R: Tools for an Introductory Class in Regression and Modeling

# Tools for an Introductory Class in Regression and Modeling

## Documentation for package ‘regclass’ version 1.7

- DESCRIPTION file

## Help Pages

A B C D E F G I J L M N O P Q R S T V W

## -- A --

ACCOUNT | Predicting whether a customer will open a new kind of account
all.correlations | Pairwise correlations between quantitative variables
all_correlations | Pairwise correlations between quantitative variables
APPLIANCE | Appliance shipments
associate | Association Analysis
ATTRACTF | Attractiveness Score (female)
ATTRACTM | Attractiveness Score (male)
AUTO | AUTO dataset

## -- B --

BODYFAT | BODYFAT data
BODYFAT2 | Secondary BODYFAT dataset
build.model | Variable selection for descriptive or predictive linear and logistic regression models
build.tree | Exploratory building of partition models
build_model | Variable selection for descriptive or predictive linear and logistic regression models
build_tree | Exploratory building of partition models
BULLDOZER | BULLDOZER data
BULLDOZER2 | Modified BULLDOZER data

## -- C --

CALLS | CALLS dataset
CENSUS | CENSUS data
CENSUSMLR | Subset of CENSUS data
CHARITY | CHARITY dataset
check.regression | Linear and Logistic Regression diagnostics
check_regression | Linear and Logistic Regression diagnostics
choose.order | Choosing order of a polynomial model
choose_order | Choosing order of a polynomial model
CHURN | CHURN dataset
combine_rare_levels | Combines rare levels of a categorical variable
confusion.matrix | Confusion matrix for logistic regression models
confusion_matrix | Confusion matrix for logistic regression models
cor.demo | Correlation demo
cor.matrix | Correlation Matrix
cor_demo | Correlation demo
cor_matrix | Correlation Matrix
CUSTCHURN | CUSTCHURN dataset
CUSTLOYALTY | CUSTLOYALTY dataset
CUSTREACQUIRE | CUSTREACQUIRE dataset
CUSTVALUE | CUSTVALUE dataset

## -- D --

DIET | DIET data
DONOR | DONOR dataset

## -- E --

EDUCATION | EDUCATION data
EX2.CENSUS | CENSUS data for Exercise 5 in Chapter 2
EX2.TIPS | TIPS data for Exercise 6 in Chapter 2
EX3.ABALONE | ABALONE dataset for Exercise D in Chapter 3
EX3.BODYFAT | Bodyfat data for Exercise F in Chapter 3
EX3.HOUSING | Housing data for Exercise E in Chapter 3
EX3.NFL | NFL data for Exercise A in Chapter 3
EX4.BIKE | Bike data for Exercise 1 in Chapter 4
EX4.STOCKPREDICT | Stock data for Exercise 2 in Chapter 4 (prediction set)
EX4.STOCKS | Stock data for Exercise 2 in Chapter 4
EX5.BIKE | BIKE dataset for Exercise 4 Chapter 5
EX5.DONOR | DONOR dataset for Exercise 4 in Chapter 5
EX6.CLICK | CLICK data for Exercise 2 in Chapter 6
EX6.DONOR | DONOR dataset for Exercise 1 in Chapter 6
EX6.WINE | WINE data for Exercise 3 Chapter 6
EX7.BIKE | BIKE dataset for Exercise 1 Chapters 7 and 8
EX7.CATALOG | CATALOG data for Exercise 2 in Chapters 7 and 8
EX9.BIRTHWEIGHT | Birthweight dataset for Exercise 1 in Chapter 9
EX9.NFL | NFL data for Exercise 2 Chapter 9
EX9.STORE | Data for Exercise 3 Chapter 9
examine_driver_Ycat | Main driver analysis when Y is a categorical quantity
examine_driver_Ynumeric | Main driver analysis when Y is a numeric quantity
extrapolation.check | A crude check for extrapolation
extrapolation_check | A crude check for extrapolation

## -- F --

find.transformations | Transformations for simple linear regression
find_transformations | Transformations for simple linear regression
FRIEND | Friendship Potential vs. Attractiveness Ratings
FUMBLES | Wins vs. Fumbles of an NFL team

## -- G --

generalization.error | Calculating the generalization error of a model on a set of data
generalization_error | Calculating the generalization error of a model on a set of data
getcp | Complexity Parameter table for partition models

## -- I --

influence.plot | Influence plot for regression diganostics
influence_plot | Influence plot for regression diganostics

## -- J --

JUNK | Junk-mail dataset

## -- L --

LARGEFLYER | Interest in frequent flier program (large version)
LAUNCH | New product launch data

## -- M --

mode_factor | Find the mode of a categorical variable
mosaic | Mosaic plot
MOVIE | Movie grosses

## -- N --

NFL | NFL database

## -- O --

OFFENSE | Some offensive statistics from 'NFL' dataset
outlier_demo | Interactive demonstration of the effect of an outlier on a regression
overfit.demo | Demonstration of overfitting
overfit_demo | Demonstration of overfitting

## -- P --

PIMA | Pima Diabetes dataset
POISON | Cockroach poisoning data
possible.regressions | Illustrating how a simple linear/logistic regression could have turned out via permutations
possible_regressions | Illustrating how a simple linear/logistic regression could have turned out via permutations
PRODUCT | Sales of a product one quarter after release
PURCHASE | PURCHASE data

## -- Q --

qq | QQ plot

## -- R --

replace_rare_levels | Replaces rare levels of a categorical variable

## -- S --

SALARY | Harris Bank Salary data
see.interactions | Examining pairwise interactions between quantitative variables for a fitted regression model
see.models | Examining model AICs from the "all possible" regressions procedure using regsubsets
see_interactions | Examining pairwise interactions between quantitative variables for a fitted regression model
see_models | Examining model AICs from the "all possible" regressions procedure using regsubsets
segmented.barchart | Segmented barchart
segmented_barchart | Segmented barchart
SMALLFLYER | Interest in a frequent flier program (small version)
SOLD26 | Predicting future sales
SPEED | Speed vs. Fuel Efficiency
STUDENT | STUDENT data
suggest_levels | Combining levels of a categorical variable
summarize.tree | Useful summaries of partition models from rpart
summarize_tree | Useful summaries of partition models from rpart
SURVEY09 | Student survey 2009
SURVEY10 | Student survey 2010
SURVEY11 | Student survey 2011

## -- T --

TIPS | TIPS dataset

## -- V --

VIF | Variance Inflation Factor
visualize.model | Visualizations of one or two variable linear or logistic regressions or of partitions models
visualize.relationship | Visualizing the relationship between y and x in a partition model
visualize_model | Visualizations of one or two variable linear or logistic regressions or of partitions models
visualize_relationship | Visualizing the relationship between y and x in a partition model

## -- W --

WINE | WINE data