DerivIntSplines: Derivatives and Integrals of B-splines and Natural Cubic...

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

Description

Numerical derivatives and integrals of functions bs() and ns() at their first argument.

Usage

dns(x, df = NULL, knots = NULL, intercept = FALSE, 
    Boundary.knots = range(x), eps = 1e-03)

dbs(x, df = NULL, knots = NULL, intercept = FALSE, 
    Boundary.knots = range(x), eps = 1e-03)

ins(x, df = NULL, knots = NULL, intercept = FALSE, 
    Boundary.knots = range(x), from = 0, weight.fun = NULL, 
    integrand.fun = NULL, ...)

ibs(x, df = NULL, knots = NULL, intercept = FALSE, 
    Boundary.knots = range(x), from = 0, weight.fun = NULL, 
    integrand.fun = NULL, ...)

Arguments

x, df, knots, intercept, Boundary.knots	see the help pages of functions ns() and bs().
eps	a numeric scalar denoting the step length for the central difference approximation, which calculates the derivative.
from	a numeric scalar denoting the lower limit of the integral.
weight.fun	a function to be applied as weights.
integrand.fun	a function to be applied in the integrand.
...	extra arguments passed to weight.fun.

Value

an object of class dns, dbs, ins or ibs.

Author(s)

Dimitris Rizopoulos [e-mail]

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