number of trials (zero or more).}
\item{m}{the number of white balls in the urn.}
\item{n}{number of observations. If \code{length(n) > 1}, the length
is taken to be the number required.}
\item{k}{the number of balls drawn from the urn.}
}
\value{
A numeric value is returned, the (true) mode of the distribution.
}
\description{
These functions return the mode of the main probability
distributions implemented in R.
}
\note{
Some functions like \code{normMode} or \code{cauchyMode}, which relate
to symmetric distributions, are trivial, but are implemented for the sake of
exhaustivity.
}
\examples{
## 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(stats::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)
curve(VGAM::dpareto(x, scale = 1, shape = 1), xlim = c(0, 10))
abline(v = paretoMode(scale = 1), col = 2)
## Poisson distribution
poisMode(lambda = 6)
poisMode(lambda = 6.1)
mlv("poisson", lambda = 6.1)
}
\seealso{
\code{\link[modeest]{mlv}} for the estimation of the mode;
the documentation of the related distributions
\code{\link[stats]{Beta}}, \code{\link[stats]{GammaDist}}, etc.
}
\author{
\code{\link[fBasics]{ghMode}} and \code{\link[fBasics]{ghtMode}} are from
package \pkg{fBasics};
\code{\link[fBasics]{hypMode}} was written by David Scott;
\code{\link[fBasics]{gldMode}}, \code{\link[fBasics]{nigMode}} and
\code{\link[stabledist]{stableMode}} were written by Diethelm Wuertz.
}
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