legendre.quadrature.rule.200

Legendre quadrature rule for n=200.

datasets

R implementation of generalized survival models (GSMs) and smooth accelerated failure time (AFT) models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth. For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x).

Mark Clements

rstpm2

Generalized Survival Models

Xing-Rong Liu

Paul Lambert

Lasse Hjort Jakobsen

Alessandro Gasparini

Gordon Smyth

Patrick Alken

Simon Wood

Rhys Ulerich

legendre.quadrature.rule.200 function

A data frame with 200 observations on the following 2 variables.<dl class="dl-horizontal">
<dt><code>x</code></dt><dd>x values between -1 and 1</dd>
<dt><code>w</code></dt><dd>weights</dd>
 </dl>

Format

Legendre quadrature rule for n=200. — legendre.quadrature.rule.200

A data frame with 200 observations on the following 2 variables.<dl class='dl-horizontal'>
<dt><code>x</code></dt><dd>x values between -1 and 1</dd>
<dt><code>w</code></dt><dd>weights</dd>
 </dl>

Legendre quadrature rule for n=200.

legendre.quadrature.rule.200: Legendre quadrature rule for n=200.

Description

Usage

Arguments

Format

Examples