Package 'regclass'

Description

Customers were marketed a new type of account at a bank. It is desired to model what factors seemed to be associated with the probability of opening the account to tune marketing strategy.

Usage

data("ACCOUNT")

Format

A data frame with 24242 observations on the following 8 variables.

Purchase

a factor with levels No Yes

Tenure

a numeric vector, the number of years the customer has been with the bank

CheckingBalance

a numeric vector, amount currently held in checking (may be negative if overdrafted)

SavingBalance

a numeric vector, amount currently held in savings (0 or larger)

Income

a numeric vector, yearly income in thousands of dollars

Homeowner

a factor with levels No Yes

Age

a numeric vector

Area.Classification

a factor with levels R S U for rural, suburban, or urban

Details

Who is more likely to open a new type of account that a bank wants to try to sell its customers? Try logistic regression or partition models to see if you can develop a model that accurately classifies purchasers vs. non-purchasers. Or, try to develop a model that does well in promoting to nearly all customers who would buy the account.

Description

This function gives a list of all pairwise correlations between quantitative variables in a dataframe. Alternatively, it can provide all pairwise correlations with just a particular variable.

Usage

all_correlations(X,type="pearson",interest=NA,sorted="none")

Arguments

Details

This function filters out any non-numerical variables in the data frame and provides correlations only between quantitative variables. It is useful for quickly glancing at the size of the correlations between many pairs of variables or all correlations with a particular variable. Further analysis should be done on pairs of interest using associate.

Note: if Spearmans' rank correlations are computed, warnings message result indicating that the exact p-value cannot be computed with ties. Running associate will give you an approximate p-value using the permutation procedure.

Author(s)

Adam Petrie

References

Introduction to Regression and Modeling

See Also

cor, associate

Examples

#all pairwise (Pearson) correlations between all quantitative variables
	data(STUDENT)
	all_correlations(STUDENT)  
	#Spearman correlations between all quantitative variables and CollegeGPA, sorted by pvalue. 
	#Gives warnings due to ties
	all_correlations(STUDENT,interest="CollegeGPA",type="spearman",sorted="significance")

Description

Appliance shipments from 1960 to 1985

Usage

data("APPLIANCE")

Format

A data frame with 26 observations on the following 7 variables.

Year

a numeric vector

Dishwasher

a numeric vector, Factory shipments (domestic) of dishwashers (thousands)

Disposal

a numeric vector, Factory shipments (domestic) of disposers (thousands)

Refrigerator

a numeric vector, Factory shipments (domestic) of refrigerators (thousands)

Washer

a numeric vector, Factory shipments (domestic) of washing machines (thousands)

DurableGoodsExp

a numeric vector, Durable goods expenditures (billions of 1972 dollars)

PrivateResInvest

a numeric vector, Private residential investment (billions of 1972 dollars)

Details

From the (former) Data and Story library.

The file gives unit shipments of dishwashers, disposers, refrigerators, and washers in the United States from 1960 to 1985. This and other data are published currently in the Department of Commerce's Survey of Current Business, and are summarized from time to time in their publication, Business Statistics. Also included in the file are durable goods expenditures and private residential investment in the United States.

Description

This function takes two quantities and computes relevent numerical measures of association. The p-values of the associations are estimated via permutation tests. Plots for diagnostics are provided as well, with optional arguments that allow for classic tests.

Usage

associate(formula, data, permutations = 500, seed=NA, plot = TRUE, classic = FALSE, 
  cex.leg=0.7, n.levels=NA,prompt=TRUE,color=TRUE,...)

Arguments

Details

This function uses Monte Carlo simulation (permutation procedure) to approximate the p-value of an association. Only complete cases are considered in the analysis.

Valid formulas may include functions of the variable, e.g. y^2, log10(x), or more complicated functions like I(x1/(x2+x3)). In the latter case, I() must surround the function of interest to be computed correctly.

When both x and y are quantitative variables, an analysis of Pearson's correlation and Spearman's rank correlation is provided. Scatterplots and histograms of the variables are provided. If classic is TRUE, the QQ-plots of the variables are provided along with tests of assumptions.

When x is categorical and y is quantitative, the averages (as well as mean ranks and medians) of y are compared between levels of x. The "discrepancy" is the F statistic for averages, Kruskal-Wallis statistic for mean ranks, and the chi-squared statistic for the median test. Side-by-side boxplots are also provided. If classic is TRUE, the QQ-plots of the distribution of y for each level of x are provided.

When x is quantitative and y is categorical, x is converted to a categorical variable with n.levels levels with equal numbers of cases. A chi-squared test is performed for the association. The classic approach assumes a multinomial logistic regression to check significance. A mosaic plot showing the distribution of y for each induced level of x is provided as well as a probability "curve". If classic is TRUE, the multinomial logistic curves for each level are provided versus x..

When both x and y are categorical, a chi-squared test is performed. The contingency table, table of expected counts, and conditional distributions are also reported along with a mosaic plot.

If the permutation procedure is used, the sampling distribution of the measure of association is displayed over the requested amount of permutations along with the observed value on the actual data (except when y is categorical with x quantitative).

If classic results are desired, then plots and tests to check assumptions are supplied. white.test from package bstats (version 1.1-11-5) and mshapiro.test from package mvnormtest (version 0.1-9) are built into the function to avoid directly referencing the libraries (which sometimes causes problems).

Author(s)

Adam Petrie

References

Introduction to Regression and Modeling

See Also

lm, glm, anova, cor, chisq.test, vglm

Examples

#Two quantitative variables
  data(SALARY)
	associate(Salary~Education,data=SALARY,permutations=1000)
	
	#y is quantitative while x is categorical
	data(SURVEY11)
	associate(X07.GPA~X40.FavAlcohol,data=SURVEY11,permutations=0,classic=TRUE)
	
	#y is categorical while x is quantitative
	data(WINE)
	associate(Quality~alcohol,data=WINE,classic=TRUE,n.levels=5) 

  #Two categorical variables (many cases, turns off prompt asking for user input)
  data(ACCOUNT)
  set.seed(320)
  #Work with a smaller subset
  SUBSET <- ACCOUNT[sample(nrow(ACCOUNT),1000),]
	associate(Purchase~Area.Classification,data=SUBSET,classic=TRUE,prompt=FALSE)

Description

The average attractiveness scores of 70 females along with physical attributes

Usage

data("ATTRACTF")

Format

A data frame with 70 observations on the following 21 variables.

