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regclass (version 1.6)

Tools for an Introductory Class in Regression and Modeling

Description

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 .

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Version

Install

install.packages('regclass')

Monthly Downloads

2,573

Version

1.6

License

GPL (>= 2)

Maintainer

Adam Petrie

Last Published

February 21st, 2020

Functions in regclass (1.6)

CHARITY dataset

CUSTLOYALTY dataset

CUSTCHURN dataset

Appliance shipments

Data for Exercise 3 Chapter 9

EX4.STOCKPREDICT

Stock data for Exercise 2 in Chapter 4 (prediction set)

NFL data for Exercise 2 Chapter 9

Housing data for Exercise E in Chapter 3

Stock data for Exercise 2 in Chapter 4

Subset of CENSUS data

NFL data for Exercise A in Chapter 3

BIKE dataset for Exercise 1 Chapters 7 and 8

Bodyfat data for Exercise F in Chapter 3

Exploratory building of partition models

WINE data for Exercise 3 Chapter 6

Bike data for Exercise 1 in Chapter 4

check_regression

Linear and Logistic Regression diagnostics

BIKE dataset for Exercise 4 Chapter 5

Some offensive statistics from NFL dataset

Complexity Parameter table for partition models

DONOR dataset for Exercise 4 in Chapter 5

Wins vs. Fumbles of an NFL team

Student survey 2009

Friendship Potential vs. Attractiveness Ratings

CENSUS data for Exercise 5 in Chapter 2

Association Analysis

Variable selection for descriptive or predictive linear and logistic regression models

Student survey 2010

Correlation Matrix

CUSTREACQUIRE dataset

Influence plot for regression diganostics

Harris Bank Salary data

DONOR dataset for Exercise 1 in Chapter 6

Junk-mail dataset

CLICK data for Exercise 2 in Chapter 6

Student survey 2011

Interest in frequent flier program (large version)

Interest in a frequent flier program (small version)

New product launch data

extrapolation_check

A crude check for extrapolation

segmented_barchart

Segmented barchart

Combining levels of a categorical variable

Examining model AICs from the "all possible" regressions procedure using regsubsets

Find the mode of a categorical variable

see_interactions

Examining pairwise interactions between quantitative variables for a fitted regression model

Variance Inflation Factor

Sales of a product one quarter after release

visualize_relationship

Visualizing the relationship between y and x in a partition model

ABALONE dataset for Exercise D in Chapter 3

TIPS data for Exercise 6 in Chapter 2

possible_regressions

Illustrating how a simple linear/logistic regression could have turned out via permutations

CUSTVALUE dataset

all_correlations

Pairwise correlations between quantitative variables

EX9.BIRTHWEIGHT

Birthweight dataset for Exercise 1 in Chapter 9

CATALOG data for Exercise 2 in Chapters 7 and 8

Pima Diabetes dataset

Choosing order of a polynomial model

confusion_matrix

Confusion matrix for logistic regression models

Correlation demo

Useful summaries of partition models from rpart

combine_rare_levels

Combines rare levels of a categorical variable

Cockroach poisoning data

find_transformations

Transformations for simple linear regression

visualize_model

Visualizations of one or two variable linear or logistic regressions or of partitions models

Speed vs. Fuel Efficiency

generalization_error

Calculating the generalization error of a model on a set of data

Interactive demonstration of the effect of an outlier on a regression

Demonstration of overfitting

Predicting future sales

Predicting whether a customer will open a new kind of account

Secondary BODYFAT dataset

Modified BULLDOZER data

Attractiveness Score (female)

Attractiveness Score (male)