dPT {tweeDEseq} | R Documentation |
Density function and random generation for the Poisson-Tweedie family of distributions.
dPT(x, mu, D, a, tol = 1e-15) rPT(n, mu, D, a, max = 10*sqrt(mu*D), tol = 1e-4)
x |
an object of class 'mlePT' or a non-negative vector containing the integers in which the distribution should be evaluated. |
mu |
numeric positive scalar giving the mean of the distribution. |
D |
numeric positive scalar giving the dispersion of the distribution. |
a |
numeric scalar smaller than 1 giving the shape parameter of the distribution. |
tol |
numeric scalar giving the tolerance. |
n |
integer scalar giving number of random values to return. |
max |
numeric scalar containing the maximum number of counts to be used in the sampling process. |
If 'x' is of class 'mlePT', 'dPT' will return the Poisson-Tweedie distribution with parameters equal to the ones estimated by 'mlePoissonTweedie' evaluated on the data that was used to estimate the parameters. If 'x' is a numeric vector, 'dPT' will return the density of the specified Poisson-Tweedie distribution evaluated on 'x'.
'rPT' generates random deviates.
Esnaola M, Puig P, Gonzalez D, Castelo R and Gonzalez JR (2013). A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments. BMC Bioinformatics 14: 254
A.H. El-Shaarawi, R. Zhu, H. Joe (2010). Modelling species abundance using the Poisson-Tweedie family. Environmetrics 22, pages 152-164.
P. Hougaard, M.L. Ting Lee, and G.A. Whitmore (1997). Analysis of overdispersed count data by mixtures of poisson variables and poisson processes. Biometrics 53, pages 1225-1238.
# To compute the density function in 1:100 of the Polya-Aeppli # distribution with mean = 20 and dispersion = 5 dPT(x = 1:100, mu = 20, D = 5, a = -1) # To generate 100 random counts of the same distribution with same # parameters rPT(n = 100, mu = 20, D = 5, a = -1)