emil: Evaluation of Modeling without Information Leakage

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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.

Author
Christofer Backlin [aut, cre], Mats Gustafsson [aut]
Date of publication
2016-06-21 07:48:48
Maintainer
Christofer Backlin <emil@christofer.backlin.se>
License
GPL (>= 2)
Version
2.2.6
URLs

View on CRAN

Man pages

as.modeling_procedure
Coerce to modeling procedure
dichotomize
Dichotomize time-to-event data
emil
Introduction to the emil package
error_fun
Performance estimation functions
evaluate
Evaluate a modeling procedure
extension
Extending the emil framework with user-defined methods
factor_to_logical
Convert factors to logicals
fill
Replace values with something else
fit
Fit a model
fit_caret
Fit a model using the 'caret' package
fit_cforest
Fit conditional inference forest
fit_coxph
Fit Cox proportional hazards model
fit_glmnet
Fit elastic net, LASSO or ridge regression model
fit_lda
Fit linear discriminant
fit_lm
Fit a linear model fitted with ordinary least squares
fit_naive_bayes
Fit a naive Bayes classifier
fit_pamr
Fit nearest shrunken centroids model.
fit_qda
Fit quadratic discriminant.
fit_randomForest
Fit random forest.
fit_rpart
Fit a decision tree
fit_svm
Fit a support vector machine
get_color
Get color palettes
get_importance
Feature (variable) importance of a fitted model
get_performance
Extract prediction performance
get_prediction
Extract predictions from modeling results
get_response
Extract the response from a data set
get_tuning
Extract parameter tuning statistics
image.resample
Visualize resampling scheme
importance_glmnet
Feature importance extractor for elastic net models
importance_pamr
Feature importance of nearest shrunken centroids.
importance_randomForest
Feature importance of random forest.
impute
Regular imputation
indent
Increase indentation
index_fit
Convert a fold to row indexes of fittdng or test set
is_blank
Wrapper for several methods to test if a variable is empty
is_constant
Check if an object contains more than one unique value
is_multi_procedure
Detect if modeling results contains multiple procedures
learning_curve
Learning curve analysis
list_method
List all available methods
log_message
Print a timestamped and indented log message
mode
Get the most common value
modeling_procedure
Setup a modeling procedure
na_index
Support function for identifying missing values
name_procedure
Get names for modeling procedures
neg_gmpa
Negative geometric mean of class specific predictive accuracy
nice_axis
Plots an axis the way an axis should be plotted.
nice_box
Plots a box around a plot
nice_require
Load a package and offer to install if missing
notify_once
Print a warning message if not printed earlier
pipe
Pipe operator
plot.learning_curve
Plot results from learning curve analysis
plot.Surv
Plot Surv vector
predict_caret
Predict using a 'caret' method
predict_cforest
Predict with conditional inference forest
predict_coxph
Predict using Cox proportional hazards model
predict_glmnet
Predict using generalized linear model with elastic net...
predict_lda
Prediction using already trained prediction model
predict_lm
Prediction using linear model
predict.model
Predict the response of unknown observations
predict_naive_bayes
Predict using naive Bayes model
predict_pamr
Prediction using nearest shrunken centroids.
predict_qda
Prediction using already trained classifier.
predict_randomForest
Prediction using random forest.
predict_rpart
Predict using a fitted decision tree
predict_svm
Predict using support vector machine
pre_factor_to_logical
Convert factors to logical columns
pre_impute
Basic imputation
pre_impute_df
Impute a data frame
pre_impute_knn
Nearest neighbors imputation
pre_log_message
Print log message during pre-processing
pre_pamr
PAMR adapted dataset pre-processing
pre_process
Data preprocessing
print.preprocessed_data
Print method for pre-processed data
pvalue
Extraction of p-value from a statistical test
pvalue.coxph
Extract p-value from a Cox proportional hazards model
pvalue.crr
Extracts p-value from a competing risk model
pvalue.cuminc
Extract p-value from a cumulative incidence estimation
pvalue.survdiff
Extracts p-value from a logrank test
resample
Resampling schemes
roc_curve
Calculate ROC curves
select
'emil' and 'dplyr' integration
subresample
Generate resampling subschemes
subtree
Extract a subset of a tree of nested lists
trivial_error_rate
Calculate the trivial error rate
tune
Tune parameters of modeling procedures
validate_data
Validate a pre-processed data set
vlines
Add vertical or horizontal lines to a plot
weighted_error_rate
Weighted error rate

