Description Usage Arguments Details Value References Examples
Employs maximisation of the likelihood for estimation of shift transformation models
1 2 
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 
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.
An object of class stmboost
with predict
and
logLik
methods.
Torsten Hothorn (2020). Transformation Boosting Machines. Statistics and Computing, 30, 141–152.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  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 insample loglikelihood
logLik(m_mlt)
### estimate conditional transformation model
bm < stmboost(m_mlt, formula = DEXfat ~ ., data = bodyfat,
method = quote(mboost::mboost))
### insample loglikelihood (NEEDS TUNING OF mstop!)
logLik(bm)
### evaluate conditional densities for two observations
predict(bm, newdata = bodyfat[1:2,], type = "density")
}

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