Score

a numeric vector giving the average attractivness score compiled after 100 student ratings

Actual.Sexuality

a factor with levels Gay Straight indicating the self-reported sexuality of the person in the picture

ApparentRace

a factor with levels black other white indicating the consensus regarding the apparent race of the person

Chin

a factor with levels pointed rounded indicating the consensus regarding the shape of the person's chin

Cleavage

a factor with levels no yes indicating the consensus regarding whether the pictured woman was prominently displaying cleavage

ClothingStyle

a factor with levels conservative revealing indicating the consensus regarding how the women was dressed

FaceSymmetryScore

a numeric vector indicating the number of people (out of 2) who agreed the woman's case was symmetric

FashionScore

a numeric vector indicating the number of people (out of 4) who agreed the woman was fashionable

FitnessScore

a numeric vector indicating the number of people (out of 4) who agreed the woman was physically fit

GayScore

a numeric vector indicating the number of people (out of 16) who agreed the woman was a lesbian

Glasses

a factor with levels Glasses No Glasses

GroomedScore

a numeric vector indicating the number of people (out of 4) who agreed the woman made a noticeable effort to look nice

HairColor

a factor with levels dark light indicating the consensus regarding the woman's hair color

HairstyleUniquess

a numeric vector indicating the number of people (out of 2) who agreed the woman had an unconventional haircut

HappinessRating

a numeric vector indicating the number of people (out of 2) who agreed the woman looked happy in her photo

LookingAtCamera

a factor with levels no yes

MakeupScore

a numeric vector indicating the number of people (out of 5) who agreed the woman was wearing a noticeable amount of makeup

NoseOddScore

a numeric vector indicating the number of people (out of 3) who agreed the woman had an unusually shaped nose

Selfie

a factor with levels no yes

SkinClearScore

a numeric vector indicating the number of people (out of 2) who agreed the woman's complexion was clear.

Smile

a factor with levels no yes

Details

Students were asked to rate on a scale of 1 (very unattractive) to 5 (very attractive) the attractiveness of 70 college-aged women who had posted their photos on a dating website. Of the nearly 100 respondents, most were straight males. Score represents the average of these ratings.

In a separate survey, students (of both genders) were asked to rate characteristics of the woman by answering the questions: what is her race, is she displaying her cleavage prominently, is she a lesbian, is she physically fit, etc. The variables ending “Score" represent the number of students who answered Yes to the question. Other variables (such as Selfie, Smile) represent the consensus among the students. The only attribute taken from the woman's profile was Actual.Sexuality.

Source

Students in BAS 320 at the University of Tennessee from 2013-2015.

Description

The average attractiveness scores of 70 males along with physical attributes

Usage

data("ATTRACTM")

Format

A data frame with 70 observations on the following 23 variables.

Score

a numeric vector giving the average attractivness score compiled after 60 student ratings

Actual.Sexuality

a factor with levels Gay Straight indicating the self-reported sexuality of the person in the picture

ApparentRace

a factor with levels black other white indicating the consensus regarding the apparent race of the person

Chin

a factor with levels pointed rounded indicating the consensus regarding the shape of the person's chin

ClothingStyle

a factor with levels conservative revealing indicating the consensus regarding how the man was dressed

FaceSymmetryScore

a numeric vector indicating the number of people (out of 7) who agreed the woman's case was symmetric

FacialHair

a factor with levels no yes indicating the consensus regarding whether the man appeared to maintain facial hair

FashionScore

a numeric vector indicating the number of people (out of 7) who agreed the woman was fashionable

FitnessScore

a numeric vector indicating the number of people (out of 8) who agreed the woman was physically fit

GayScore

a numeric vector indicating the number of people (out of 16) who agreed the man was gay

Glasses

a factor with levels no yes

GroomedScore

a numeric vector indicating the number of people (out of 6) who agreed the woman made a noticeable effort to look nice

HairColor

a factor with levels dark light unseen indicating the consensus regarding the man's hair color

HairstyleUniquess

a numeric vector indicating the number of people (out of 4) who agreed the woman had an unconventional haircut

HappinessRating

a numeric vector indicating the number of people (out of 6) who agreed the man looked happy in her photo

Hat

a factor with levels no yes

LookingAtCamera

a factor with levels no yes

NoseOddScore

a numeric vector indicating the number of people (out of 3) who agreed the woman had an unusually shaped nose

Piercings

a factor with levels no yes indicating whether the man had visible piercings

Selfie

a factor with levels no yes

SkinClearScore

a numeric vector indicating the number of people (out of 2) who agreed the woman's complexion was clear.

Smile

a factor with levels no yes

Tattoo

a factor with levels no yes

Details

Students were asked to rate on a scale of 1 (very unattractive) to 5 (very attractive) the attractiveness of 70 college-aged men who had posted their photos on a dating website. Of the nearly 60 respondents, most were straight females. Score represents the average of these ratings.

In a separate survey, students (of both genders) were asked to rate characteristics of the man by answering the questions: what is his race, how symmetric does his face look, is he gay, is he physically fit, etc. The variables ending “Score" represent the number of students who answered Yes to the question. Other variables (such as Hat, Smile) represent the consensus among the students. The only attribute taken from the man's profile was Actual.Sexuality.

Source

Students in BAS 320 at the University of Tennessee from 2013-2015.

Description

Characteristics of cars from 1991

Usage

data("AUTO")

Format

A data frame with 82 observations on the following 5 variables.

CabVolume

a numeric vector, cubic feet of cab space

Horsepower

a numeric vector, engine horsepower

FuelEfficiency

a numeric vector, average miles per gallon

TopSpeed

a numeric vector, miles per hour

Weight

a numeric vector, in units of 100 lbs

Details

Although this is a popular dataset, there is some question as to the units of the fuel efficiency. The source claims it to be in miles per gallon, but the numbers reported seem unrealistic. However, the units do not appear to be in km/gallon or km/L.

Source

Data provided by the U.S. Environmental Protection Agency and obtained from the (former) Data and Story library

References

R.M. Heavenrich, J.D. Murrell, and K.H. Hellman, Light Duty Automotive Technology and Fuel Economy Trends Through 1991, U.S. Environmental Protection Agency, 1991 (EPA/AA/CTAB/91-02)

Description

Popular Bodyfat dataset

Usage

data("BODYFAT")

Format

A data frame with 252 observations on the following 14 variables.

BodyFat

a numeric vector indicating the percentage body fat 0-100

Age

a numeric vector, yrs

Weight

a numeric vector, lbs

Height

a numeric vector, inches

Neck

a numeric vector

Chest

a numeric vector

Abdomen

a numeric vector

Hip

a numeric vector

Thigh

a numeric vector

Knee

a numeric vector

Ankle

a numeric vector

Biceps

a numeric vector

Forearm

a numeric vector

Wrist

a numeric vector

Details

Bodyfat can be accurately measured by the hydrostatic technique, where someone is submereged in a tank of water. It would be useful to be able to predict body fat from measurements that are simpler to obtain. Unless otherwise specified, all physical measurements are in centimeters.

