Survival Conditional Inference Tree Learner

Survival Partition Tree where a significance test is used to determine the univariate splits. Calls partykit::ctree() from partykit.

Prediction types

This learner returns two prediction types:

1. distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using the internal partykit::predict.party() function.

2. crank: the expected mortality using mlr3proba::.surv_return().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.ctree")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”

• Feature Types: “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3proba, mlr3extralearners, partykit, coin, sandwich

Parameters

Id	Type	Default	Levels	Range
teststat	character	quadratic	quadratic, maximum	-
splitstat	character	quadratic	quadratic, maximum	-
splittest	logical	FALSE	TRUE, FALSE	-
testtype	character	Bonferroni	Bonferroni, MonteCarlo, Univariate, Teststatistic	-
nmax	untyped	-		-
alpha	numeric	0.05		\([0, 1]\)
mincriterion	numeric	0.95		\([0, 1]\)
logmincriterion	numeric	-		\((-\infty, \infty)\)
minsplit	integer	20		\([1, \infty)\)
minbucket	integer	7		\([1, \infty)\)
minprob	numeric	0.01		\([0, \infty)\)
stump	logical	FALSE	TRUE, FALSE	-
lookahead	logical	FALSE	TRUE, FALSE	-
MIA	logical	FALSE	TRUE, FALSE	-
nresample	integer	9999		\([1, \infty)\)
tol	numeric	-		\([0, \infty)\)
maxsurrogate	integer	0		\([0, \infty)\)
numsurrogate	logical	FALSE	TRUE, FALSE	-
mtry	integer	Inf		\([0, \infty)\)
maxdepth	integer	Inf		\([0, \infty)\)
maxvar	integer	-		\([1, \infty)\)
multiway	logical	FALSE	TRUE, FALSE	-
splittry	integer	2		\([0, \infty)\)
intersplit	logical	FALSE	TRUE, FALSE	-
majority	logical	FALSE	TRUE, FALSE	-
caseweights	logical	FALSE	TRUE, FALSE	-
applyfun	untyped	-		-
cores	integer	NULL		\((-\infty, \infty)\)
saveinfo	logical	TRUE	TRUE, FALSE	-
update	logical	FALSE	TRUE, FALSE	-
splitflavour	character	ctree	ctree, exhaustive	-
cluster	untyped	-		-
scores	untyped	-		-
doFit	logical	TRUE	TRUE, FALSE	-
maxpts	integer	25000		\((-\infty, \infty)\)
abseps	numeric	0.001		\([0, \infty)\)
releps	numeric	0		\([0, \infty)\)

References

Hothorn T, Zeileis A (2015). “partykit: A Modular Toolkit for Recursive Partytioning in R.” Journal of Machine Learning Research, 16(118), 3905-3909. http://jmlr.org/papers/v16/hothorn15a.html.

Hothorn T, Hornik K, Zeileis A (2006). “Unbiased Recursive Partitioning: A Conditional Inference Framework.” Journal of Computational and Graphical Statistics, 15(3), 651–674. doi:10.1198/106186006x133933 , https://doi.org/10.1198/106186006x133933.

See also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

adibender

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCTree

Methods

Public methods

• LearnerSurvCTree$new()

• LearnerSurvCTree$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvCTree$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvCTree$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("surv.ctree")
print(learner)
#> 
#> ── <LearnerSurvCTree> (surv.ctree): Conditional Inference Tree ─────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3proba, mlr3extralearners, partykit, coin, and sandwich
#> • Predict Types: [crank] and distr
#> • Feature Types: integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: weights
#> • Other settings: use_weights = 'use'

# Define a Task
task = tsk("grace")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
#> 
#> Model formula:
#> Surv(time, status, type = "right") ~ age + los + revasc + revascdays + 
#>     stchange + sysbp
#> 
#> Fitted party:
#> [1] root
#> |   [2] age <= 71
#> |   |   [3] revascdays <= 171
#> |   |   |   [4] revasc <= 0
#> |   |   |   |   [5] los <= 2: 2.000 (n = 21)
#> |   |   |   |   [6] los > 2
#> |   |   |   |   |   [7] revascdays <= 16: 16.000 (n = 34)
#> |   |   |   |   |   [8] revascdays > 16: 79.000 (n = 18)
#> |   |   |   [9] revasc > 0
#> |   |   |   |   [10] age <= 63: Inf (n = 145)
#> |   |   |   |   [11] age > 63: Inf (n = 78)
#> |   |   [12] revascdays > 171: Inf (n = 89)
#> |   [13] age > 71
#> |   |   [14] revascdays <= 169
#> |   |   |   [15] revasc <= 0: 29.000 (n = 111)
#> |   |   |   [16] revasc > 0: Inf (n = 117)
#> |   |   [17] revascdays > 169: Inf (n = 57)
#> 
#> Number of inner nodes:    8
#> Number of terminal nodes: 9


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> surv.cindex 
#>   0.8237039