# Transformation Forests Partitioned and aggregated transformation models ```r traforest(object, parm = 1:length(coef(object)), reparm = NULL, intercept = c("none", "shift", "scale", "shift-scale"), update = TRUE, min_update = length(coef(object)) * 2, mltargs = list(), ...) ## S3 method for class 'traforest' predict(object, newdata, mnewdata = data.frame(1), K = 20, q = NULL, type = c("weights", "node", "coef", "trafo", "distribution", "survivor", "density", "logdensity", "hazard", "loghazard", "cumhazard", "quantile"), OOB = FALSE, simplify = FALSE, trace = FALSE, updatestart = FALSE, applyfun = NULL, cores = NULL, ...) ## S3 method for class 'traforest' logLik(object, newdata, weights = NULL, OOB = FALSE, coef = NULL, ...) ``` ## Arguments - `object`: an object of class `ctm` or `mlt` specifying the abstract model to be partitioned. - `parm`: parameters of `object` those corresponding score is used for finding partitions. - `reparm`: optional matrix of contrasts for reparameterisation of the scores. `teststat = "quadratic"` is invariant to this operation but `teststat = "max"` might be more powerful for example when formulating an implicit into an explicit intercept term. - `intercept`: add optional intercept parameters (constraint to zero) to the model. - `mltargs`: arguments to `mlt` for fitting the transformation models. - `update`: logical, if `TRUE`, models and thus scores are updated in every node. If `FALSE`, the model and scores are computed once in the root node. The latter option is faster but less accurate. - `min_update`: number of observations necessary to refit the model in a node. If less observations are available, the parameters from the parent node will be reused. - `newdata`: an optional data frame of observations for the forest. - `mnewdata`: an optional data frame of observations for the model. - `K`: number of grid points to generate (in the absence of `q`). - `q`: quantiles at which to evaluate the model. - `type`: type of prediction or plot to generate. - `OOB`: compute out-of-bag predictions. - `simplify`: simplify predictions (if possible). - `trace`: a logical indicating if a progress bar shall be printed while the predictions are computed. - `updatestart`: try to be smart about starting values for computing predictions (experimental). - `applyfun`: an optional `lapply`-style function with arguments `function(X, FUN, ...)` for looping over `newdata`. The default is to use the basic `lapply` function unless the `cores` argument is specified (see below). - `cores`: numeric. If set to an integer the `applyfun` is set to `mclapply` with the desired number of `cores`. - `weights`: an optional vector of weights. - `coef`: an optional matrix of precomputed coefficients for `newdata` (using `predict`). Helps to compute the coefficients once for later reuse (different weights, for example). - ``...``: arguments to `cforest`, at least `formula` and `data`. ## Details 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")`. ## Returns An object of class `traforest` with corresponding `logLik` and `predict` methods. ## References Torsten Hothorn and Achim Zeileis (2021). Predictive Distribution Modelling Using Transformation Forests. **Journal of Computational and Graphical Statistics**, tools:::Rd_expr_doi("10.1080/10618600.2021.1872581") . Torsten Hothorn (2018). Top-Down Transformation Choice. **Statistical Modelling**, 3-4 , 274-298. tools:::Rd_expr_doi("10.1177/1471082X17748081") . Natalia Korepanova, Heidi Seibold, Verena Steffen and Torsten Hothorn (2019). Survival Forests under Test: Impact of the Proportional Hazards Assumption on Prognostic and Predictive Forests for ALS Survival. tools:::Rd_expr_doi("10.1177/0962280219862586") . ## Examples ```r ### Example: Personalised Medicine Using Partitioned and Aggregated Cox-Models ### A combination of and ### 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") GBSG2$y <- with(GBSG2, Surv(time, cens)) ### set-up Cox model with overall treatment effect in hormonal therapy cmod <- Coxph(y ~ horTh, data = GBSG2, support = c(100, 2000), order = 5) ### overall log-hazard ratio coef(cmod) ### roughly the same as coef(coxph(y ~ horTh, data = GBSG2)) ## Not run: ### estimate age-dependent Cox models (here ignoring all other covariates) ctrl <- ctree_control(minsplit = 50, minbucket = 20, mincriterion = 0) set.seed(290875) tf_cmod <- traforest(cmod, formula = y ~ horTh | age, control = ctrl, ntree = 50, mtry = 1, trace = TRUE, data = GBSG2) ### plot age-dependent treatment effects vs. overall treatment effect nd <- data.frame(age = 30:70) cf <- predict(tf_cmod, newdata = nd, type = "coef") nd$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. ### mclapply doesn't work in Windows if (.Platform$OS.type != "windows") { ### computing predictions: predicted coefficients cf1 <- predict(tf_cmod, newdata = nd, type = "coef") ### speedup with plenty of RAM and 4 cores cf2 <- predict(tf_cmod, newdata = nd, cores = 4, type = "coef") ### memory-efficient with low RAM and _one_ core cf3 <- predict(tf_cmod, newdata = nd, cores = 4, applyfun = lapply, type = "coef") all.equal(cf1, cf2) all.equal(cf1, cf3) } ## End(Not run) ```

traforest function