Type: Package
Title: Bagged k-Nearest Neighbors Survival Prediction
Version: 0.1.5
Date: 2017-05-10
Author: Marvin N. Wright
Maintainer: Marvin N. Wright <marv@wrig.de>
Description: Implements a bootstrap aggregated (bagged) version of the k-nearest neighbors survival probability prediction method (Lowsky et al. 2013). In addition to the bootstrapping of training samples, the features can be subsampled in each baselearner to break the correlation between them. The Rcpp package is used to speed up the computation.
Imports: prodlim, pec, Rcpp (≥ 0.11.2), parallel, methods
LinkingTo: Rcpp
Suggests: survival, testthat
License: GPL-3
RoxygenNote: 5.0.1
NeedsCompilation: yes
Packaged: 2017-05-10 13:17:38 UTC; wright
Repository: CRAN
Date/Publication: 2017-05-10 15:37:49 UTC

Bagged k-nearest neighbors survival prediction

Description

Bootstrap aggregated (bagged) version of the k-nearest neighbors survival probability prediction method (Lowsky et al. 2013). In addition to the bootstrapping of training samples, the features can be subsampled in each base learner.

Usage

bnnSurvival(formula, data, k = max(1, nrow(data)/10),
  num_base_learners = 50, num_features_per_base_learner = NULL,
  metric = "mahalanobis", weighting_function = function(x) {     x * 0 + 1
  }, replace = TRUE, sample_fraction = NULL)

Arguments

formula

Object of class formula or character describing the model to fit.

data

Training data of class data.frame.

k

Number nearest neighbors to use. If a vector is given, the optimal k of these values is found using 5-fold cross validation.

num_base_learners

Number of base learners to use for bootstrapping.

num_features_per_base_learner

Number of features randomly selected in each base learner. Default: all.

metric

Metric d(x,y) used to measure the distance between observations. Currently only "mahalanobis".

weighting_function

Weighting function w(d(,x,y)) used to weight the observations based on their distance.

replace

Sample with or without replacement.

sample_fraction

Fraction of observations to sample in [0,1]. Default is 1 for replace=TRUE, and 0.6321 for replace=FALSE.

Details

For a description of the k-nearest neighbors survival probability prediction method see (Lowsky et al. 2013). Please note, that parallel processing, as currently implemented, does not work on Microsoft Windows platforms.

The weighting function needs to be defined for all distances >= 0. The default function is constant 1, a possible alternative is w(x) = 1/(1+x).

To use the non-bagged version as in Lowsky et al. 2013, use num_base_learners=1, replace=FALSE and sample_fraction=1.

Value

bnnSurvivalEnsemble object. Use predict() with a new data set to predict survival probabilites.

Author(s)

Marvin N. Wright

References

Lowsky, D.J. et al. (2013). A K-nearest neighbors survival probability prediction method. Stat Med, 32(12), 2062-2069.

See Also

predict

Examples

require(bnnSurvival)

## Use only 1 core
options(mc.cores = 1)

## Load a dataset and split in training and test data
require(survival)
n <- nrow(veteran)
idx <- sample(n, 2/3*n)
train_data <- veteran[idx, ]
test_data <- veteran[-idx, ]

## Create model with training data and predict for test data
model <- bnnSurvival(Surv(time, status) ~ trt + karno + diagtime + age + prior, train_data, 
                     k = 20, num_base_learners = 10, num_features_per_base_learner = 3)
result <- predict(model, test_data)

## Plot survival curve for the first observations
plot(timepoints(result), predictions(result)[1, ])

Get optimal number of neighbors

Description

Get optimal number of neighbors for bnnSurvival by cross validation

Usage

get_best_k(formula, data, k, ...)

Arguments

formula

Formula

data

Data

k

Number of neighbors

...

Further arguments passed to bnnSurvival

Value

Optimal k

Compute prediction for all samples.

Description

Compute prediction for all samples.

Usage

## S4 method for signature 'bnnSurvivalBaseLearner'
predict(object, train_data, test_data,
  timepoints, metric, weighting_function, k)

Arguments

object

bnnSurvivalBaseLearner object

train_data

Training data (with response)

test_data

Test data (without response)

timepoints

Timepoint to predict at

metric

Metric used

weighting_function

Weighting function used

k

Number of nearest neighbors

Predict survival probabilities with bagged k-nearest neighbors survival prediction.

Description

Predict survival probabilities with bagged k-nearest neighbors survival prediction.

Usage

## S4 method for signature 'bnnSurvivalEnsemble'
predict(object, test_data)

Arguments

object

Object of class bnnSurvivalEnsemble, created with bnnSurvival().

test_data

Data set containing data to predict survival.

Function to extract survival probability predictions from bnnSurvivalEnsemble. Use with pec package.

Description

Function to extract survival probability predictions from bnnSurvivalEnsemble. Use with pec package.

Usage

## S3 method for class 'bnnSurvivalEnsemble'
predictSurvProb(object, newdata, times, ...)

Arguments

object

bnnSurvivalEnsemble object.

newdata

Data used for prediction.

times

Not used.

...

Not used.

Value

survival probability predictions

Get Predictions

Description

Get Predictions

Usage

predictions(object, ...)

Arguments

object

Object to extract predictions from

...

further arguments passed to or from other methods.

Get Predictions

Description

Get Predictions

Usage

## S4 method for signature 'bnnSurvivalResult'
predictions(object)

Arguments

object

bnnSurvivalResult object to extract predictions from

Generic print method for bnnSurvivalEnsemble

Description

Generic print method for bnnSurvivalEnsemble

Usage

## S4 method for signature 'bnnSurvivalEnsemble'
print(x)

Arguments

x

bnnSurvivalEnsemble object to print

Generic print method for bnnSurvivalResult

Description

Generic print method for bnnSurvivalResult

Usage

## S4 method for signature 'bnnSurvivalResult'
print(x)

Arguments

x

bnnSurvivalResult object to print

Generic show method for bnnSurvivalEnsemble

Description

Generic show method for bnnSurvivalEnsemble

Usage

## S4 method for signature 'bnnSurvivalEnsemble'
show(object)

Arguments

object

bnnSurvivalEnsemble object to show

Generic show method for bnnSurvivalResult

Description

Generic show method for bnnSurvivalResult

Usage

## S4 method for signature 'bnnSurvivalResult'
show(object)

Arguments

object

bnnSurvivalResult object to show

Get Timepoints

Description

Get Timepoints

Usage

timepoints(object, ...)

Arguments

object

Object to extract timepoints from

...

further arguments passed to or from other methods.

Get timepoints

Description

Get timepoints

Usage

## S4 method for signature 'bnnSurvivalResult'
timepoints(object)

Arguments

object

bnnSurvivalResult object to extract timepoints from