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emil (version 2.1.1)

Evaluation of Modeling without Information Leakage

Description

A toolbox for designing and evaluating predictive models with resampling methods. The aim of this package is to provide a simple and efficient general framework for working with any type of prediction problem, be it classification, regression or survival analysis, that is easy to extend and adapt to your specific setting. Some commonly used methods for classification, regression and survival analysis are included.

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Version

Install

install.packages('emil')

Monthly Downloads

1

Version

2.1.1

License

GPL (>= 2)

Maintainer

Christofer Backlin

Last Published

July 9th, 2015

Functions in emil (2.1.1)

Performance estimation functions

as.modeling_procedure

Coerce to modeling procedure

Extending the emil framework with user-defined methods

importance_glmnet

Feature importance extractor for elastic net models

Extract predictions from modeling results

Extract parameter tuning statistics

Regular imputation

Convert a fold to row indexes of fittdng or test set

Introduction to the emil package

Fit conditional inference forest

predict_cforest

Predict with conditional inference forest

Support function for identifying missing values

Dichotomize time-to-event data

Extract the response from a data set

Fit linear discriminant

PAMR adapted dataset pre-processing

Fit a decision tree

Fit elastic net, LASSO or ridge regression model

Extracts p-value from a competing risk model

Increase indentation

Nearest neighbors imputation

Load a package and offer to install if missing

Prediction using already trained prediction model

Extract p-value from a Cox proportional hazards model

Predict the response of unknown observations

Wrapper for several methods to test if a variable is empty

Feature (variable) importance of a fitted model

Fit a linear model fitted with ordinary least squares

Print a warning message if not printed earlier

print.preprocessed_data

Print method for pre-processed data

Extract a subset of a tree of nested lists

Print a timestamped and indented log message

fit_randomForest

Fit random forest.

Plots an axis the way an axis should be plotted.

Fit a model using the caret package

Add vertical or horizontal lines to a plot

Basic imputation

Get color palettes

Get names for modeling procedures

is_multi_procedure

Detect if modeling results contains multiple procedures

Learning curve analysis

Resampling schemes

Prediction using nearest shrunken centroids.

get_performance

Extract prediction performance

Predict using generalized linear model with elastic net regularization

Extract p-value from a cumulative incidence estimation

Plots a box around a plot

plot.learning_curve

Plot results from learning curve analysis

emil and dplyr integration

Generate resampling subschemes

List all available methods

Replace values with something else

Predict using Cox proportional hazards model

pvalue.survdiff

Extracts p-value from a logrank test

Calculate ROC curves

Fit Cox proportional hazards model

Visualize resampling scheme

Prediction using already trained classifier.

Predict using a fitted decision tree

Negative geometric mean of class specific predictive accuracy

Prediction using linear model

Data preprocessing

importance_pamr

Feature importance of nearest shrunken centroids.

modeling_procedure

Setup a modeling procedure

Tune parameters of modeling procedures

Fit quadratic discriminant.

Plot Surv vector

importance_randomForest

Feature importance of random forest.

Evaluate a modeling procedure

weighted_error_rate

Weighted error rate

Predict using a caret method

predict_randomForest

Prediction using random forest.

Fit nearest shrunken centroids model.

Extraction of p-value from a statistical test