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emil (version 1.1-6)

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

46

Version

1.1-6

License

GPL (>= 2)

Maintainer

Christofer Backlin

Last Published

August 1st, 2014

Functions in emil (1.1-6)

Trivial function

as.matrix.outcome

Convert outcome vector to matrix

Fit a linear model fitted with ordinary least squares

emil.predict.glmnet

Predict using generalized linear model with elastic net regularization

emil.vimp.randomForest

Variable importance of random forest.

Variable importance of nearest shrunken centroids.

emil.predict.lm

Prediction using linear model

Load a package and offer to install if missing

Resampling schemes

Get events on factor form

emil.predict.qda

Prediction using already trained classifier.

Wrapper for several methods to test if a variable is empty

Data preprocessing

emil.predict.cforest

Predict with conditional inference forest

Fit quadratic discriminant.

Dimension of an outcome vector

predict.modeling.procedure

Predict the response of unknown observations

Perform modeling

Convert object to Surv vector

Test if object is of class outcome

emil.predict.pamr

Prediction using nearest shrunken centroids.

emil.fit.glmnet

Fit GLM with LASSO, Ridge or elastic net regularization.

Extract a subset of a tree of nested lists

Fit linear discriminant

emil.predict.caret

Predict using a caret method

Check for missing values

Performance estimation functions

Extract p-value from a Cox proportional hazards model

resample.mapply

Compare true response to resampled predictions

Extraction of p-value from a statistical test

Fit a model using the caret package

as.data.frame.outcome

Convert outcome vector to data frame

Introduction to the emil package

Convert object to outcome vector

Replace values with something else

p.value.survdiff

Extracts p-value from a logrank test

Print outcome vector

evaluate.modeling

Performance estimation of modeling procedures

Visualize resampling scheme

emil.extensions

Extending the emil framework with user-defined methods

Negative geometric mean of class specific predictive accuracy

as.outcome.Surv

Convert Surv vector to outcome vector

Regular imputation

as.character.outcome

Convert outcome vector to character vector

Length of an outcome vector

emil.fit.randomForest

Fit random forest.

Fit nearest shrunken centroids model.

emil.predict.lda

Prediction using already trained prediction model

emil.predict.randomForest

Prediction using random forest.

Print a timestamped and indented log message

Return events in integer form

as.Surv.outcome

Convert outcome vector to Surv vector

Convert a fold to row indexes of fittdng or test set

modeling.procedure

Setup a modeling procedure

emil.fit.cforest

Fit conditional inference forest

Extract p-value from a cumulative incidence estimation

Create a vector of outcomes

Generate resampling subschemes

weighted.error.rate

Weighted error rate

Extracts p-value from a competing risk model

Print a warning message if not printed earlier

Variable importance of a fitted model

PAMR adapted dataset pre-processing

Extract and organize predictions according to a resampling scheme

Plot outcome vector

Tune parameters of modeling procedures