Those functions are used in the distribution book of gamlss, see Rigby et. al 2019.
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"))
a gamlss family distribution
the mu parameter values
The sigma parameter values
the nu parameter values
the tau parameter values
the binomial denominator
minimal value for the y axis
maximal value for the y axis
the size of the letters in the two axes
the overall size of all plotting characters
colours
spacing between plots
The type of lines used
the number of points in the curve
the maximum value for the y axis
the maximum permissible value for Y
the size of the legend
where in the figure to put the legend
where in the whole plot to put the legend
The result is a plot
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.
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.
# NOT RUN {
count_1_31()
# }
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