PolrME: Mixed-effects Additive Transformation Models for Ordered...

In tramME: Transformation Models with Mixed Effects

Mixed-effects Additive Transformation Models for Ordered Categorical Responses

Description

Estimates mixed-effects additive transformation models for ordered categorical responses with various link functions.

Usage

PolrME(
  formula,
  data,
  subset,
  weights,
  offset,
  na.action = na.omit,
  method = c("logistic", "probit", "loglog", "cloglog"),
  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.
method	a character describing the link function.
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

Details

The transformation function is parameterized as a step function on a scale defined by the link function (see Hothorn et al., 2018).

The model extends tram::Polr with random effects and (optionally penalized) additive terms. For details on mixed-effect transformation models, see Tamasi and Hothorn (2021).

The elements of the linear predictor are parameterized with negative parameters (i.e. negative = TRUE in tram).

Value

A PolrME model object.

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>

Examples

data("soup", package = "ordinal")
m <- PolrME(SURENESS | SOUPFREQ ~ PROD + (1 | RESP/PROD),
            data = soup, method = "probit")
summary(m)

tramME documentation built on July 2, 2024, 5:07 p.m.