Source

This is a modified version of the data available in “Fitting Percentage of Body Fat to Simple Body Measurements" as appearing in Journal of Statistics Education v4 n1 (1996). http://www.amstat.org/publications/jse/v4n1/datasets.johnson.html

Description

Bodyfat dataset illustrating quirks of statistical significance

Usage

data("BODYFAT2")

Format

A data frame with 20 observations on the following 4 variables.

Triceps

a numeric vector, cm

Thigh

a numeric vector, cm

Midarm

a numeric vector, cm

BodyFat

a numeric vector, 0-100 representing percent

Details

The physical measurements are circumferences of body parts of 25-34 year-old healthy females.

Source

This is a classic dataset found in many textbooks and in many places online. The original source may be Neter, Kutner, Nachtsheim, Wasserman, 1997, p. 261: Applied Statistical Models (4th Edition).

Description

This function uses bestglm to consider an extensive array of models and makes recommendations on what set of variables is appropriate for the final model. Model hierarchy is not preserved. Interactions and multi-level categorical variables are allowed.

Usage

build_model(form,data,type="predictive",Kfold=5,repeats=10,
prompt=TRUE,seed=NA,holdout=NA,...)

Arguments

Details

This procedure takes the formula specified by form and the original dataframe and simply converts it into a form that bestglm (which normally cannot do cross-validation when categorical variables are involved) can use by adding in columns to represent interactions and categorical variables.

One the dataframe has been generated, a warning is given to the user if the procedure may take too long (many rows or many potential predictors), and then bestglm is run. A plot and table of models' performances is given, as well as a recommendation for a final set of variables (model with the lowest AIC/estimated generalization error, or a simpler model that is more or less equivalent).

The command returns a list with bestformula (the formula of the model with the lowest AIC or the model chosen by the one standard deviation rule), bestmodel (the fitted model that had the lowest AIC or the one chosen by the one standard deviation rule), predictors (a list giving the predictors that appeared in the best model with 1 predictor, with 2 predictors, etc).

If a descriptive model is sought, the last component of the returned list is AICtable (a data frame containing the number of predictors and the AIC of the best model with that number of predictors; a * denotes the model with the lowest AIC while a + denotes the simplest model whose AIC is within 2 of the lowest).

If a predictive model is sought, the last component of the returned list is CVtable (a data frame containing the number of predictors and the estimated generalization error of the best model with that number of predictors along with the SD from repeated K-fold cross validation; a * denotes the model with the lowest error while the + denotes the model selected with the one standard deviation rule). Note that the generalization error in the second column of this table is the squared error if the response is quantitative and is another measure of error (not the misclassification rate) if the response is categorical. Additional columns are provided to give the root mean squared error or misclassification rate.

Note: bestmodel is the one selected by the one standard deviation rule or the simplest one whose AIC is no more than 2 above the model with the lowest AIC. Because the procedure does not respect model hierarchy and can include interactions, the formula returned may not be immediately useable if it involves a categorical variable since the variable returned is how R names indicator variables. You may have to manually fit the model based on the selected predictors.

If HOLDOUT is given a plot of the error on the holdout sample versus the number of predictors (for the best model at that number of predictors) is provided along with the estimated generalization error from the training set. This can be used to see if the models generalize well, but is in general not used to tune which model is selected.

Author(s)

Adam Petrie

References

Introduction to Regression and Modeling with R

See Also

bestglm, regsubsets, see.models, generalization.error.

Examples

#Descriptive model.  Note: Tip and Bill should not be used simultaneously as 
  #predictors of TipPercentage, so leave Tip out since it's not known ahead of time
  data(TIPS)
  MODELS <- build_model(TipPercentage~.-Tip,data=TIPS,type="descriptive")
  MODELS$AICtable
  MODELS$predictors[[1]] #Variable in best model with a single predictors
  MODELS$predictors[[2]] #Variables in best model with two predictors
  summary(MODELS$bestmodel) #Summary of best model, in this case with two predictors

  #Another descriptive model (large dataset so changing prompt=FALSE for documentation)
  data(PURCHASE)
  set.seed(320)
  #Take a subset of full dataframe for quick illustration
  SUBSET <- PURCHASE[sample(nrow(PURCHASE),500),]
  MODELS <- build_model(Purchase~.,data=SUBSET,type="descriptive",prompt=FALSE)
  MODELS$AICtable  #Model with 1 or 2 variables look pretty good
  #Predict whether a purchase is made by # of previous visits and distance to store
	MODELS$predictors[[2]]  

  #Predictive model.  
  data(SALARY)
  set.seed(2010)
  train.rows <- sample(nrow(SALARY),0.7*nrow(SALARY),replace=TRUE)
  TRAIN <- SALARY[train.rows,]
  HOLDOUT <- SALARY[-train.rows,]
  MODELS <- build_model(Salary~.^2,data=TRAIN,holdout=HOLDOUT)
  summary(MODELS$bestmodel)
  M <- lm(Salary~Gender+Education:Months,data=TRAIN)
  generalization_error(M,HOLDOUT)
  
  #Predictive model for WINE data, takes a while.  Misclassification rate on holdout sample is 18%.
  data(WINE)
  set.seed(2010)
  train.rows <- sample(nrow(WINE),0.7*nrow(WINE),replace=TRUE)
  TRAIN <- WINE[train.rows,]
  HOLDOUT <- WINE[-train.rows,]
  ## Not run: MODELS <- build_model(Quality~.,data=TRAIN,seed=1919,holdout=HOLDOUT)
  ## Not run: MODELS$CVtable

Description

A tool to choose the "correct" complexity parameter of a tree

Usage

build_tree(form, data, minbucket = 5, seed=NA, holdout, mincp=0)

Arguments

Details

This command combines the action of building a tree to its maximum possible extent using rpart and looking at the results using getcp. A plot of the estimated relative generalization error (as determined by 10-fold cross validation) versus the number of splits is provided. In addition, the complexity parameter table giving the cp of the tree with the lowest error (and of the simplest tree with an error within one standard deviation of the lowest error) is reported.

If holdout is given, the RMSE/misclassification rate on the training and holdout samples are provided in the cp table.

Author(s)

Adam Petrie

References

Introduction to Regression and Modeling

See Also

rpart, getcp

Examples

data(JUNK)
  build_tree(Junk~.,data=JUNK,seed=1337)
  data(CENSUS)
  build_tree(ResponseRate~.,data=CENSUS,seed=2017,mincp=0.001)
  data(OFFENSE)
  build_tree(Win~.,data=OFFENSE[1:200,],seed=2029,holdout=OFFENSE[201:352,])

Description

Predicting the sales price of a bulldozer at auction

Usage

data("BULLDOZER")

Format

A data frame with 924 observations on the following 6 variables.

