Description Usage Arguments Details Value Note Author(s) References See Also Examples

Those functions are used in the distribution book of gamlss, see Rigby et. al 2019.

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binom_1_31(family = BI, mu = c(0.1, 0.5, 0.7), bd = NULL, miny = 0,
maxy = 20, cex.axis = 1.2, cex.all = 1.5)
binom_2_33(family = BB, mu = c(0.1, 0.5, 0.8), sigma = c(0.5, 1, 2),
bd = NULL, miny = 0, maxy = 10, cex.axis = 1.5,
cex.all = 1.5)
binom_3_33(family = ZIBB, mu = c(0.1, 0.5, 0.8), sigma = c(0.5, 1, 2),
nu = c(0.01, 0.3), bd = NULL, miny = 0, maxy = 10,
cex.axis = 1.5, cex.all = 1.5, cols = c("darkgray", "black"),
spacing = 0.3, legend.cex=1, legend.x="topright",
legend.where=c("left","right", "center"))
contR_2_12(family = "NO", mu = c(0, -1, 1), sigma = c(1, 0.5, 2),
cols=c(gray(.1),gray(.2),gray(.3)),
ltype = c(1, 2, 3), maxy = 7,
no.points = 201, y.axis.lim = 1.1,
cex.axis = 1.5, cex.all = 1.5,
legend.cex=1, legend.x="topleft" )
contR_3_11(family = "PE", mu = 0, sigma = 1, nu = c(1, 2, 3),
cols=c(gray(.1),gray(.2),gray(.3)), maxy = 7, no.points = 201,
ltype = c(1, 2, 3), y.axis.lim = 1.1, cex.axis = 1.5,
cex.all = 1.5, legend.cex=1, legend.x="topleft")
contR_4_13(family = "SEP3", mu = 0, sigma = 1, nu = c(0.5, 1, 2),
tau = c(1, 2, 5), cols=c(gray(.1),gray(.2),gray(.3)), maxy = 7,
no.points = 201, ltype = c(1, 2, 3),
y.axis.lim = 1.1, cex.axis = 1.5, cex.all = 1.5,
legend.cex=1, legend.x="topleft",
legend.where=c("left","right"))
contRplus_2_11(family = GA, mu = 1, sigma = c(0.1, 0.6, 1),
cols=c(gray(.1),gray(.2),gray(.3)),
maxy = 4, no.points = 201,
y.axis.lim = 1.1, ltype = c(1, 2, 3),
cex.axis = 1.5, cex.all = 1.5,
legend.cex=1, legend.x="topright")
contRplus_3_13(family = "BCCG", mu = 1, sigma = c(0.15, 0.2, 0.5),
nu = c(-2, 0, 4),
cols=c(gray(.1),gray(.2),gray(.3)),
maxy = 4, ltype = c(1, 2, 3),
no.points = 201, y.axis.lim = 1.1,
cex.axis = 1.5, cex.all = 1.5,
legend.cex=1, legend.x="topright",
legend.where=c("left","right"))
contRplus_4_33(family = BCT, mu = 1, sigma = c(0.15, 0.2, 0.5),
nu = c(-4, 0, 2), tau = c(100, 5, 1),
cols=c(gray(.1),gray(.2),gray(.3)),
maxy = 4, ltype = c(1, 2, 3),
no.points = 201, y.axis.lim = 1.1,
cex.axis = 1.5, cex.all = 1.5,
legend.cex=1, legend.x="topright",
legend.where=c("left","right"))
contR01_2_13(family = "BE", mu = c(0.2, 0.5, 0.8), sigma = c(0.2, 0.5, 0.8),
cols=c(gray(.1),gray(.2),gray(.3)),
ltype = c(1, 2, 3), maxy = 7, no.points = 201,
y.axis.lim = 1.1, maxYlim = 10,
cex.axis = 1.5, cex.all = 1.5,
legend.cex=1, legend.x="topright",
legend.where=c("left","right", "center"))
contR01_4_33(family = GB1, mu = c(0.5), sigma = c(0.2, 0.5, 0.7),
nu = c(1, 2, 5), tau = c(0.5, 1, 2),
cols=c(gray(.1),gray(.2),gray(.3)),
maxy = 0.999, ltype = c(1, 2, 3),
no.points = 201, y.axis.lim = 1.1,
maxYlim = 10,cex.axis = 1.5, cex.all = 1.5,
legend.cex=1, legend.x="topright",
legend.where=c("left","right", "center"))
count_1_31(family = PO, mu = c(1, 2, 5), miny = 0, maxy = 10,
cex.axis = 1.2, cex.all = 1.5)
count_1_22(family = PO, mu = c(1, 2, 5, 10), miny = 0,
maxy = 20, cex.axis = 1.2, cex.all = 1.5)
count_2_32(family = NBI, mu = c(0.5, 1, 5), sigma = c(0.1, 2),
miny = 0, maxy = 10, cex.axis = 1.5, cex.all = 1.5)
count_2_32R(family = NBI, mu = c(1, 2), sigma = c(0.1, 1, 2),
miny = 0, maxy = 10, cex.axis = 1.5, cex.all = 1.5)
count_2_33(family = NBI, mu = c(0.1, 1, 2), sigma = c(0.5, 1, 2),
miny = 0, maxy = 10, cex.axis = 1.5, cex.all = 1.5)
count_3_32(family = SICHEL, mu = c(1, 5, 10), sigma = c(0.5, 1),
nu = c(-0.5, 0.5), miny = 0, maxy = 10, cex.axis = 1.5,
cex.all = 1.5, cols = c("darkgray", "black"), spacing = 0.2,
legend.cex=1, legend.x="topright",
legend.where=c("left","right"))
count_3_33(family = SICHEL, mu = c(1, 5, 10), sigma = c(0.5, 1, 2),
nu = c(-0.5, 0.5, 1), miny = 0, maxy = 10, cex.axis = 1.5,
cex.all = 1.5, cols = c("darkgray", "black"), spacing = 0.3,
legend.cex=1, legend.x="topright",
legend.where=c("left","right", "center"))
``` |

`family` |
a gamlss family distribution |

`mu` |
the |

`sigma` |
The |

`nu` |
the |

`tau` |
the |

`bd` |
the binomial denominator |

`miny` |
minimal value for the y axis |

`maxy` |
maximal value for the y axis |

`cex.axis` |
the size of the letters in the two axes |

`cex.all` |
the overall size of all plotting characters |

`cols` |
colours |

`spacing` |
spacing between plots |

`ltype` |
The type of lines used |

`no.points` |
the number of points in the curve |

`y.axis.lim` |
the maximum value for the y axis |

`maxYlim` |
the maximum permissible value for Y |

`legend.cex` |
the size of the legend |

`legend.x` |
where in the figure to put the legend |

`legend.where` |
where in the whole plot to put the legend |

Th function plot different types of continuous and discrete distributions:
i) `contR`

: continuous distribution defined on minus infinity to plus infinity,
ii) `contRplus`

: continuous distribution defined from zero to plus infinity,
iii) `contR01`

: continuous distribution defined from zero to 1,
iv) `bimom`

binomial type discrete distributions,
v) `count`

count type discrete distributions.

The first number after the first underline in the name of the function indicates the number of parameters in the distribution. The two numbers after the second underline indicate how may rows and columns are in the plot.

The result is a plot

more notes

Mikis Stasinopoulos, Robert Rigby, Gillian Heller, Fernada De Bastiani

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. and De Bastiani F., (2019)
*Distributions for modelling location scale and shape: Using GAMLSS in R*, Chapman and Hall/CRC (in press).

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, http://www.jstatsoft.org/v23/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.

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