emil: Evaluation of Modeling without Information Leakage
Version 2.2.6

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.

AuthorChristofer Backlin [aut, cre], Mats Gustafsson [aut]
Date of publication2016-06-21 07:48:48
MaintainerChristofer Backlin <emil@christofer.backlin.se>
LicenseGPL (>= 2)
Version2.2.6
URL https://github.com/Molmed/emil
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("emil")

Popular man pages

dichotomize: Dichotomize time-to-event data
emil: Introduction to the emil package
fit_glmnet: Fit elastic net, LASSO or ridge regression model
pre_factor_to_logical: Convert factors to logical columns
pvalue.coxph: Extract p-value from a Cox proportional hazards model
pvalue.survdiff: Extracts p-value from a logrank test
tune: Tune parameters of modeling procedures
See all...

All man pages Function index File listing

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

Functions

Surv_event_types Source code
\%>\% Man page
as.data.frame.roc_curve Man page Source code
as.data.table.roc_curve Man page Source code
as.modeling_procedure Man page Source code
as.modeling_procedure.character Source code
as.modeling_procedure.default Source code
as.modeling_procedure.modeling_procedure Source code
debug_flag Source code
detune Man page Source code
dichotomize Man page Source code
emil Man page
error_fun Man page
error_rate Man page Source code
evaluate Man page Source code
example_string Source code
extension Man page
factor_to_logical Man page Source code
fill Man page Source code
fit Man page Source code
fit_caret Man page Source code
fit_cforest Man page Source code
fit_coxph Man page Source code
fit_glmnet Man page Source code
fit_lasso Man page Source code
fit_lda Man page Source code
fit_lm Man page Source code
fit_naive_bayes Man page Source code
fit_pamr Man page Source code
fit_qda Man page Source code
fit_randomForest Man page Source code
fit_ridge_regression Man page Source code
fit_rpart Man page Source code
fit_svm Man page Source code
get_color Man page Source code
get_color.default Man page Source code
get_color.factor Man page Source code
get_importance Man page Source code
get_importance.list Source code
get_importance.model Source code
get_importance.modeling_result Source code
get_performance Man page Source code
get_prediction Man page Source code
get_response Man page Source code
get_response.character Source code
get_response.default Source code
get_response.formula Source code
get_tuning Man page Source code
get_tuning.model Source code
get_tuning.modeling_procedure Source code
get_tuning.modeling_result Source code
hlines Man page Source code
image.crossvalidation Man page Source code
image.resample Man page Source code
importance_glmnet Man page Source code
importance_lasso Man page
importance_pamr Man page Source code
importance_randomForest Man page Source code
importance_ridge_regression Man page
impute Man page
impute_knn Man page Source code
impute_median Man page Source code
indent Man page Source code
index_fit Man page Source code
index_test Man page Source code
is_blank Man page Source code
is_constant Man page Source code
is_constant_character Source code
is_constant_complex Source code
is_constant_numeric Source code
is_multi_procedure Man page Source code
is_tunable Man page Source code
is_tuned Man page Source code
learning_curve Man page Source code
list_method Man page Source code
log_message Man page Source code
logical_subset Source code
logical_subset.default Source code
logical_subset.fold Source code
mode Man page Source code
modeling_procedure Man page Source code
mse Man page Source code
multify Source code
na_fill Man page Source code
na_index Man page Source code
name_procedure Man page Source code
neg_auc Man page Source code
neg_gmpa Man page Source code
neg_harrell_c Man page Source code
nice_axis Man page Source code
nice_box Man page Source code
nice_require Man page Source code
notify_once Man page Source code
plot.Surv Man page Source code
plot.learning_curve Man page Source code
plot.roc_curve Man page Source code
positive_integer_subset Source code
positive_integer_subset.default Source code
positive_integer_subset.fold Source code
pre_center Man page Source code
pre_convert Man page Source code
pre_factor_to_logical Man page Source code
pre_impute Man page Source code
pre_impute_df Man page Source code
pre_impute_knn Man page Source code
pre_impute_mean Man page Source code
pre_impute_median Man page Source code
pre_log_message Man page Source code
pre_pamr Man page Source code
pre_pca Man page Source code
pre_process Man page
pre_remove Man page Source code
pre_remove_constant Man page Source code
pre_remove_correlated Man page Source code
pre_scale Man page Source code
pre_split Man page Source code
pre_transpose Man page Source code
predict.model Man page Source code
predict_caret Man page Source code
predict_cforest Man page Source code
predict_coxph Man page Source code
predict_glmnet Man page Source code
predict_lasso Man page
predict_lda Man page Source code
predict_lm Man page Source code
predict_naive_bayes Man page Source code
predict_pamr Man page Source code
predict_qda Man page Source code
predict_randomForest Man page Source code
predict_ridge_regression Man page
predict_rpart Man page Source code
predict_svm Man page Source code
print.modeling_procedure Source code
print.preprocessed_data Man page Source code
pvalue Man page Source code
pvalue.coxph Man page Source code
pvalue.crr Man page Source code
pvalue.cuminc Man page Source code
pvalue.survdiff Man page Source code
resample Man page Source code
resample_bootstrap Man page Source code
resample_crossvalidation Man page Source code
resample_holdout Man page Source code
reset_notification Man page Source code
rmse Man page Source code
roc_curve Man page Source code
select Man page
select_.list Man page Source code
select_.modeling_result Man page Source code
select_list Source code
subresample Man page Source code
subtree Man page Source code
trapz Source code
trivial_error_rate Man page Source code
tune Man page Source code
validate_data Man page Source code
vlines Man page Source code
weighted_error_rate Man page Source code

