traforest {trtf} | R Documentation |
Partitioned and aggregated transformation models
traforest(object, parm = 1:length(coef(object)), mltargs = list(maxit = 10000), update = TRUE, ...)
object |
an object of class |
parm |
parameters of |
mltargs |
arguments to |
update |
logical, if |
... |
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")
.
An object of class traforest
with corresponding 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: Personalised Medicine Using Partitioned and Aggregated Cox-Models ### A combination of <DOI:10.1177/0962280217693034> 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$y <- with(GBSG2, Surv(time, cens)) ### overall log-hazard ratio coef(cmod <- mlt(m, data = GBSG2))["horThyes"] ### roughly the same as coef(coxph(y ~ horTh, data = GBSG2)) ## Not run: ### estimate age-dependent Cox models (here ignoring all other covariates) ctrl <- ctree_control(minsplit = 50, minbucket = 20, mincriterion = 0) set.seed(290875) tf_cmod <- traforest(m, formula = y ~ horTh | age, control = ctrl, ntree = 50, mtry = 1, trace = TRUE, data = GBSG2) ### plot age-dependent treatment effects vs. overall treatment effect nd <- data.frame(age = 30:70) cf <- predict(tf_cmod, newdata = nd, type = "coef") nd$logHR <- sapply(cf, function(x) x["horThyes"]) plot(logHR ~ age, data = nd, pch = 19, xlab = "Age", ylab = "log-Hazard Ratio") abline(h = coef(cmod <- mlt(m, data = GBSG2))["horThyes"]) ### treatment most beneficial in very young patients ### NOTE: scale of log-hazard ratios depends on ### corresponding baseline hazard function which _differs_ ### across age; interpretation of positive / negative treatment effect is, ### however, save. ## End(Not run)