R Graphical Manual

Last data update: 2014.03.03

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R Release (3.2.3)
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Results 1 - 9 of 9 found.
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pl.dist (Package: MixedPoisson) :

The function fits a mixed Poisson distribution, in which the random parameter follows Lindley distribution. As the method of estimation Expectation-maximization algorithm is used. In M-step the analytical formulas taken from [Karlis, 2005] are applied.

● Data Source: CranContrib
● Keywords:
● Alias: pl.dist
● 0 images

pln.reg.glm (Package: MixedPoisson) :

The function fits a mixed Poisson regression, in which the random parameter follows Log_normal distribution. As the method of estimation Expectation-maximization algorithm is used. In M-step the GLM is applied.

● Data Source: CranContrib
● Keywords:
● Alias: pln.reg.glm
● 0 images

pg.reg.glm (Package: MixedPoisson) :

The function fits a mixed Poisson regression, in which the random parameter follows Gamma distribution. As the method of estimation Expectation-maximization algorithm is used. In M-step the GLM is applied.

● Data Source: CranContrib
● Keywords:
● Alias: pg.reg.glm
● 0 images

pig.dist.glm (Package: MixedPoisson) :

The function fits a mixed Poisson distribution, in which the random parameter follows Inverse_Gaussian distribution. As the method of estimation Expectation-maximization algorithm is used. In M-step the GLM is applied.

● Data Source: CranContrib
● Keywords:
● Alias: pig.dist.glm
● 0 images

pg.dist.glm (Package: MixedPoisson) :

The function fits a mixed Poisson distribution, in which the random parameter follows Gamma distribution. As the method of estimation Expectation-maximization algorithm is used. In M-step the GLM is applied.

● Data Source: CranContrib
● Keywords:
● Alias: pg.dist.glm
● 0 images

MixedPoisson-package (Package: MixedPoisson) :

The package provides functions for estimating parameters of different mixed Poisson models. Expectation-Maximization (EM) algorithm is used as the estimation technique.

● Data Source: CranContrib
● Keywords:
● Alias: MixedPoisson, MixedPoisson-package
● 0 images

pln.dist.glm (Package: MixedPoisson) :

The function fits a mixed Poisson distribution, in which the random parameter follows Log_normal distribution. As the method of estimation Expectation-maximization algorithm is used. In M-step the GLM is applied.

● Data Source: CranContrib
● Keywords:
● Alias: pln.dist.glm
● 0 images

pg.dist (Package: MixedPoisson) :

The function fits a mixed Poisson distribution, in which the random parameter follows Gamma distribution (the negative-binomial distribution). As the method of estimation Expectation-maximization algorithm is used. In M-step the analytical formulas taken from [Karlis, 2005] are applied.

● Data Source: CranContrib
● Keywords:
● Alias: pg.dist
● 0 images

pig.reg.glm (Package: MixedPoisson) :

The function fits a mixed Poisson regression, in which the random parameter follows inverse_Gaussian distribution. As the method of estimation Expectation-maximization algorithm is used. In M-step the GLM is applied.

● Data Source: CranContrib
● Keywords:
● Alias: pig.reg.glm
● 0 images