Files

inst
inst/CITATION
tests
tests/testthat.R
tests/testthat
tests/testthat/test-methods.r
tests/testthat/test-resample.r
tests/testthat/test-survival.r
tests/testthat/test-modeling.r
tests/testthat/test-preprocess.r
tests/testthat/test-cppfunction.r
tests/testthat/test-extractors.r
tests/testthat/test-helpers.r
tests/testthat/test-procedure.r
tests/testthat/test-debug.r
src
src/is_constant.cpp
src/RcppExports.cpp
NAMESPACE
NEWS.md
R
R/lda.r
R/survival.r
R/learning-curve.r
R/lm.r
R/glmnet.r
R/rpart.r
R/reshape-result.r
R/imputation.r
R/error-functions.r
R/cforest.r
R/RcppExports.R
R/RcppWrappers.r
R/modeling.r
R/modeling_procedure.r
R/plotting.r
R/pamr.r
R/resampling.r
R/preprocessing.r
R/randomForest.r
R/caret.r
R/roc-curve.r
R/e1071.r
R/helpers.r
R/message.r
R/qda.r
MD5
DESCRIPTION
man
man/index_fit.Rd
man/fit_randomForest.Rd
man/pre_impute.Rd
man/predict_lda.Rd
man/fit.Rd
man/nice_axis.Rd
man/pipe.Rd
man/predict_glmnet.Rd
man/tune.Rd
man/subtree.Rd
man/predict_coxph.Rd
man/predict.model.Rd
man/pvalue.survdiff.Rd
man/fit_pamr.Rd
man/error_fun.Rd
man/trivial_error_rate.Rd
man/learning_curve.Rd
man/weighted_error_rate.Rd
man/fit_lda.Rd
man/get_response.Rd
man/subresample.Rd
man/roc_curve.Rd
man/evaluate.Rd
man/get_tuning.Rd
man/predict_pamr.Rd
man/fit_qda.Rd
man/importance_randomForest.Rd
man/factor_to_logical.Rd
man/print.preprocessed_data.Rd
man/importance_pamr.Rd
man/nice_require.Rd
man/pre_pamr.Rd
man/is_constant.Rd
man/pvalue.crr.Rd
man/is_multi_procedure.Rd
man/get_prediction.Rd
man/validate_data.Rd
man/predict_cforest.Rd
man/plot.learning_curve.Rd
man/get_importance.Rd
man/na_index.Rd
man/dichotomize.Rd
man/resample.Rd
man/fit_glmnet.Rd
man/pre_impute_knn.Rd
man/fit_naive_bayes.Rd
man/pre_log_message.Rd
man/extension.Rd
man/is_blank.Rd
man/indent.Rd
man/mode.Rd
man/get_performance.Rd
man/neg_gmpa.Rd
man/image.resample.Rd
man/predict_caret.Rd
man/pre_impute_df.Rd
man/predict_randomForest.Rd
man/name_procedure.Rd
man/impute.Rd
man/vlines.Rd
man/fit_cforest.Rd
man/fit_rpart.Rd
man/plot.Surv.Rd
man/pvalue.coxph.Rd
man/pre_factor_to_logical.Rd
man/pre_process.Rd
man/fill.Rd
man/as.modeling_procedure.Rd
man/modeling_procedure.Rd
man/get_color.Rd
man/fit_coxph.Rd
man/fit_caret.Rd
man/predict_naive_bayes.Rd
man/notify_once.Rd
man/nice_box.Rd
man/importance_glmnet.Rd
man/predict_qda.Rd
man/pvalue.cuminc.Rd
man/fit_lm.Rd
man/predict_rpart.Rd
man/list_method.Rd
man/predict_lm.Rd
man/pvalue.Rd
man/log_message.Rd
man/predict_svm.Rd
man/select.Rd
man/fit_svm.Rd
man/emil.Rd
emil documentation built on May 19, 2017, 10:38 a.m.

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