Generalized Gamma model for SAR intensities (R6)

R6 class for the generalized Gamma law (see dggamma()). Estimation is carried out by profiling: for a given power \(\alpha\), the maximum likelihood estimates of \(\phi\) and \(\mu\) are available in closed form (up to a one-dimensional root), so the profile likelihood is maximised over \(\alpha\) only.

Super class

SARModel -> GGammaSAR

Methods

Public methods

• GGammaSAR$new()

• GGammaSAR$density()

• GGammaSAR$random()

• GGammaSAR$fit()

• GGammaSAR$entropy()

• GGammaSAR$clone()

Inherited methods

• SARModel$entropy_variance()
• SARModel$glance()
• SARModel$log_likelihood()
• SARModel$n_parameters()
• SARModel$print()
• SARModel$tidy()

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GGammaSAR$new()

Create a generalized Gamma model.

Usage

GGammaSAR$new(alpha = 1, phi = 1, mu = 1)

Arguments

alpha

Power (> 0).

phi

Shape (> 0).

mu

Inverse scale (> 0).

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GGammaSAR$density()

(Log-)density.

Usage

GGammaSAR$density(x, log = FALSE)

Arguments

x

Numeric vector of intensities.

log

Return log-densities?

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GGammaSAR$random()

Simulate intensities.

Usage

GGammaSAR$random(n)

Arguments

n

Number of observations.

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GGammaSAR$fit()

Fit by (profile) maximum likelihood.

Usage

GGammaSAR$fit(x, phi = NULL)

Arguments

x

Numeric vector of positive intensities.

phi

Known shape (number of looks). If NULL it is estimated.

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GGammaSAR$entropy()

Shannon entropy.

Usage

GGammaSAR$entropy()

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GGammaSAR$clone()

The objects of this class are cloneable with this method.

Usage

GGammaSAR$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

set.seed(1)
z <- rggamma(1000, alpha = 1.5, phi = 3, mu = 2)
m <- GGammaSAR$new()$fit(z)
m
#> <Generalized Gamma> fitted to n = 1000 observations
#>   alpha = 1.34428
#>   phi = 3.4875
#>   mu = 2.47093
#>   logLik = -458.979
#>   Shannon entropy = 0.458979