Files in this package

emil
emil/inst
emil/inst/CITATION
emil/tests
emil/tests/testthat.R
emil/tests/testthat
emil/tests/testthat/test-methods.r
emil/tests/testthat/test-resample.r
emil/tests/testthat/test-survival.r
emil/tests/testthat/test-modeling.r
emil/tests/testthat/test-preprocess.r
emil/tests/testthat/test-cppfunction.r
emil/tests/testthat/test-extractors.r
emil/tests/testthat/test-helpers.r
emil/tests/testthat/test-procedure.r
emil/tests/testthat/test-debug.r
emil/src
emil/src/is_constant.cpp
emil/src/RcppExports.cpp
emil/NAMESPACE
emil/NEWS.md
emil/R
emil/R/lda.r
emil/R/survival.r
emil/R/learning-curve.r
emil/R/lm.r
emil/R/glmnet.r
emil/R/rpart.r
emil/R/reshape-result.r
emil/R/imputation.r
emil/R/error-functions.r
emil/R/cforest.r
emil/R/RcppExports.R
emil/R/RcppWrappers.r
emil/R/modeling.r
emil/R/modeling_procedure.r
emil/R/plotting.r
emil/R/pamr.r
emil/R/resampling.r
emil/R/preprocessing.r
emil/R/randomForest.r
emil/R/caret.r
emil/R/roc-curve.r
emil/R/e1071.r
emil/R/helpers.r
emil/R/message.r
emil/R/qda.r
emil/MD5
emil/DESCRIPTION
emil/man
emil/man/index_fit.Rd
emil/man/fit_randomForest.Rd
emil/man/pre_impute.Rd
emil/man/predict_lda.Rd
emil/man/fit.Rd
emil/man/nice_axis.Rd
emil/man/pipe.Rd
emil/man/predict_glmnet.Rd
emil/man/tune.Rd
emil/man/subtree.Rd
emil/man/predict_coxph.Rd
emil/man/predict.model.Rd
emil/man/pvalue.survdiff.Rd
emil/man/fit_pamr.Rd
emil/man/error_fun.Rd
emil/man/trivial_error_rate.Rd
emil/man/learning_curve.Rd
emil/man/weighted_error_rate.Rd
emil/man/fit_lda.Rd
emil/man/get_response.Rd
emil/man/subresample.Rd
emil/man/roc_curve.Rd
emil/man/evaluate.Rd
emil/man/get_tuning.Rd
emil/man/predict_pamr.Rd
emil/man/fit_qda.Rd
emil/man/importance_randomForest.Rd
emil/man/factor_to_logical.Rd
emil/man/print.preprocessed_data.Rd
emil/man/importance_pamr.Rd
emil/man/nice_require.Rd
emil/man/pre_pamr.Rd
emil/man/is_constant.Rd
emil/man/pvalue.crr.Rd
emil/man/is_multi_procedure.Rd
emil/man/get_prediction.Rd
emil/man/validate_data.Rd
emil/man/predict_cforest.Rd
emil/man/plot.learning_curve.Rd
emil/man/get_importance.Rd
emil/man/na_index.Rd
emil/man/dichotomize.Rd
emil/man/resample.Rd
emil/man/fit_glmnet.Rd
emil/man/pre_impute_knn.Rd
emil/man/fit_naive_bayes.Rd
emil/man/pre_log_message.Rd
emil/man/extension.Rd
emil/man/is_blank.Rd
emil/man/indent.Rd
emil/man/mode.Rd
emil/man/get_performance.Rd
emil/man/neg_gmpa.Rd
emil/man/image.resample.Rd
emil/man/predict_caret.Rd
emil/man/pre_impute_df.Rd
emil/man/predict_randomForest.Rd
emil/man/name_procedure.Rd
emil/man/impute.Rd
emil/man/vlines.Rd
emil/man/fit_cforest.Rd
emil/man/fit_rpart.Rd
emil/man/plot.Surv.Rd
emil/man/pvalue.coxph.Rd
emil/man/pre_factor_to_logical.Rd
emil/man/pre_process.Rd
emil/man/fill.Rd
emil/man/as.modeling_procedure.Rd
emil/man/modeling_procedure.Rd
emil/man/get_color.Rd
emil/man/fit_coxph.Rd
emil/man/fit_caret.Rd
emil/man/predict_naive_bayes.Rd
emil/man/notify_once.Rd
emil/man/nice_box.Rd
emil/man/importance_glmnet.Rd
emil/man/predict_qda.Rd
emil/man/pvalue.cuminc.Rd
emil/man/fit_lm.Rd
emil/man/predict_rpart.Rd
emil/man/list_method.Rd
emil/man/predict_lm.Rd
emil/man/pvalue.Rd
emil/man/log_message.Rd
emil/man/predict_svm.Rd
emil/man/select.Rd
emil/man/fit_svm.Rd
emil/man/emil.Rd