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

 ```1 2 3 4 5 6 7``` ```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 d.rizopoulos@erasmusmc.nl

Examples

 ```1 2 3 4 5 6``` ```## 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.