SalePrice

a numeric vector

YearsAgo

a numeric vector, the number of years ago (before present) that the sale occurred

YearMade

a numeric vector, year of manufacture of machine

Usage

a numeric vector, hours of usage at time of sale

Blade

a numeric vector, width of the bulldozer blade (feet)

Tire

a numeric vector, size of primary tires

Details

The goal is to predict the sale price of a particular piece of heavy equiment at auction based on its usage, equipment type, and configuration. The data represents a heavily modified version of competition data found on kaggle.com. See original source for actual dataset

References

https://www.kaggle.com/c/bluebook-for-bulldozers

Description

The BULLDOZER dataset but with the year the dozer was made as a categorical variable

Usage

data("BULLDOZER2")

Format

A data frame with 924 observations on the following 6 variables.

Price

a numeric vector

YearsAgo

a numeric vector

Usage

a numeric vector

Tire

a numeric vector

Decade

a factor with levels 1960s and 1970s 1980s 1990s 2000s

BladeSize

a numeric vector

Details

This is the BULLDOZER data except here YearMade has been coded into a four level categorical varaible called Decade

Description

Summary of students' cell phone providers and relative frequency of dropped calls

Usage

data("CALLS")

Format

A data frame with 579 observations on the following 2 variables.

Provider

a factor with levels ATT Sprint USCellular Verizon

DropCallFreq

a factor with levels Occasionally Often Rarely

Details

Data is self-reported by students. The dropped call frequency is based on individuals' perceptions and not any independent quantititatve measure. The data is a subset of SURVEY09.

Source

Student survey from STAT 201, University of Tennessee Knoxville, Fall 2009

Description

Information from the 2010 US Census

Usage

data("CENSUS")

Format

A data frame with 3534 observations on the following 39 variables.

ResponseRate

a numeric vector, 0-100 representing the percentage of households in a block group that mailed in the form

Area

a numeric vector, land area in square miles

Urban

a numeric vector, percentage of block group in Urbanized area (50000 or greater)

Suburban

a numeric vector, percentage of block group in an Urban Cluster area (2500 to 49999)

Rural

a numeric vector, percentage of block group in an Urban Cluster area (2500 to 49999)

Male

a numeric vector, percentage of males

AgeLess5

a numeric vector, percentage of individuals aged less than 5 years old

Age5to17

a numeric vector

Age18to24

a numeric vector

Age25to44

a numeric vector

Age45to64

a numeric vector

Age65plus

a numeric vector

Hispanics

a numeric vector, percentage of individuals who identify as Hispanic

Whites

a numeric vector, percentage of individuals who identify as white (alone)

Blacks

a numeric vector

NativeAmericans

a numeric vector

Asians

a numeric vector

Hawaiians

a numeric vector

Other

a numeric vector, percentage of individuals who identify as another ethnicity

RelatedHH

a numeric vector, percentage of households where at least 2 members are related by birth, marriage, or adoption; same-sex couple households with no relatives of the householder present are not included

MarriedHH

a numeric vector, percentage of households in which the householder and his or her spouse are listed as members of the same household; does not include same-sex married couples

NoSpouseHH

a numeric vector, percentage of households with no spousal relationship present

FemaleHH

a numeric vector, percentage of households with a female householder and no husband of householder present

AloneHH

a numeric vector, percentage of households where householder is living alone

WithKidHH

a numeric vector, percentage of households which have at least one person under the age of 18

MedianHHIncomeBlock

a numeric vector, median income of households in the block group (from American Community Survey)

MedianHHIncomeCity

a numeric vector, median income of households in the tract

OccupiedUnits

a numeric vector, percentage of housing units that are occupied

RentingHH

a numeric vector, percentage of housing units occupied by renters

HomeownerHH

a numeric vector, percentage of housing units occupied by the owner

MobileHomeUnits

a numeric vector, percentage of housing units that are mobile homes (from American Community Survey)

CrowdedUnits

a numeric vector, percentage of housing units with more than 1 person per room on average

NoPhoneUnits

a numeric vector, percentage of housing units without a landline

NoPlumbingUnits

a numeric vector, percentage of housing units without active plumbing

NewUnits

a numeric vector, percentage of housing units constructed in 2010 or later

Population

a numeric vector, number of people in the block group

NumHH

a numeric vector, number of households in the block group

NumUnits

a numeric vector, number of housing units in the block group

logMedianHouseValue

a numeric vector, the logarithm of the median home value in the block group

Details

The goal is to predict ResponseRate from the other predictors. ResponseRate is the percentage of households in a block group that mailed in the census forms. A block group is on average about 40 blocks, each typically bounded by streets, roads, or water. The number of block groups per county in the US is typically between about 5 and 165 with a median of about 20.

References

See https://www2.census.gov/programs-surveys/research/guidance/planning-databases/2014/pdb-block-2014-11-20a.pdf for variable definitions.

Description

A portion of the CENSUS dataset used for illustration

Usage

data("CENSUSMLR")

Format

A data frame with 1000 observations on the following 7 variables.

Response

a numeric vector, percentage 0-100 of household that mailed in the census form

Population

a numeric vector, the number of people living in the census block based on 2010 census

ACSPopulation

a numeric vector, the number of people living in the census block based on 2010 census

Rural

a numeric vector, the number of people living in a rural area (in that census block)

Males

a numeric vector, the number of males living in the census block

Elderly

a numeric vector, the number of people aged 65+ living in the census block

Hispanic

a numeric vector, the number of people who self-identify as Hispanic in the census block

Details

See CENSUS data for more information.

Description

Charity data (adapted from a small section of a charity's donor database)

Usage

data("CHARITY")

Format

A data frame with 15283 observations on the following 11 variables.

Donate

a factor with levels Donate No

Homeowner

a factor with levels No Yes

Gender

a factor with levels F M

UnlistedPhone

a factor with levels No Yes

ResponseProportion

a numeric vector giving the fraction of solications that resulted in a donation

NumResponses

a numeric vector giving the number of past donations

CardResponseCount

a numeric vector giving the number of past solicitations

MonthsSinceLastResponse

a numeric vector giving the number of months since last response to solicitation (which may have been declining to give)

LastGiftAmount

a numeric vector giving the amount of the last donation

MonthSinceLastGift

a numeric vector giving the number of months since last donation

LogIncome

a numeric vector giving the logarithm of a scaled and normalized yearly income

Details

This dataset is adapted from a real-world database of donors to a charity.

Source

Unknown

Description

If the model is a linear regression, obtain tests of linearity, equal spread, and Normality as well as relevant plots (residuals vs. fitted values, histogram of residuals, QQ plot of residuals, and predictor vs. residuals plots). If the model is a logistic regression model, a goodness of fit test is given.

Usage

check_regression(M,extra=FALSE,tests=TRUE,simulations=500,n.cats=10,seed=NA,prompt=TRUE)

Arguments

Details

This function provides standard visual and statistical diagnostics for regression models.

For linear regression, tests of linearity, equal spread, and Normality are performed and residuals plots are generated.

