trafotree {trtf} | R Documentation |
Partitioned transformation models
trafotree(object, parm = 1:length(coef(object)), mltargs = list(maxit = 10000), ...)
object |
an object of class |
parm |
parameters of |
mltargs |
arguments to |
... |
arguments to |
Conditional inference trees are used for partitioning likelihood-based transformation
models as described in Hothorn and Zeileis (2017). The method can be seen
in action in Hothorn (2018) and the corresponding code is available as
demo("BMI")
. demo("applications")
performs transformation
tree analyses for some standard benchmarking problems.
An object of class trafotree
with corresponding plot
, logLik
and
predict
methods.
Torsten Hothorn and Achim Zeileis (2017). Transformation Forests. https://arxiv.org/abs/1701.02110.
Torsten Hothorn (2018). Top-Down Transformation Choice. Statistical Modelling, https://arxiv.org/abs/1706.08269.
### Example: Stratified Medicine Using Partitioned Cox-Models ### A combination of <DOI:10.1515/ijb-2015-0032> and <arXiv:1701.02110> ### based on infrastructure in the mlt R add-on package described in ### https://cran.r-project.org/web/packages/mlt.docreg/vignettes/mlt.pdf library("trtf") library("survival") ### German Breast Cancer Study Group 2 data set data("GBSG2", package = "TH.data") ### set-up Cox model with overall treatment effect in hormonal therapy yvar <- numeric_var("y", support = c(100, 2000), bounds = c(0, Inf)) By <- Bernstein_basis(yvar, order = 5, ui = "incre") m <- ctm(response = By, shifting = ~ horTh, todistr = "MinExt", data = GBSG2) GBSG2$y <- with(GBSG2, Surv(time, cens)) ### overall log-hazard ratio coef(cmod <- mlt(m, data = GBSG2))["horThyes"] ### roughly the same as coef(coxph(y ~ horTh, data = GBSG2)) ### partition the model, ie both the baseline hazard function AND the ### treatment effect (part_cmod <- trafotree(m, formula = y ~ horTh | age + menostat + tsize + tgrade + pnodes + progrec + estrec, data = GBSG2)) ### compare the log-likelihoods logLik(cmod) logLik(part_cmod) ### stronger effects in nodes 2 and 4 and no effect in node 5 coef(part_cmod)[, "horThyes"] ### plot the conditional survivor functions; blue is untreated ### and green is hormonal therapy nd <- data.frame(horTh = sort(unique(GBSG2$horTh))) plot(part_cmod, newdata = nd, tp_args = list(type = "survivor", col = c("cadetblue3", "chartreuse4")))