DPO | R Documentation |

The function `DPO()`

defines the double Poisson distribution, a two parameters distribution, for a `gamlss.family`

object to be used in GAMLSS fitting using the function `gamlss()`

. The functions `dDPO`

, `pDPO`

, `qDPO`

and `rPO`

define the density, distribution function, quantile function and random generation for the double Poisson, `DPO()`

, distribution. The function `get_C()`

calculates numericaly the constant of proportionality needed for the pdf to sum up to 1.

DPO(mu.link = "log", sigma.link = "log") dDPO(x, mu = 1, sigma = 1, log = FALSE) pDPO(q, mu = 1, sigma = 1, lower.tail = TRUE, log.p = FALSE) qDPO(p, mu = 1, sigma = 1, lower.tail = TRUE, log.p = FALSE, max.value = 10000) rDPO(n, mu = 1, sigma = 1, max.value = 10000) get_C(x, mu, sigma)

`mu.link` |
the link function for |

`sigma.link` |
the link function for |

`x, q` |
vector of (non-negative integer) quantiles |

`p` |
vector of probabilities |

`mu` |
the |

`sigma` |
the |

`lower.tail` |
logical; if |

`log, log.p` |
logical; if |

`max.value` |
a constant, set to the default value of 10000 for how far the algorithm should look for q |

`n` |
how many random values to generate |

The definition for the Double Poisson distribution first introduced by Efron (1986) is:

*f(y|mu,sigma)=(1/sigma)^{1/2} [(e^{-y} y^y)/y!] [(e mu)/y]^{y/sigma} C*

for *y=0,1,2, ...,Inf*, *μ>0* and *σ>0* where *C* is the constant of proportinality which is calculated numerically using the function `get_C`

.

The function `DPO`

returns a `gamlss.family`

object which can be used to fit a double Poisson distribution in the `gamlss()`

function.

The distributons calculates the constant of proportionality numerically therefore it can be slow for large data

Mikis Stasinopoulos, Bob Rigby and Marco Enea

Efron, B., 1986. Double exponential families and their use in generalized linear Regression. Journal of the American Statistical Association 81 (395), 709-721.

Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion),
*Appl. Statist.*, **54**, part 3, pp 507-554.

Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019)
*Distributions for modeling location, scale, and shape: Using GAMLSS in R*, Chapman and Hall/CRC, doi: 10.1201/9780429298547. An older version can be found in https://www.gamlss.com/.

Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R.
*Journal of Statistical Software*, Vol. **23**, Issue 7, Dec 2007, doi: 10.18637/jss.v023.i07.

Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017)
*Flexible Regression and Smoothing: Using GAMLSS in R*,
Chapman and Hall/CRC. doi: 10.1201/b21973

(see also https://www.gamlss.com/).

`PO`

DPO() # overdisperse DPO x <- 0:20 plot(x, dDPO(x, mu=5, sigma=3), type="h", col="red") # underdisperse DPO plot(x, dDPO(x, mu=5, sigma=.3), type="h", col="red") # generate random sample Y <- rDPO(100,5,.5) plot(table(Y)) points(0:20, 100*dDPO(0:20, mu=5, sigma=.5)+0.2, col="red") # fit a model to the data # library(gamlss) # gamlss(Y~1,family=DPO)

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