mmlt | R Documentation |
Conditional transformation models for multivariate continuous, discrete, or a mix of continuous and discrete outcomes
mmlt(..., formula = ~ 1, data, conditional = FALSE, theta = NULL, fixed = NULL,
scale = FALSE, optim = mltoptim(auglag = list(maxtry = 5)),
args = list(seed = 1, M = 1000), dofit = TRUE, domargins = TRUE)
## S3 method for class 'cmmlt'
coef(object, newdata,
type = c("all", "conditional", "Lambdapar", "Lambda", "Lambdainv",
"Precision", "PartialCorr", "Sigma", "Corr",
"Spearman", "Kendall"), fixed = TRUE,
...)
## S3 method for class 'mmmlt'
coef(object, newdata,
type = c("all", "marginal", "Lambdapar", "Lambda", "Lambdainv",
"Precision", "PartialCorr", "Sigma", "Corr",
"Spearman", "Kendall"), fixed = TRUE,
...)
## S3 method for class 'mmlt'
predict(object, newdata, margins = 1:J,
type = c("trafo", "distribution", "survivor", "density", "hazard"),
log = FALSE, args = object$args, ...)
## S3 method for class 'mmlt'
simulate(object, nsim = 1L, seed = NULL, newdata, K = 50, ...)
... |
marginal transformation models, one for each response, for
|
formula |
a model formula describing a model for the dependency
structure via the lambda parameters. The default is set to |
data |
a data.frame. |
conditional |
logical; parameters are defined conditionally (only
possible when all models are probit models). This is the default as
described by Klein et al. (2022). If |
theta |
an optional vector of starting values. |
fixed |
an optional named numeric vector of predefined parameter values
or a logical (for |
scale |
a logical indicating if (internal) scaling shall be applied to the model coefficients. |
optim |
a list of optimisers as returned by |
args |
a list of arguments for |
dofit |
logical; parameters are fitted by default, otherwise a list with log-likelihood and score function is returned. |
domargins |
logical; all model parameters are fitted by default, including the parameters of marginal models. |
object |
an object of class |
newdata |
an optional data.frame coefficients and predictions shall be computed for. |
type |
type of coefficient or prediction to be returned. |
margins |
indices defining marginal models to be evaluated. Can be
single integers giving the marginal distribution of the corresponding
variable, or multiple integers (currently only |
log |
logical; return log-probabilities or log-densities if
|
nsim |
number of samples to generate. |
seed |
optional seed for the random number generator. |
K |
number of grid points to generate. |
The function implements core functionality for fitting multivariate conditional transformation models as described by Klein et al (2020).
An object of class mmlt
with coef
and predict
methods.
Nadja Klein, Torsten Hothorn, Luisa Barbanti, Thomas Kneib (2022), Multivariate Conditional Transformation Models. Scandinavian Journal of Statistics, 49, 116–142, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/sjos.12501")}.
Torsten Hothorn (2024), On Nonparanormal Likelihoods. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2408.17346")}.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.