nb.OptimizeDispersion: (NB) model. The NB distribution is parametrized by two...
In drisso/learn2count: Structure Learning for Count Data
nb.OptimizeDispersion R Documentation
(NB) model. The NB distribution is parametrized by two
parameters: the mean value and the dispersion of the negative binomial distribution
Description
(NB) model. The NB distribution is parametrized by two
parameters: the mean value and the dispersion of the negative binomial distribution
Usage
nb.OptimizeDispersion(mu, Y, n)
Arguments
mu
the vector mean of the negative binomial
Y
the vector of counts
n
the length of the vector to return
Note that theta is sometimes called inverse dispersion parameter (and
phi=1/theta is then called the dispersion parameter). We follow the
convention that the variance of the NB variable with mean mu and dispersion
theta is mu + mu^2/theta.
drisso/learn2count documentation built on March 25, 2023, 4:21 p.m.
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| nb.OptimizeDispersion | R Documentation |
(NB) model. The NB distribution is parametrized by two parameters: the mean value and the dispersion of the negative binomial distribution
Description
(NB) model. The NB distribution is parametrized by two parameters: the mean value and the dispersion of the negative binomial distribution
Usage
nb.OptimizeDispersion(mu, Y, n)
Arguments
mu |
the vector mean of the negative binomial |
Y |
the vector of counts |
n |
the length of the vector to return Note that theta is sometimes called inverse dispersion parameter (and phi=1/theta is then called the dispersion parameter). We follow the convention that the variance of the NB variable with mean mu and dispersion theta is mu + mu^2/theta. |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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