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regclass (version 1.5)

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

1.5

License

GPL (>= 2)

Maintainer

Adam Petrie

Last Published

January 10th, 2017

Functions in regclass (1.5)

Secondary BODYFAT dataset

Variable selection for descriptive or predictive linear and logistic regression models

all_correlations

Pairwise correlations between quantitative variables

Predicting whether a customer will open a new kind of account

Attractiveness Score (male)

Association Analysis

Appliance shipments

Attractiveness Score (female)

Exploratory building of partition models

Modified BULLDOZER data

Choosing order of a polynomial model

CHARITY dataset

Subset of CENSUS data

check_regression

Linear and Logistic Regression diagnostics

CUSTREACQUIRE dataset

TIPS data for Exercise 6 in Chapter 2

CUSTVALUE dataset

ABALONE dataset for Exercise D in Chapter 3

WINE data for Exercise 3 Chapter 6

BIKE dataset for Exercise 1 Chapters 7 and 8

NFL data for Exercise A in Chapter 3

Bike data for Exercise 1 in Chapter 4

BIKE dataset for Exercise 4 Chapter 5

DONOR dataset for Exercise 4 in Chapter 5

extrapolation_check

A crude check for extrapolation

CUSTLOYALTY dataset

CUSTCHURN dataset

Friendship Potential vs. Attractiveness Ratings

Wins vs. Fumbles of an NFL team

see_interactions

Examining pairwise interactions between quantitative variables for a fitted regression model

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

Student survey 2011

Student survey 2010

Correlation Matrix

Correlation demo

CENSUS data for Exercise 5 in Chapter 2

NFL data for Exercise 2 Chapter 9

Data for Exercise 3 Chapter 9

Interest in frequent flier program (large version)

New product launch data

Interactive demonstration of the effect of an outlier on a regression

Some offensive statistics from NFL dataset

segmented_barchart

Segmented barchart

combine_rare_levels

Combines rare levels of a categorical variable

Interest in a frequent flier program (small version)

confusion_matrix

Confusion matrix for logistic regression models

Bodyfat data for Exercise F in Chapter 3

Housing data for Exercise E in Chapter 3

CATALOG data for Exercise 2 in Chapters 7 and 8

EX9.BIRTHWEIGHT

Birthweight dataset for Exercise 1 in Chapter 9

Stock data for Exercise 2 in Chapter 4

EX4.STOCKPREDICT

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

CLICK data for Exercise 2 in Chapter 6

generalization_error

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

DONOR dataset for Exercise 1 in Chapter 6

Complexity Parameter table for partition models

Cockroach poisoning data

Demonstration of overfitting

Pima Diabetes dataset

Sales of a product one quarter after release

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

possible_regressions

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

Harris Bank Salary data

Useful summaries of partition models from rpart

Student survey 2009

Combining levels of a categorical variable

Influence plot for regression diganostics

Find the mode of a categorical variable

Junk-mail dataset

Speed vs. Fuel Efficiency

Predicting future sales

Variance Inflation Factor

find_transformations

Transformations for simple linear regression