gt: The Generalized Student's t Distribution

In JMbayes: Joint Modeling of Longitudinal and Time-to-Event Data under a Bayesian Approach

Description

Density, distribution function, quantile function and random generation for the generalized Student's t distribution.

Usage

dgt(x, mu = 0, sigma = 1, df = stop("no df arg"), log = FALSE)

pgt(q, mu = 0, sigma = 1, df = stop("no df arg")) 

qgt(p, mu = 0, sigma = 1, df = stop("no df arg"))

rgt(n, mu = 0, sigma = 1, df = stop("no df arg"))

Arguments

x, q	vector of quantiles.
p	vector of probabilities.
n	a numeric scalar denoting the number of observations.
mu	a vector of means.
sigma	a vector of standard deviations.
log	logical; if TRUE the density is computed in the log scale.
df	a numeric scalar denoting the degrees of freedom.

Value

dgt gives the density, pgt gives the distribution function, qgt gives the quantile function, and rgt generates random deviates.

Author(s)

Dimitris Rizopoulos [e-mail]

Examples

## Not run: 
x <- rnorm(10, mean = 10, sd = 3)
dgt(x, mu = 10, sigma = 3, df = 4)
rgt(10, mu = 10, sigma = 3, df = 4)

## End(Not run)

JMbayes documentation built on Jan. 9, 2020, 9:07 a.m.