The test for linearity (a goodness of fit test) is an F-test. A simple linear regression model predicting y from x is fit and compared to a model treating each value of the predictor as some level of a categorical variable. If this more sophisticated model does not offer a significant improvement in the sum of squared errors, the linearity assumption in that predictor is reasonable. If the p-value is larger 0.05, then statistically we can consider the relationship to be linear. If the p-value is smaller than 0.05, check the residuals plot and the predictor vs residuals plots for signs of obvious curvature (the test can be overly sensitive to inconsequential violations for larger sample sizes). The test can only be run if are two or more individuals that have a common value of x. A test of the model as a whole is run similarly if at least two individuals have identical combinations of all predictor variables.

Note: if categorical variables, interactions, polynomial terms, etc., are present in the model, the test for linearity is conducted for each term even when it does not necessarily make sense to do so.

The test for equal spread is the Breusch-Pagan test. If the p-value is larger 0.05, then statistically we can consider the residuals to have equal spread everywhere. If the p-value is smaller than 0.05, check the residuals plot for obvious signs of unequal spread (the test can be overly sensitive to inconsequential violations for larger sample sizes).

The test for Normality is the Shapiro-Wilk test when the sample size is smaller than 5000, or the KS-test for larger sample sizes. If the p-value is larger 0.05, then statistically we can consider the residuals to be Normally distributed. If the p-value is smaller than 0.05, check the histogram and QQ plot of residuals to look for obvious signs of non-Normality (e.g., skewness or outlier). The test can be overly sensitive to inconsequential violations for larger sample sizes.

The first three plots displayed are the residuals plot (residuals vs. fitted values), histogram of residuals, and QQ plot of residuals. The function gives the option of pressing Enter to display additional predictor vs. residual plots if extra=TRUE, or to terminate by typing 'q' in the console and pressing Enter. If polynomial or interactions terms are present in the model, a plot is provided for each term. If categorical predictors are present, plots are provided for each indicator variable.

For logistic regression, two goodness of fit tests are offered.

Method 1 is a crude test that assumes the fitted logistic regression is correct, then generates an artifical sample according the predicted probabilities. A chi-squared test is conducted that compares the observed levels to the predicted levels. The test is failed is the p-value is less than 0.05. The test is not sensitive to departures from the logistic curve unless the sample size is very large or the logistic curve is a really bad model.

Method 2 is a Hosmer-Lemeshow type goodness of fit test. The observations are put into 10 groups according to the probability predicted by the logistic regression model. For example, if there were 200 observations, the first group would have the cases with the 20 smallest predicted probabilities, the second group would have the cases with the 20 next smallest probabilities, etc. The number of cases with the level of interest is compared with the expected number given the fitted logistic regression model via a chi-squared test. The test is failed is the p-value is less than 0.05.

Note: for both methods, the p-values of the chi-squared tests are estimate via Monte Carlo simulation instead of any asymptotic results.

Author(s)

Adam Petrie

References

Introduction to Regression and Modeling

See Also

lm, glm, shapiro.test, ks.test, bptest (in package lmtest). The goodness of fit test for logistic regression is further detailed and implemented in package 'rms' using the commands lrm and residuals.

Examples

#Simple linear regression where everything looks good 
  data(FRIEND)
  M <- lm(FriendshipPotential~Attractiveness,data=FRIEND)
  check_regression(M)
  
  #Multiple linear regression (prompt is FALSE only for documentation)
  data(AUTO)
  M <- lm(FuelEfficiency~.,data=AUTO)
  check_regression(M,extra=TRUE,prompt=FALSE)
  
  
  #Multiple linear regression with a categorical predictors and an interaction
  data(TIPS)
  M <- lm(TipPercentage~Bill*PartySize*Weekday,data=TIPS)
  check_regression(M)
  
  #Multiple linear regression with polynomial term (prompt is FALSE only for documentation)
  #Note:  in this example only plots are provided
  data(BULLDOZER)
  M <- lm(SalePrice~.-YearMade+poly(YearMade,2),data=BULLDOZER)
  check_regression(M,extra=TRUE,tests=FALSE,prompt=FALSE)

  #Simple logistic regression.  Use 8 categories since only 8 unique values of Dose
  data(POISON)
	M <- glm(Outcome~Dose,data=POISON,family=binomial)
	check_regression(M,n.cats=8,seed=892)

  #Multiple logistic regression
  data(WINE)
  M <- glm(Quality~.,data=WINE,family=binomial)
	check_regression(M,seed=2010)

Description

This function takes a simple linear regression model and displays the adjusted R^2 and AICc for the original model (order 1) and for polynomial models up to a specified maximum order and plots the fitted models.

Usage

choose_order(M,max.order=6,sort=FALSE,loc="topleft",...)

Arguments

Details

The function outputs a table of the order of the polynomial and the according adjusted R^2 and AICc. One strategy for picking the best order is to find the highest value of R^2 adjusted, then to choose the smallest order (simplest model) that has an R^2 adjusted within 0.005. Another strategy is the find the lowest value of AICc, then to choose the smallest order that has an AICc no more than 2 higher.

The scatterplot of the data is provided and the fitted models are displayed as well.

Author(s)

Adam Petrie

References

Introduction to Regression and Modeling

Examples

data(BULLDOZER)
	M <- lm(SalePrice~YearMade,data=BULLDOZER)
  #Unsorted list, messing with plot options to make it look alright
	choose_order(M,pch=20,cex=.3)
	
	#Sort by R2adj.  A 10th order polynomial is highest, but this seems overly complex
	choose_order(M,max.order=10,sort=TRUE)

	#Sort by AICc.  4th order is lowest, but 2nd order is simpler and within 2 of lowest
	choose_order(M,max.order=10,sort="aic")

Description

Churn data (artificial based on claims similar to real world) from the UCI data repository

Usage

data("CHURN")

Format

A data frame with 5000 observations on the following 18 variables.

churn

a factor with levels No Yes

accountlength

a numeric vector

internationalplan

a factor with levels no yes

voicemailplan

a factor with levels no yes

numbervmailmessages

a numeric vector

totaldayminutes

a numeric vector

totaldaycalls

a numeric vector

totaldaycharge

a numeric vector

totaleveminutes

a numeric vector

totalevecalls

a numeric vector

totalevecharge

a numeric vector

totalnightminutes

a numeric vector

totalnightcalls

a numeric vector

totalnightcharge

a numeric vector

totalintlminutes

a numeric vector

totalintlcalls

a numeric vector

totalintlcharge

a numeric vector

numbercustomerservicecalls

a numeric vector

Details

This dataset is modified from the one stored at the UCI data repository (namely, the area code and phone number have been deleted). This is artificial data similar to what is found in actual customer profiles. Charges are in dollars.

