Package: regclass 1.6

regclass: Tools for an Introductory Class in Regression and Modeling

Contains basic tools for visualizing, interpreting, and building regression models. It has been designed for use with the book Introduction to Regression and Modeling with R by Adam Petrie, Cognella Publishers, ISBN: 978-1-63189-250-9 <https://titles.cognella.com/introduction-to-regression-and-modeling-with-r-9781631892509>.

Authors:Adam Petrie

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regclass.pdf |regclass.html✨
regclass/json (API)

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

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

29 exports 1.00 score 18 dependencies 3 mentions 291 scripts 1.9k downloads

Last updated 5 years agofrom:548f2ed1dc. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 01 2024
R-4.5-winNOTESep 01 2024
R-4.5-linuxNOTESep 01 2024
R-4.4-winOKSep 01 2024
R-4.4-macOKSep 01 2024
R-4.3-winOKSep 01 2024
R-4.3-macOKSep 01 2024

Exports:all_correlationsassociatebuild_modelbuild_treecheck_regressionchoose_ordercombine_rare_levelsconfusion_matrixcor_democor_matrixextrapolation_checkfind_transformationsgeneralization_errorgetcpinfluence_plotmode_factormosaicoutlier_demooverfit_demopossible_regressionsqqsee_interactionssee_modelssegmented_barchartsuggest_levelssummarize_treeVIFvisualize_modelvisualize_relationship

Dependencies:bestglmcodetoolsforeachglmnetgrpregiteratorslatticeleapsMatrixplsrandomForestRcppRcppEigenrpartrpart.plotshapesurvivalVGAM

Readme and manuals

Help Manual

Help pageTopics
Predicting whether a customer will open a new kind of accountACCOUNT
Pairwise correlations between quantitative variablesall.correlations all_correlations
Appliance shipmentsAPPLIANCE
Association Analysisassociate
Attractiveness Score (female)ATTRACTF
Attractiveness Score (male)ATTRACTM
AUTO datasetAUTO
BODYFAT dataBODYFAT
Secondary BODYFAT datasetBODYFAT2
Variable selection for descriptive or predictive linear and logistic regression modelsbuild.model build_model
Exploratory building of partition modelsbuild.tree build_tree
BULLDOZER dataBULLDOZER
Modified BULLDOZER dataBULLDOZER2
CALLS datasetCALLS
CENSUS dataCENSUS
Subset of CENSUS dataCENSUSMLR
CHARITY datasetCHARITY
Linear and Logistic Regression diagnosticscheck.regression check_regression
Choosing order of a polynomial modelchoose.order choose_order
CHURN datasetCHURN
Combines rare levels of a categorical variablecombine_rare_levels
Confusion matrix for logistic regression modelsconfusion.matrix confusion_matrix
Correlation democor.demo cor_demo
Correlation Matrixcor.matrix cor_matrix
CUSTCHURN datasetCUSTCHURN
CUSTLOYALTY datasetCUSTLOYALTY
CUSTREACQUIRE datasetCUSTREACQUIRE
CUSTVALUE datasetCUSTVALUE
DIET dataDIET
DONOR datasetDONOR
EDUCATION dataEDUCATION
CENSUS data for Exercise 5 in Chapter 2EX2.CENSUS
TIPS data for Exercise 6 in Chapter 2EX2.TIPS
ABALONE dataset for Exercise D in Chapter 3EX3.ABALONE
Bodyfat data for Exercise F in Chapter 3EX3.BODYFAT
Housing data for Exercise E in Chapter 3EX3.HOUSING
NFL data for Exercise A in Chapter 3EX3.NFL
Bike data for Exercise 1 in Chapter 4EX4.BIKE
Stock data for Exercise 2 in Chapter 4 (prediction set)EX4.STOCKPREDICT
Stock data for Exercise 2 in Chapter 4EX4.STOCKS
BIKE dataset for Exercise 4 Chapter 5EX5.BIKE
DONOR dataset for Exercise 4 in Chapter 5EX5.DONOR
CLICK data for Exercise 2 in Chapter 6EX6.CLICK
DONOR dataset for Exercise 1 in Chapter 6EX6.DONOR
WINE data for Exercise 3 Chapter 6EX6.WINE
BIKE dataset for Exercise 1 Chapters 7 and 8EX7.BIKE
CATALOG data for Exercise 2 in Chapters 7 and 8EX7.CATALOG
Birthweight dataset for Exercise 1 in Chapter 9EX9.BIRTHWEIGHT
NFL data for Exercise 2 Chapter 9EX9.NFL
Data for Exercise 3 Chapter 9EX9.STORE
A crude check for extrapolationextrapolation.check extrapolation_check
Transformations for simple linear regressionfind.transformations find_transformations
Friendship Potential vs. Attractiveness RatingsFRIEND
Wins vs. Fumbles of an NFL teamFUMBLES
Calculating the generalization error of a model on a set of datageneralization.error generalization_error
Complexity Parameter table for partition modelsgetcp
Influence plot for regression diganosticsinfluence.plot influence_plot
Junk-mail datasetJUNK
Interest in frequent flier program (large version)LARGEFLYER
New product launch dataLAUNCH
Find the mode of a categorical variablemode_factor
Mosaic plotmosaic
Movie grossesMOVIE
NFL databaseNFL
Some offensive statistics from 'NFL' datasetOFFENSE
Interactive demonstration of the effect of an outlier on a regressionoutlier_demo
Demonstration of overfittingoverfit.demo overfit_demo
Pima Diabetes datasetPIMA
Cockroach poisoning dataPOISON
Illustrating how a simple linear/logistic regression could have turned out via permutationspossible.regressions possible_regressions
Sales of a product one quarter after releasePRODUCT
PURCHASE dataPURCHASE
QQ plotqq
Harris Bank Salary dataSALARY
Examining pairwise interactions between quantitative variables for a fitted regression modelsee.interactions see_interactions
Examining model AICs from the "all possible" regressions procedure using regsubsetssee.models see_models
Segmented barchartsegmented.barchart segmented_barchart
Interest in a frequent flier program (small version)SMALLFLYER
Predicting future salesSOLD26
Speed vs. Fuel EfficiencySPEED
STUDENT dataSTUDENT
Combining levels of a categorical variablesuggest_levels
Useful summaries of partition models from rpartsummarize.tree summarize_tree
Student survey 2009SURVEY09
Student survey 2010SURVEY10
Student survey 2011SURVEY11
TIPS datasetTIPS
Variance Inflation FactorVIF
Visualizations of one or two variable linear or logistic regressions or of partitions modelsvisualize.model visualize_model
Visualizing the relationship between y and x in a partition modelvisualize.relationship visualize_relationship
WINE dataWINE