mtram: Transformation Models for Clustered Data

Description Usage Arguments Details Value References

View source: R/mtram.R

Description

Marginally interpretable transformation models for clustered data. Highly experimental, use at your own risk.

Usage

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

Arguments

object

A tram object.

formula

A formula specifying the random effects.

data

A data frame.

standardise

Two types of models can be estimated: M1 (with standardise = FALSE) corresponds to a marginal distribution function without direct interpretation of the fixed effects, M2 (with standardise = TRUE) allows a marginal interpretation of scaled fixed effects as log-odds or log-hazard ratios (depending on object). See Hothorn (2019).

grd

A sparse grid used for numerical integration to get the likelihood.

Hessian

A logical, if TRUE, the hessian is computed and returned.

...

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 Hothorn (2019) and examples are given in the mtram package vignette.

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

Value

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

References

Torsten Hothorn (2019). Marginally Interpretable Parametric Linear Transformation Models for Clustered Observations. Technical Report.


tram documentation built on March 26, 2020, 7:42 p.m.