tram (version 0.8-3)

mtram: 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, ...)

Value

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

methods.

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.

This is a proof-of-concept implementation. Only coef() and logLik() methods are available at the moment.

References

Luisa Barbanti and Torsten Hothorn (2023). A Transformation Perspective on Marginal and Conditional Models, Biostatistics, tools:::Rd_expr_doi("10.48550/arXiv.1910.09219").

Examples

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