| Title: | Transformation Boosting Machines |
| Version: | 0.3-7 |
| Date: | 2025-04-09 |
| Description: | Boosting the likelihood of conditional and shift transformation models as introduced in <doi:10.1007/s11222-019-09870-4>. |
| Depends: | mlt (≥ 1.0-6), mboost (≥ 2.8-2) |
| Imports: | variables, basefun, sandwich, coneproj, methods |
| Suggests: | TH.data (≥ 1.0-9), tram (≥ 0.2-3), survival, partykit, lattice, latticeExtra, knitr, colorspace, gamlss.data, trtf |
| VignetteBuilder: | knitr |
| URL: | http://ctm.R-forge.R-project.org |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Packaged: | 2025-04-09 14:40:54 UTC; hothorn |
| Author: | Torsten Hothorn 
[aut, cre] |
| Maintainer: | Torsten Hothorn <Torsten.Hothorn@R-project.org> |
| Repository: | CRAN |
| Date/Publication: | 2025-04-09 15:00:02 UTC |
Likelihood Boosting for Conditional Transformation Models
Description
Employs maximisation of the likelihood for estimation of conditional transformation models
Usage
ctmboost(model, formula, data = list(), weights = NULL,
method = quote(mboost::mboost), ...)
Arguments
model |
an object of class |
formula |
a model formula describing how the parameters of
|
data |
an optional data frame of observations. |
weights |
an optional vector of weights. |
method |
a call to |
... |
additional arguments to |
Details
The parameters of model depend on explanatory variables in a
possibly structured additive way (see Hothorn, 2020). The number of boosting
iterations is a hyperparameter which needs careful tuning.
Value
An object of class ctmboost with predict and
logLik methods.
References
Torsten Hothorn (2020). Transformation Boosting Machines. Statistics and Computing, 30, 141–152.
Examples
if (require("TH.data") && require("tram")) {
data("bodyfat", package = "TH.data")
### estimate unconditional model
m_mlt <- BoxCox(DEXfat ~ 1, data = bodyfat, prob = c(.1, .99))
### get corresponding in-sample log-likelihood
logLik(m_mlt)
### estimate conditional transformation model
bm <- ctmboost(m_mlt, formula = DEXfat ~ ., data = bodyfat,
method = quote(mboost::mboost))
### in-sample log-likelihood (NEEDS TUNING OF mstop!)
logLik(bm)
### evaluate conditional densities for two observations
predict(bm, newdata = bodyfat[1:2,], type = "density")
}
Likelihood Boosting for Shift Transformation Models
Description
Employs maximisation of the likelihood for estimation of shift transformation models
Usage
stmboost(model, formula, data = list(), weights = NULL,
method = quote(mboost::mboost), mltargs = list(), ...)
Arguments
model |
an object of class |
formula |
a model formula describing how the parameters of
|
data |
an optional data frame of observations. |
weights |
an optional vector of weights. |
method |
a call to |
mltargs |
a list with arguments to be passed to
|
... |
additional arguments to |
Details
The parameters of model depend on explanatory variables in a
possibly structured additive way (see Hothorn, 2020). The number of boosting
iterations is a hyperparameter which needs careful tuning.
Value
An object of class stmboost with predict and
logLik methods.
References
Torsten Hothorn (2020). Transformation Boosting Machines. Statistics and Computing, 30, 141–152.
Examples
if (require("TH.data") && require("tram")) {
data("bodyfat", package = "TH.data")
### estimate unconditional model
m_mlt <- BoxCox(DEXfat ~ 1, data = bodyfat, prob = c(.1, .99))
### get corresponding in-sample log-likelihood
logLik(m_mlt)
### estimate conditional transformation model
bm <- stmboost(m_mlt, formula = DEXfat ~ ., data = bodyfat,
method = quote(mboost::mboost))
### in-sample log-likelihood (NEEDS TUNING OF mstop!)
logLik(bm)
### evaluate conditional densities for two observations
predict(bm, newdata = bodyfat[1:2,], type = "density")
}