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emil (version 2.2.3)

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

2.2.3

License

GPL (>= 2)

Maintainer

Christofer Backlin

Last Published

November 19th, 2015

Functions in emil (2.2.3)

get_prediction

Extract predictions from modeling results
trivial_error_rate

Calculate the trivial error rate
pvalue

Extraction of p-value from a statistical test
fit

Fit a model
fill

Replace values with something else
fit_cforest

Fit conditional inference forest
is_blank

Wrapper for several methods to test if a variable is empty
fit_caret

Fit a model using the caret package
fit_qda

Fit quadratic discriminant.
emil

Introduction to the emil package
get_tuning

Extract parameter tuning statistics
factor_to_logical

Convert factors to logicals
list_method

List all available methods
evaluate

Evaluate a modeling procedure
is_constant

Check if an object contains more than one unique value
log_message

Print a timestamped and indented log message
as.modeling_procedure

Coerce to modeling procedure
plot.Surv

Plot Surv vector
learning_curve

Learning curve analysis
is_multi_procedure

Detect if modeling results contains multiple procedures
fit_lm

Fit a linear model fitted with ordinary least squares
predict_rpart

Predict using a fitted decision tree
dichotomize

Dichotomize time-to-event data
predict_pamr

Prediction using nearest shrunken centroids.
pre_impute_knn

Nearest neighbors imputation
fit_rpart

Fit a decision tree
impute

Regular imputation
importance_pamr

Feature importance of nearest shrunken centroids.
fit_naive_bayes

Fit a naive Bayes classifier
pre_pamr

PAMR adapted dataset pre-processing
fit_lda

Fit linear discriminant
fit_randomForest

Fit random forest.
get_performance

Extract prediction performance
predict_randomForest

Prediction using random forest.
get_response

Extract the response from a data set
pvalue.coxph

Extract p-value from a Cox proportional hazards model
image.resample

Visualize resampling scheme
nice_require

Load a package and offer to install if missing
predict_qda

Prediction using already trained classifier.
weighted_error_rate

Weighted error rate
fit_glmnet

Fit elastic net, LASSO or ridge regression model
mode

Get the most common value
print.preprocessed_data

Print method for pre-processed data
pvalue.cuminc

Extract p-value from a cumulative incidence estimation
fit_svm

Fit a support vector machine
fit_coxph

Fit Cox proportional hazards model
predict_lda

Prediction using already trained prediction model
predict_cforest

Predict with conditional inference forest
importance_glmnet

Feature importance extractor for elastic net models
pvalue.survdiff

Extracts p-value from a logrank test
predict_caret

Predict using a caret method
resample

Resampling schemes
pre_impute

Basic imputation
predict_naive_bayes

Predict using naive Bayes model
name_procedure

Get names for modeling procedures
vlines

Add vertical or horizontal lines to a plot
predict_coxph

Predict using Cox proportional hazards model
plot.learning_curve

Plot results from learning curve analysis
nice_box

Plots a box around a plot
select

emil and dplyr integration
validate_data

Validate a pre-processed data set
predict_glmnet

Predict using generalized linear model with elastic net regularization
notify_once

Print a warning message if not printed earlier
indent

Increase indentation
pre_log_message

Print log message during pre-processing
%>%

Pipe operator
nice_axis

Plots an axis the way an axis should be plotted.
importance_randomForest

Feature importance of random forest.
pvalue.crr

Extracts p-value from a competing risk model
get_color

Get color palettes
predict_svm

Predict using support vector machine
get_importance

Feature (variable) importance of a fitted model
subresample

Generate resampling subschemes
tune

Tune parameters of modeling procedures
subtree

Extract a subset of a tree of nested lists
neg_gmpa

Negative geometric mean of class specific predictive accuracy
index_fit

Convert a fold to row indexes of fittdng or test set
predict_lm

Prediction using linear model
modeling_procedure

Setup a modeling procedure
pre_process

Data preprocessing
extension

Extending the emil framework with user-defined methods
pre_factor_to_logical

Convert factors to logical columns
roc_curve

Calculate ROC curves
predict.model

Predict the response of unknown observations
na_index

Support function for identifying missing values
pre_impute_df

Impute a data frame
error_fun

Performance estimation functions
fit_pamr

Fit nearest shrunken centroids model.