Source

Though originally on the UCI data repository, actual data was obtained via https://www.sgi.com/tech/mlc/db/

Description

This function takes a categorical variable and combines all levels with frequencies less than a user-specified threshold named Combined

Usage

combine_rare_levels(x,threshold=20,newname="Combined")

Arguments

Details

Returns a list of two objects:

values - The recoded values of the categorical variable. All levels which appeared threshold times or fewer are now known as Combined combined - The levels that have been combined together

If, after being combined, the newname level has threshold or fewer instances, the remaining level that appears least often is combined as well.

Author(s)

Adam Petrie

References

Introduction to Regression and Modeling

Examples

data(EX6.CLICK)
	x <- EX6.CLICK[,15]
	table(x)
	
	#Combine all levels which appear 700 or fewer times (AA, CC, DD)
	y <- combine_rare_levels(x,700)
  table( y$values )
  
  #Combine all levels which appear 1350 or fewer times.  This forces BB (which
  #occurs 2422 times) into the Combined level since the three levels that appear
  #fewer than 1350 times do not appear more than 1350 times combined
	y <- combine_rare_levels(x,1350)
  table( y$values )

Description

This function takes the output of a logistic regression created with glm and returns the confusion matrix.

Usage

confusion_matrix(M,DATA=NA)

Arguments

Details

This function makes classifications on the data used to build a logistic regression model by predicting the "level of interest" (last alphabetically) when the predicted probability exceeds 50%.

Author(s)

Adam Petrie

See Also

glm

Examples

#On WINE data as a whole
  data(WINE)
  M <- glm(Quality~.,data=WINE,family=binomial)
  confusion_matrix(M)
  
  #Calculate generalization error using training/holdout
  set.seed(1010)
  train.rows <- sample(nrow(WINE),0.7*nrow(WINE),replace=TRUE)
  TRAIN <- WINE[train.rows,]
  HOLDOUT <- WINE[-train.rows,]
  M <- glm(Quality~.,data=TRAIN,family=binomial)
	confusion_matrix(M,HOLDOUT)
	
	
	#Predicting donation
	#Model predicting from recent average gift amount is significant, but its
	#classifications are the same as the naive model (majority rules)
	data(DONOR)
	M.naive <- glm(Donate~1,data=DONOR,family=binomial)
	confusion_matrix(M.naive)
	M <- glm(Donate~RECENT_AVG_GIFT_AMT,data=DONOR,family=binomial)
	confusion_matrix(M)

Description

This function shows the correlation and coefficient of determination as user interactively adds datapoints. Useful for seeing what different values of correlation look like and seeing the effect of outliers.

Usage

cor_demo(cex.leg=0.8)

Arguments

Details

This function allows the user to generate data by click on a plot. Once two points are added, the correlation (r) and coefficient of determination (r^2) are displayed. When an additional point is added, these values are updated in the upper left with previous values being displayed in the upper right. The effect of outliers on the correlation and coefficient of determination can easily be illustrated. Pressing the red UNDO button on the plot will allow you to take away recently added points for further exploration.

Note: To end the demo, you MUST click on the red box labeled "End" (or press Escape, which will return an error)

Author(s)

Adam Petrie

Description

This function produces the matrix of correlations between all quantitative variables in a dataframe.

Usage

cor_matrix(X,type="pearson")

Arguments

Details

This function filters out any non-numerical variables and provides correlations only between quantitative variables. Best for datasets with only a few variables. The correlation matrix is returned (with class matrix).

Author(s)

Adam Petrie

References

Introduction to Regression and Modeling

See Also

cor

Examples

data(TIPS)
	cor_matrix(TIPS)
	data(AUTO)
	cor_matrix(AUTO,type="spearman")

Description

Customer database describing customer churn (adapted from a former case study)

Usage

data("CUSTCHURN")

Format

A data frame with 500 observations on the following 11 variables.

Duration

a numeric vector giving the days that the company was considered a customer. Note: censored at 730 days, which is the value for someone who is currently a customer (not churned)

Churn

a factor with levels N Y giving whether the customer has churned or not

RetentionCost

a numeric vector giving the average amount of money spent per year to retain the individual or company as a customer

EBiz

a factor with levels No Yes giving whether the customer was an e-business or not

CompanyRevenue

a numeric vector giving the company's revenue

CompanyEmployees

a numeric vector giving the number of employees working for the company

Categories

a numeric vector giving the number of product categories from which customer made a purchase of their lifetime

NumPurchases

a numeric vector giving the total amount of purchases over the customer's lifetime

Details

Each row corresponds to a customer of a Fortune 500 company. These customers are businesses, which may or may not exclusively be an e-business. Whether a customer is still a customer (or has churned) after 730 days is recorded.

Source

Unknown

Description

Customer database describing customer value (adapted from a former case study) and whether they have a loyalty card

Usage

data("CUSTLOYALTY")

Format

A data frame with 500 observations on the following 9 variables.

Gender

a factor with levels Female Male giving the customer's gender

Married

a factor with levels Married Single giving the customer's marital status

Income

a factor with levels f0t30, f30t45, f45t60, f60t75, f75t90, f90toINF giving the approximate yearly income of the customer. The first level corresponds to 30K or less, the second level corresponds to 30K to 45K, and the last level corresponds to 90K or above

FirstPurchase

a numeric vector giving the amount of the customer's first purchase amount

LoyaltyCard

a factor with levels No Yes that gives whether the customer has a loyalty card for the store

WalletShare

a numeric vector giving the percentage from 0 to 100 of similar products that the customer makes at this store. A value of 100 means the customer uses this store exclusively for such purchases.

CustomerLV

a numeric vector giving the lifetime value of the customer and reflects the amount spent acquiring and retaining the customer along with the revenue brought in by the customer

TotTransactions

a numeric vector giving the total number of consecutive months the customer has made a transaction in the last year

LastTransaction

a numeric vector giving the total amount of months since the customers last transaction

Details

Each row corresponds to a customer of a local chain. Does having a loyalty card increase the customer's value?

Source

Unknown

Description

Customer reacquisition

Usage

data("CUSTREACQUIRE")

Format

A data frame with 500 observations on the following 9 variables.

Reacquire

a factor with levels No Yes indicating whether a customer who has previously churned was reacquired

Lifetime2

a numeric vector giving the days that the company was considered a customer

Value2

a numeric vector giving the lifetime value of the customer (related to the amount of money spent on reacquisition and the revenue brought in by the customer; can be negative)

Lifetime1

a numeric vector giving the days that the company was considered a customer before churning the first time

OfferAmount

a numeric vector giving the money equivalent of a special offer given to the former customer in an attempt to reacquire

Lapse

a numeric vector giving the number of days between the customer churning and the time of the offer

PriceChange

a numeric vector giving the percentage by which the typical product purchased by the customer has changed from the time they churned to the time the special offer was sent

Gender

a factor with levels Female Male giving the gender of the customer

Age

a numeric vector giving the age of the customer

Details

A company kept records of its success in reacquiring customers that had previously churned. Data is based on a previous case study.

