Transformation Models for Clustered Data
Marginally interpretable transformation models for clustered data. Highly experimental, use at your own risk.
mtram(object, formula, data, standardise = FALSE,
grd = SparseGrid::createSparseGrid(type = "KPU",
dimension = length(rt$cnms[[1]]), k = 10),
Hessian = FALSE, ...)object |
A |
formula |
A formula specifying the random effects. |
data |
A data frame. |
standardise |
Two types of models can be estimated: M1 (with |
grd |
A sparse grid used for numerical integration to get the likelihood. |
Hessian |
A logical, if |
... |
Additional argument. |
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.
An object of class tram with coef() and logLik()
methods.
Torsten Hothorn (2019). Marginally Interpretable Parametric Linear Transformation Models for Clustered Observations. Technical Report.
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