trtf (version 0.4-2)

traforest: Transformation Forests

Description

Partitioned and aggregated transformation models

Usage

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 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 traforest
logLik(object, newdata, weights = NULL, OOB = FALSE, coef = NULL,  ...)

Value

An object of class traforest with corresponding logLik and

predict methods.

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").

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

### 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))

if (FALSE) {

### 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)

}

}