Source

Unknown

Description

Customer database describing customer value (adapted from a former case study)

Usage

data("CUSTVALUE")

Format

A data frame with 500 observations on the following 11 variables.

Acquired

a factor with levels No Yes indicating whether a potential customer was acquired

Duration

a numeric vector giving the days that the company was considered a customer

LifetimeValue

a numeric vector giving the lifetime value of the customer (related to the amount of money spent on acquisition and the revenue brought in by the customer; can be negative)

AcquisitionCost

a numeric vector giving the amount of money spent attempting to acquire as a customer

RetentionCost

a numeric vector giving the average amount of money spent per year to retain the individual or company as a customer

NumPurchases

a numeric vector giving the total amount of purchases over the customer's lifetime

Categories

a numeric vector giving the number of product categories from which customer made a purchase of their lifetime

WalletShare

a numeric vector giving the percentage of purchases of similar products the customer makes with this company; a few values exceed 100 for some reason

EBiz

a factor with levels No Yes giving whether the customer was an e-business or not

CompanyRevenue

a numeric vector giving the company's revenue

CompanyEmployees

a numeric vector giving the number of employees working for the company

Details

Each row corresponds to a (potential) customer of a Fortune 500 company. These customers are businesses, which may or may not exclusively an e-business.

Source

Unknown

Description

The weight of a person over time who is dieting and exercising

Usage

data("DIET")

Format

A data frame with 35 observations on the following 2 variables.

Weight

a numeric vector, lbs

Day

a numeric vector, the number of days after the diet started

Details

This data was collected by the author and consists of his weight measured first thing in the morning over the course of amount a month. The scale round to the nearest 0.2 lbs.

Description

Adapted from the KDD-CUP-98 data set concerning data regarding donations made to a national veterans organization.

Usage

data("DONOR")

Format

A data frame with 19372 observations on the following 50 variables.

Donate

a factor with levels No Yes

Donation.Amount

a numeric vector

ID

a numeric vector

MONTHS_SINCE_ORIGIN

a numeric vector, number of months donor has been in the database

DONOR_AGE

a numeric vector

IN_HOUSE

a numeric vector, 1 if person has donated to the charity's “In House" program

URBANICITY

a factor with levels ? C R S T U

SES

a factor with levels ? 1 2 3 4, one of five possible codes indicating socioeconomic status

CLUSTER_CODE

a factor with levels . 01 02 ... 53, one of 54 possible cluster codes, which are unique in terms of socioeconomic status, urbanicity, ethnicity, and other demographic characteristics

HOME_OWNER

a factor with levels H U

DONOR_GENDER

a factor with levels A F M U

INCOME_GROUP

a numeric vector, but in reality one of 7 possible income groups inferred from demographics

PUBLISHED_PHONE

a numeric vector, listed (1) vs not listed (0)

OVERLAY_SOURCE

a factor with levels B M N P, source from which the donor was match; B is both sources and N is neither

MOR_HIT_RATE

a numeric vector, number of known times donor has responded to a mailed solicitation from a group other than the charity

WEALTH_RATING

a numeric vector, but in reality one of 10 groups based on demographics

MEDIAN_HOME_VALUE

a numeric vector, inferred from other variables

MEDIAN_HOUSEHOLD_INCOME

a numeric vector, inferred from other variables

PCT_OWNER_OCCUPIED

a numeric vector, percent of owner-occupied housing near where person lives

PER_CAPITA_INCOME

a numeric vector, of neighborhood in which person lives

PCT_ATTRIBUTE1

a numeric vector, percent of residents in person's neighborhood that are male and active military

PCT_ATTRIBUTE2

a numeric vector, percent of residents in person's neighborhood that are male and veterans

PCT_ATTRIBUTE3

a numeric vector, percent of residents in person's neighborhood that are Vietnam veterans

PCT_ATTRIBUTE4

a numeric vector, percent of residents in person's neighborhood that are WW2 veterans

PEP_STAR

a numeric vector, 1 if has achieved STAR donor status and 0 otherwise

RECENT_STAR_STATUS

a numeric vector, 1 if achieved STAR within last 4 years

RECENCY_STATUS_96NK

a factor with levels A (active) E (inactive) F (first time) L (lapsing)N (new) S (star donor) as of 1996.

FREQUENCY_STATUS_97NK

a numeric vector indicating number of times donated in last period (but period is determined by RECENCY STATUS 96NK)

RECENT_RESPONSE_PROP

a numeric vector, proportion of responses to the individual to the number of (card or other) solicitations from the charitable organization since four years ago

RECENT_AVG_GIFT_AMT

a numeric vector, average donation from the individual to the charitable organization since four years ago

RECENT_CARD_RESPONSE_PROP

a numeric vector, number of times the individual has responded to a card solicitation from the charitable organization since four years ago

RECENT_AVG_CARD_GIFT_AMT

a numeric vector, average donation from the individual in response to a card solicitation from the charitable organization since four years ago

RECENT_RESPONSE_COUNT

a numeric vector, number of times the individual has responded to a promotion (card or other) from the charitable organization since four years ago

RECENT_CARD_RESPONSE_COUNT

a numeric vector, number of times the individual has responded to a card solicitation from the charitable organization since four years ago

MONTHS_SINCE_LAST_PROM_RESP

a numeric vector, number of months since the individual has responded to a promotion by the charitable organization

LIFETIME_CARD_PROM

a numeric vector, total number of card promotions sent to the individual by the charitable organization

LIFETIME_PROM

a numeric vector, total number of promotions sent to the individual by the charitable organization

LIFETIME_GIFT_AMOUNT

a numeric vector, total lifetime donation amount from the individual to the charitable organization

LIFETIME_GIFT_COUNT

a numeric vector, total number of donations from the individual to the charitable organization

LIFETIME_AVG_GIFT_AMT

a numeric vector, lifetime average donation from the individual to the charitable organization

LIFETIME_GIFT_RANGE

a numeric vector, difference between maximum and minimum donation amounts from the individual

LIFETIME_MAX_GIFT_AMT

a numeric vector

LIFETIME_MIN_GIFT_AMT

a numeric vector

LAST_GIFT_AMT

a numeric vector

CARD_PROM_12

a numeric vector, number of card promotions sent to the individual by the charitable organization in the last 12 months

NUMBER_PROM_12

a numeric vector, number of promotions (card or other) sent to the individual by the charitable organization in the last 12 months

MONTHS_SINCE_LAST_GIFT

a numeric vector

MONTHS_SINCE_FIRST_GIFT

a numeric vector

FILE_AVG_GIFT

a numeric vector, same as LIFETIME_AVG_GIFT_AMT

FILE_CARD_GIFT

a numeric vector, lifetime average donation from the individual in response to all card solicitations from the charitable organization

Details

Originally, this data was used with the 1998 KDD competition (https://kdd.ics.uci.edu/databases/kddcup98/kddcup98.html). This particular version has been adapted from the version available in SAS Enterprise Miner (http://support.sas.com/documentation/cdl/en/emgsj/61207/PDF/default/emgsj.pdf Appendix 2 for descriptions of variable names). One goal is to determine whether a past donor donated in response to the 97NK mail solicitation and (if so), how much, based on age, gender, most recent donation amount, total gift amount, etc.

