train.classifier: Predict Survival with classifcation methods

In nguforche/MLSurvival: Machine Learning for Survival Analysis

Description

Train machine learning classification models on time to event data using the caret package

Usage

train_classifier(form, dat, method = "gbm", predict.times,
  trControl = NULL, parallel = FALSE, mc.cores = 2, seed = 123,
  ...)

Arguments

form	survival formula
dat	data frame
method	classifcation algorithm. The following algorithms have been implemented. glm logistic regression glmnet elastic net gbm gradient boosting machine ranger random forest svmRadial support vector machine with radial basis kernel xgbTree extreme gradient boosting machine
predict.times	survival prediction times
trControl	control parameters for the caret train function. Set to NULL to use a default 5-fold cross-validation
parallel	run cross-validation in parallel? Uses mclapply which works only on linux
...	further arguments passed to caret or other methods.
tuneLength	same as tuneLength in the caret package

Value

returns a list with items:

• finalModel: final model trained on the complete data (dat) using optimal tuning paramters

• fitted: predictions on complete data (dat)

• threshold: optimal classification threshold

• resamples: cross-validation results: predictions on resampled data

• predict.times: survival prediction times

• bestTune: optimal tuning parameters

• method: classification algorithm

nguforche/MLSurvival documentation built on July 28, 2019, 1:59 p.m.