ctm {mlt} | R Documentation |
Conditional Transformation Models
Description
Specification of conditional transformation models
Usage
ctm(response, interacting = NULL, shifting = NULL, scaling = NULL,
scale_shift = FALSE, data = NULL,
todistr = c("Normal", "Logistic", "MinExtrVal", "MaxExtrVal",
"Exponential", "Laplace", "Cauchy"),
sumconstr = inherits(interacting, c("formula", "formula_basis")), ...)
Arguments
response |
a basis function, ie, an object of class |
interacting |
a basis function, ie, an object of class |
shifting |
a basis function, ie, an object of class |
scaling |
a basis function, ie, an object of class |
scale_shift |
a logical choosing between two different model types
in the presence of a |
data |
either a |
todistr |
a character vector describing the distribution to be transformed |
sumconstr |
a logical indicating if sum constraints shall be applied |
... |
arguments to |
Details
Specification of a transformation model of the form
(scale_shift = FALSE
) or
(scale_shift = TRUE
)
with bases (
response
), (
interacting
),
(
shifting
), and (
scaling
). All except
response
can be missing (in this case an unconditional distribution
is estimated).
This function only specifies the model which can then be fitted using
mlt
. The shift term is positive by default.
Possible choices of the distributions the model transforms to (the inverse
link functions ) include the
standard normal (
"Normal"
), the standard logistic
("Logistic"
), the standard minimum extreme value
("MinExtrVal"
, also known as Gompertz distribution), and the
standard maximum extreme value ("MaxExtrVal"
, also known as Gumbel
distribution) distributions. The exponential distribution
("Exponential"
) can be used to fit Aalen additive hazard models.
Laplace and Cauchy distributions are also available.
Value
An object of class ctm
.
References
Torsten Hothorn, Lisa Moest, Peter Buehlmann (2018), Most Likely Transformations, Scandinavian Journal of Statistics, 45(1), 110–134, doi:10.1111/sjos.12291.