mtram: Transformation Models for Clustered Data

In tram: Transformation Models

Transformation Models for Clustered Data

Description

Marginally interpretable transformation models for clustered data.

Usage

mtram(object, formula, data,
      grd = SparseGrid::createSparseGrid(type = "KPU", 
                dimension = length(rt$cnms[[1]]), k = 10), 
      Hessian = FALSE,  tol = .Machine$double.eps, ...)

Arguments

object	A tram object.
formula	A formula specifying the random effects.
data	A data frame.
grd	A sparse grid used for numerical integration to get the likelihood.
Hessian	A logical, if TRUE, the hessian is computed and returned.
tol	numerical tolerance.
...	Additional argument.

Details

A Gaussian copula with a correlation structure obtained from a random intercept or random intercept / random slope model (that is, clustered or longitudinal data can by modelled only) is used to capture the correlations whereas the marginal distributions are described by a transformation model. The methodology is described in Barbanti and Hothorn (2022) and examples are given in the mtram package vignette.

Only coef() and logLik() methods are available at the moment, see vignette("mtram", package = "tram") for worked examples.

Value

An object of class tram with coef() and logLik() methods.

References

Luisa Barbanti and Torsten Hothorn (2023). A Transformation Perspective on Marginal and Conditional Models, Biostatistics, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/biostatistics/kxac048")}.

Examples

  ### For illustrations see
  ## vignette("mtram", package = "tram")
  ## or
  ## demo("mtram", package = "tram")

tram documentation built on Aug. 25, 2023, 5:15 p.m.