Package: RegrCoeffsExplorer 1.1.0

Vadim Tyuryaev

RegrCoeffsExplorer: Efficient Visualization of Regression Coefficients for lm(), glm(), and glmnet() Objects

The visualization tool offers a nuanced understanding of regression dynamics, going beyond traditional per-unit interpretation of continuous variables versus categorical ones. It highlights the impact of unit changes as well as larger shifts like interquartile changes, acknowledging the distribution of empirical data. Furthermore, it generates visualizations depicting alterations in Odds Ratios for predictors across minimum, first quartile, median, third quartile, and maximum values, aiding in comprehending predictor-outcome interplay within empirical data distributions, particularly in logistic regression frameworks.

Authors:Vadim Tyuryaev [aut, cre], Aleksandr Tsybakin [aut], Jane Heffernan [aut], Hanna Jankowski [aut], Kevin McGregor [aut]

RegrCoeffsExplorer_1.1.0.tar.gz
RegrCoeffsExplorer_1.1.0.zip(r-4.7-any)RegrCoeffsExplorer_1.1.0.zip(r-4.6-any)RegrCoeffsExplorer_1.1.0.zip(r-4.5-any)
RegrCoeffsExplorer_1.1.0.tgz(r-4.6-any)RegrCoeffsExplorer_1.1.0.tgz(r-4.5-any)
RegrCoeffsExplorer_1.1.0.tar.gz(r-4.7-any)RegrCoeffsExplorer_1.1.0.tar.gz(r-4.6-any)
RegrCoeffsExplorer_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
RegrCoeffsExplorer/json (API)

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

Bug tracker:https://github.com/vadimtyuryaev/regrcoeffsexplorer/issues

Pkgdown/docs site:https://vadimtyuryaev.github.io

On CRAN:

Conda:

coefficients-of-linear-regressionconfidence-intervalsempirical-dataglmglmnetlasso-regressionlmpostselectioninferenceregression-analysisregularized-linear-regressionregularized-logistic-regressionselectiveinferencestatistics-for-data-sciencevisualization

4.48 score 3 stars 4 scripts 302 downloads 3 exports 81 dependencies

Last updated from:abb7725609. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK161
source / vignettesOK234
linux-releaseOK164
macos-releaseOK126
macos-oldrelOK128
windows-develOK102
windows-releaseOK111
windows-oldrelOK105
wasm-releaseOK116

Exports:detransformplot_ORvis_reg

Dependencies:abindbackportsbootbroomcarcarDataclicodetoolscolorspacecorrplotcowplotcpp11DerivdoBydplyrfarverforeachforecastFormulafracdiffgenericsggplot2ggpubrggrepelggsciggsignifglmnetgluegridExtragtableisobanditeratorslabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmgcvminqamodelrnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpolynompurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrstatixS7scalesshapeSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo

BetaVisualizer
LIBRARIES | EXAMPLES | 1. LM object | 1.1 Data | 1.2 Default side-by-side plots | 1.3 Visualizing per-unit change together with an intercept | 1.4 Adding Confidence Intervals (CIs) | 1.4 Customizing pallete, title, and modifying default realized effect size calculations | 1.5 Customizing individual graphs | 2. GLM object | 2.1 Data and fitted object | 2.2 Default side-by-side plots, CIs and 99% confidence interval | 3 GLMNET model objects | 3.1. Data | 3.2 LASSO - data preparation and model | 3.3 Checking correlations for numeric variables of interest | 3.4 Default LASSO plots with custom realized effect size | 3.5 Modifying idividial plots and arraning them back together | 3.6 Post Selection Inference | 3.6.1 Data | 3.6.2 Data observations | 3.6.3 A note about rounding | 3.6.4 Model | 3.6.5 A note on data scaling and centering in relation to glmnet objects | 3.6.6 LASSO regression with CIs and custom realized effect size | 3.6.7 A note on Selective Inference | 3.6.7 GLMNET with penalty factor and CIs | 3.6.8 A second note on data scaling and centering in relation to fixedLassoInf objects | 3.6.9 Post selection inference with CIs and penalty factors

Last update: 2025-01-14
Started: 2024-04-23

OddsRatioVisualizer
LIBRARIES | EXAMPLES | 1. Data | 2. Function | 3. Visualization | 3.1 Default Plots | 3.2 Customization | 3.3 Multiple Plots

Last update: 2024-11-13
Started: 2024-04-23