tramME: Mixed-effects Additive transformation models

Description

A general function to define and fit tramME models.

Usage

tramME(
  formula,
  data,
  subset,
  weights,
  offset,
  na.action,
  tram = NULL,
  call = NULL,
  ctm = NULL,
  smooth = NULL,
  negative = NULL,
  silent = TRUE,
  resid = FALSE,
  do_update = FALSE,
  estinit = TRUE,
  initpar = NULL,
  fixed = NULL,
  nofit = FALSE,
  control = optim_control(),
  ...
)

Arguments

formula: A formula describing the model. Smooth additive terms are defined the way as in mgcv, and random effects consistently with the notation used in lme4.
data: an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula).
subset: an optional vector specifying a subset of observations to be used in the fitting process.
weights: an optional vector of case weights to be used in the fitting process. Should be NULL or a numeric vector. If present, the weighted log-likelihood is maximised.
offset: this can be used to specify an _a priori_ known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector of length equal to the number of cases.
na.action: a function which indicates what should happen when the data contain NAs. The default is set to na.omit.
tram: Parameter vector for the tram model type.
call: The original function call (to be passed from the wrapper).
ctm: A model object of the ctm class that descibes the fixed-effects part of the tramME model.
smooth: A tramME_smooth object that describes the smooth additive elements of the tramME model.
negative: Logical; if TRUE, the model is parameterized with negative coefficinets for the elements of the linear predictor.
silent: Logical. Make TMB functionality silent.
resid: Logical. If TRUE, the score residuals are also calculated. This comes with some performance cost.
do_update: Logical. If TRUE, the model is set up so that the weights and the offsets are updateable. This comes with some performance cost.
estinit: Logical. Estimate a vector of initial values for the fixed effects parameters from a (fixed effects only) mlt model
initpar: Named list of initial parameter values, if NULL, it is ignored
fixed: a named vector of fixed regression coefficients; the names need to correspond to column names of the design matrix
nofit: logical, if TRUE, creates the model object, but does not run the optimization
control: list with controls for optimization
...: Optional arguments to tram

Warning

Typically, the tramME function shouldn't be called directly; it is only exported to allow the advanced users to define their tramME models in a more flexible way from their basic building blocks.

Details

The specific model functions (LmME, BoxCoxME, ColrME, etc.) are wrappers around this function.

For a general description of the transformation model family, see Hothorn et al. (2018), for details on the mixed-effects extension, see Tamasi and Hothorn (2021).

References

Hothorn, Torsten, Lisa Möst, and Peter Bühlmann. "Most Likely Transformations." Scandinavian Journal of Statistics 45, no. 1 (March 2018): 110–34. <doi:10.1111/sjos.12291>

Tamasi, Balint, and Torsten Hothorn. "tramME: Mixed-Effects Transformation Models Using Template Model Builder." The R Journal 13, no. 2 (2021): 398–418. <doi:10.32614/RJ-2021-075>