Package: autoMrP 1.1.0

Philipp Broniecki

autoMrP: Improving MrP with Ensemble Learning

A tool that improves the prediction performance of multilevel regression with post-stratification (MrP) by combining a number of machine learning methods. For information on the method, please refer to Broniecki, Wüest, Leemann (2020) ''Improving Multilevel Regression with Post-Stratification Through Machine Learning (autoMrP)'' in the 'Journal of Politics'. Final pre-print version: <https://lucasleemann.files.wordpress.com/2020/07/automrp-r2pa.pdf>.

Authors:Reto Wüest [aut], Lucas Leemann [aut], Florian Schaffner [aut], Philipp Broniecki [aut, cre], Hadley Wickham [ctb]

autoMrP_1.1.0.tar.gz
autoMrP_1.1.0.zip(r-4.5)autoMrP_1.1.0.zip(r-4.4)autoMrP_1.1.0.zip(r-4.3)
autoMrP_1.1.0.tgz(r-4.4-any)autoMrP_1.1.0.tgz(r-4.3-any)
autoMrP_1.1.0.tar.gz(r-4.5-noble)autoMrP_1.1.0.tar.gz(r-4.4-noble)
autoMrP_1.1.0.tgz(r-4.4-emscripten)autoMrP_1.1.0.tgz(r-4.3-emscripten)
autoMrP.pdf |autoMrP.html✨
autoMrP/json (API)
NEWS

# Install 'autoMrP' in R:
install.packages('autoMrP', repos = c('https://retowuest.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/retowuest/automrp/issues

Datasets:

On CRAN:

6.05 score 25 stars 328 downloads 3 exports 106 dependencies

Last updated 23 days agofrom:5e2df1225c. Checks:OK: 1 ERROR: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winERROROct 31 2024
R-4.5-linuxERROROct 31 2024
R-4.4-winERROROct 31 2024
R-4.4-macERROROct 31 2024
R-4.3-winERROROct 31 2024
R-4.3-macERROROct 31 2024

Exports:auto_MrPplot.autoMrPsummary.autoMrP

Dependencies:abindbackportsbase64encbootbslibcachemcheckmateCholWishartclasscliclustercodetoolscolorspacecpp11data.tabledigestdoParalleldoRNGdplyre1071EBMAforecastevaluatefansifarverfastmapfontawesomeforcatsforeachforeignFormulafsgbmgenericsggplot2glmmLassogluegridExtragtablegtoolshighrHmischtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonliteknitrlabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixmemoisemgcvmimeminqamunsellmvtnormnlmenloptrnnetpillarpkgconfigplyrproxypurrrquadprogR.cacheR.methodsS3R.ooR.rspR.utilsR6rappdirsRColorBrewerRcppRcppEigenrlangrmarkdownrngtoolsrpartrstudioapisassscalesseparationplotstringistringrsurvivaltibbletidyrtidyselecttinytexutf8vctrsvglmerviridisviridisLitewithrxfunyamlzoo

autoMrP: Multilevel Models and Post-Stratification (MrP) Combined with Machine Learning in R

Rendered fromautoMrP_vignette.pdf.asisusingR.rsp::asison Oct 31 2024.

Last update: 2021-01-15
Started: 2021-01-15

Readme and manuals

Help Manual

Help pageTopics
Quasi census data.absentee_census
A sample of the absentee voting item from the CCES 2008absentee_voting
Improve MrP through ensemble learning.auto_MrP
Best subset classifierbest_subset_classifier
Estimates the inverse binary cross-entropy, i.e. 0 is the best score and 1 the worst.binary_cross_entropy
Bootstrappinng wrapper for auto_mrpboot_auto_mrp
Quasi census data.census
Generates folds for cross-validationcv_folding
Deep MrP classifierdeep_mrp_classifier
Bayesian Ensemble Model Averaging EBMAebma
Generates data fold to be used for EBMA tuningebma_folding
EBMA multicore tuning - parallelises over draws.ebma_mc_draws
EBMA multicore tuning - parallelises over tolerance values.ebma_mc_tol
Catches user input errorserror_checks
Estimates the inverse f1 score, i.e. 0 is the best score and 1 the worst.f1_score
GB classifiergb_classifier
GB classifier updategb_classifier_update
Lasso classifierlasso_classifier
Sequence that is equally spaced on the log scalelog_spaced
Estimates loss value.loss_function
Ranks tuning parameters according to loss functionsloss_score_ranking
Estimates the mean absolute prediction error.mean_absolute_error
Estimates the mean squared prediction error.mean_squared_error
Estimates the mean squared false error.mean_squared_false_error
A list of models for the best subset selection.model_list
A list of models for the best subset selection with PCA.model_list_pca
Register cores for multicore computingmulticore
A table for the summary functionoutput_table
A plot method for autoMrP objects. Plots unit-level preference estiamtes.plot.autoMrP
Apply post-stratification to classifiers.post_stratification
Predicts on newdata from glmmLasso objectspredict_glmmLasso
Suppress cat in external packagequiet
Apply best subset classifier to MrP.run_best_subset
Best subset multicore tuning.run_best_subset_mc
Optimal individual classifiersrun_classifiers
Apply deep mrp to the best subset classifier to MrP.run_deep_bs
Apply PCA classifier to MrP.run_deep_pca
Apply gradient boosting classifier to MrP.run_gb
GB multicore tuning.run_gb_mc
Apply lasso classifier to MrP.run_lasso
Lasso multicore tuning.run_lasso_mc_lambda
Apply PCA classifier to MrP.run_pca
Apply support vector machine classifier to MrP.run_svm
SVM multicore tuning.run_svm_mc
A summary method for autoMrP objects.summary.autoMrP
A sample of a survey item from the CCES 2008survey_item
SVM classifiersvm_classifier
Quasi census data.taxes_census
Sample on raising taxes from the 2008 National Annenberg Election Studies.taxes_survey