Package: autoMrP 1.0.6

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_1.0.6.tar.gz
autoMrP_1.0.6.zip(r-4.5)autoMrP_1.0.6.zip(r-4.4)autoMrP_1.0.6.zip(r-4.3)
autoMrP_1.0.6.tgz(r-4.4-any)autoMrP_1.0.6.tgz(r-4.3-any)
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autoMrP_1.0.6.tgz(r-4.4-emscripten)autoMrP_1.0.6.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'))

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

Datasets:

absentee_census - Quasi census data.
absentee_voting - A sample of the absentee voting item from the CCES 2008
census - Quasi census data.
survey_item - A sample of a survey item from the CCES 2008
taxes_census - Quasi census data.
taxes_survey - Sample on raising taxes from the 2008 National Annenberg Election Studies.

On CRAN:

3 exports 24 stars 2.40 score 100 dependencies 401 downloads

Last updated 8 months agofrom:74b9bc0966. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Aug 27 2024
R-4.5-win	OK	Aug 27 2024
R-4.5-linux	OK	Aug 27 2024
R-4.4-win	OK	Aug 27 2024
R-4.4-mac	OK	Aug 27 2024
R-4.3-win	OK	Aug 27 2024
R-4.3-mac	OK	Aug 27 2024

Exports:auto_MrP plot.autoMrP summary.autoMrP

Dependencies:abind backports base64enc boot bslib cachem checkmate CholWishart class cli cluster codetools colorspace cpp11 data.table digest doParallel doRNG dplyr e1071 EBMAforecast evaluate fansi farver fastmap fontawesome forcats foreach foreign Formula fs gbm generics ggplot2 glmmLasso glue gridExtra gtable gtools highr Hmisc htmlTable htmltools htmlwidgets isoband iterators jquerylib jsonlite knitr labeling lattice lifecycle lme4 lmtest magrittr MASS Matrix memoise mgcv mime minqa munsell mvtnorm nlme nloptr nnet pillar pkgconfig plyr proxy purrr R6 rappdirs RColorBrewer Rcpp RcppEigen rlang rmarkdown rngtools rpart rstudioapi sass scales separationplot stringi stringr survival tibble tidyr tidyselect tinytex utf8 vctrs vglmer viridis viridisLite withr xfun yaml zoo

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

Rendered fromautoMrP_vignette.pdf.asisusingR.rsp::asison Aug 27 2024.

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

Help page	Topics
Quasi census data.	absentee_census
A sample of the absentee voting item from the CCES 2008	absentee_voting
Improve MrP through ensemble learning.	auto_MrP
Best subset classifier	best_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_mrp	boot_auto_mrp
Quasi census data.	census
Generates folds for cross-validation	cv_folding
Deep MrP classifier	deep_mrp_classifier
Bayesian Ensemble Model Averaging EBMA	ebma
Generates data fold to be used for EBMA tuning	ebma_folding
EBMA multicore tuning - parallelises over draws.	ebma_mc_draws
EBMA multicore tuning - parallelises over tolerance values.	ebma_mc_tol
Catches user input errors	error_checks
Estimates the inverse f1 score, i.e. 0 is the best score and 1 the worst.	f1_score
GB classifier	gb_classifier
GB classifier update	gb_classifier_update
Lasso classifier	lasso_classifier
Sequence that is equally spaced on the log scale	log_spaced
Estimates loss value.	loss_function
Ranks tuning parameters according to loss functions	loss_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 computing	multicore
A table for the summary function	output_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 objects	predict_glmmLasso
Suppress cat in external package	quiet
Apply best subset classifier to MrP.	run_best_subset
Best subset multicore tuning.	run_best_subset_mc
Optimal individual classifiers	run_classifiers
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 2008	survey_item
SVM classifier	svm_classifier
Quasi census data.	taxes_census
Sample on raising taxes from the 2008 National Annenberg Election Studies.	taxes_survey

Package: autoMrP 1.0.6

autoMrP: Improving MrP with Ensemble Learning

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

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Help Manual

Usage by other packages (reverse dependencies)