Mathematical and statistical functions for the EmpiricalMV distribution, which is commonly used in sampling such as MCMC.
Returns an R6 object inheriting from class SDistribution.
The distribution is supported on \(x_1,...,x_k\).
EmpMV(data = data.frame(1, 1))
N/A
N/A
distr6::Distribution -> distr6::SDistribution -> EmpiricalMV
nameFull name of distribution.
short_nameShort name of distribution for printing.
descriptionBrief description of the distribution.
new()Creates a new instance of this R6 class.
EmpiricalMV$new(data = NULL, decorators = NULL)
data[matrix]
Matrix-like object where each column is a vector of observed samples corresponding
to each variable.
decorators(character())
Decorators to add to the distribution during construction.
EmpiricalMV$new(MultivariateNormal$new()$rand(100))
mean()The arithmetic mean of a (discrete) probability distribution X is the expectation $$E_X(X) = \sum p_X(x)*x$$ with an integration analogue for continuous distributions.
EmpiricalMV$mean(...)
...Unused.
variance()The variance of a distribution is defined by the formula $$var_X = E[X^2] - E[X]^2$$ where \(E_X\) is the expectation of distribution X. If the distribution is multivariate the covariance matrix is returned.
EmpiricalMV$variance(...)
...Unused.
setParameterValue()Sets the value(s) of the given parameter(s).
EmpiricalMV$setParameterValue( ..., lst = NULL, error = "warn", resolveConflicts = FALSE )
...ANY
Named arguments of parameters to set values for. See examples.
lst(list(1))
Alternative argument for passing parameters. List names should be parameter names and list values
are the new values to set.
error(character(1))
If "warn" then returns a warning on error, otherwise breaks if "stop".
resolveConflicts(logical(1))
If FALSE (default) throws error if conflicting parameterisations are provided, otherwise
automatically resolves them by removing all conflicting parameters.
clone()The objects of this class are cloneable with this method.
EmpiricalMV$clone(deep = FALSE)
deepWhether to make a deep clone.
The EmpiricalMV distribution is defined by the pmf, $$p(x) = \sum I(x = x_i) / k$$ for \(x_i \epsilon R, i = 1,...,k\).
Sampling from this distribution is performed with the sample function with the elements given as the support set and uniform probabilities. Sampling is performed with replacement, which is consistent with other distributions but non-standard for Empirical distributions. Use simulateEmpiricalDistribution to sample without replacement.
The cdf assumes that the elements are supplied in an indexed order (otherwise the results are meaningless).
McLaughlin, M. P. (2001). A compendium of common probability distributions (pp. 2014-01). Michael P. McLaughlin.
Other discrete distributions:
Bernoulli,
Binomial,
Categorical,
Degenerate,
DiscreteUniform,
Empirical,
Geometric,
Hypergeometric,
Logarithmic,
Matdist,
Multinomial,
NegativeBinomial,
WeightedDiscrete
Other multivariate distributions:
Dirichlet,
Multinomial,
MultivariateNormal
# NOT RUN {
## ------------------------------------------------
## Method `EmpiricalMV$new`
## ------------------------------------------------
EmpiricalMV$new(MultivariateNormal$new()$rand(100))
# }
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