Description

Data on the College GPAs of students in an introductory statistics class

Usage

data("EDUCATION")

Format

A data frame with 607 observations on the following 18 variables.

CollegeGPA

a numeric vector

Gender

a factor with levels Female Male

HSGPA

a numeric vector, can range up to 5 if the high school allowed it

ACT

a numeric vector, ACT score

APHours

a numeric vector, number of AP hours student took in HS

JobHours

a numeric vector, number of hours student currently works on average

School

a factor with levels Private Public, type of HS

LanguagesSpoken

a numeric vector

HSHonorsClasses

a numeric vector, number of honors classes taken in HS

SmokeInHS

a factor with levels No Yes

PayCollegeNoLoans

a factor with levels No Yes, can the student and his/her family pay for the University of Tennessee without taking out loans?

ClubsInHS

a numeric vector, number of clubs belonged to in HS

JobInHS

a factor with levels No Yes, whether the student maintained a job at some point while in HS

Churchgoer

a factor with levels No Yes, answer to the question Do you regularly attend chruch?

Height

a numeric vector (inches)

Weight

a numeric vector (lbs)

Family

what position they are in the family, a factor with levels Middle Child Oldest Child Only Child Youngest Child

Pet

favorite pet, a factor with levels Both Cat Dog Neither

Details

Responses are from students in an introductory statistics class at the University of Tennessee in 2010. One goal to try to predict someone's college GPA from some of the students' characteristics. What information about a high school student could a college admission's counselor use to anticipate that student's performance in college?

Description

CENSUS data for Exercise 5 in Chapter 2

Usage

data("EX2.CENSUS")

Format

A data frame with 3534 observations on the following 41 variables.

ResponseRate

a numeric vector

Area

a numeric vector

Urban

a numeric vector

Suburban

a numeric vector

Rural

a numeric vector

Male

a numeric vector

Female

a numeric vector

AgeLess5

a numeric vector

Age5to17

a numeric vector

Age18to24

a numeric vector

Age25to44

a numeric vector

Age45to64

a numeric vector

Age65plus

a numeric vector

Hispanics

a numeric vector

Whites

a numeric vector

Blacks

a numeric vector

NativeAmericans

a numeric vector

Asians

a numeric vector

Hawaiians

a numeric vector

Other

a numeric vector

RelatedHH

a numeric vector

MarriedHH

a numeric vector

NoSpouseHH

a numeric vector

FemaleHH

a numeric vector

AloneHH

a numeric vector

WithKidHH

a numeric vector

MedianHHIncomeBlock

a numeric vector

MedianHHIncomeCity

a numeric vector

OccupiedUnits

a numeric vector

VacantUnits

a numeric vector

RentingHH

a numeric vector

HomeownerHH

a numeric vector

MobileHomeUnits

a numeric vector

CrowdedUnits

a numeric vector

NoPhoneUnits

a numeric vector

NoPlumbingUnits

a numeric vector

NewUnits

a numeric vector

Population

a numeric vector

NumHH

a numeric vector

NumUnits

a numeric vector

logMedianHouseValue

a numeric vector

Details

See CENSUS for variable descriptions (this data is nearly identical). The goal is to predict ResponseRate from the other predictors. ResponseRate is the percentage of households in a block group that mailed in the census forms. A block group is on average about 40 blocks, each typically bounded by streets, roads, or water. The number of block groups per county in the US is typically between about 5 and 165 with a median of about 20.

Description

TIPS data for Exercise 6 in Chapter 2

Usage

data("EX2.TIPS")

Format

A data frame with 244 observations on the following 8 variables.

Tip.Percentage

a numeric vector

Bill_in_USD

a numeric vector

Tip_in_USD

a numeric vector

Gender

a factor with levels Female Male

Smoker

a factor with levels No Yes

Weekday

a factor with levels Friday Saturday Sunday Thursday

Day_Night

a factor with levels Day Night

Size_of_Party

a numeric vector

Details

See TIPS for more details. This is the same dataset except that the names of the variables are different.

Description

ABALONE dataset for Exercise D in Chapter 3

Usage

data("EX3.ABALONE")

Format

A data frame with 1528 observations on the following 7 variables.

Length

a numeric vector

Diameter

a numeric vector

Height

a numeric vector

Whole.Weight

a numeric vector

Meat.Weight

a numeric vector

Shell.Weight

a numeric vector

Rings

a numeric vector

Details

Abalone are sea creatures that are considered a delicacy and have very pretty iridescent shells. See https://en.wikipedia.org/wiki/Abalone. Predicting the age of the abalone from physical measurements could be useful for harvesting purposes. Dimensions are in mm and weights are in grams. Rings is an indicator of the age of the abalone (Age is about 1.5 plus the number of rings).

Source

Data is adapted from the abalone dataset on UCI Data Repository https://archive.ics.uci.edu/ml/datasets/Abalone. Only the male abalone are represented in this dataset.

References

See page on UCI for full details of owner and donor of this data.

Description

Bodyfat data for Exercise F in Chapter 3

Usage

data("EX3.BODYFAT")

Format

A data frame with 20 observations on the following 4 variables.

Triceps

a numeric vector

Thigh

a numeric vector

Midarm

a numeric vector

Fat

a numeric vector

Details

Same data as BODYFAT2, which you can see for more details.

Description

Housing data for Exercise E in Chapter 3

Usage

data("EX3.HOUSING")

Format

A data frame with 522 observations on the following 2 variables.

AREA

a numeric vector, square area of house

PRICE

a numeric vector, selling price

Details

Selling prices of houses (perhaps in the Boston area in Massachusettes).

Source

Original source unknown, but it appears in many places around the internet, e.g., public.iastate.edu/~pdixon/stat500/data/realestate.txt

Description

NFL data for Exercise A in Chapter 3

Usage

data("EX3.NFL")

Format

A data frame with 352 observations on the following 137 variables.

Year

a numeric vector

Team

a factor with levels Arizona Atlanta Baltimore Buffalo Carolina Chicago Cincinnati Cleveland Dallas Denver Detroit GreenBay Houston Indianapolis Jacksonville KansasCity Miami Minnesota NewEngland NewOrleans NYGiants NYJets Oakland Philadelphia Pittsburgh SanDiego SanFrancisco Seattle St.Louis TampaBay Tennessee Washington

Next.Years.Wins

a numeric vector

Wins

a numeric vector

X1.Off.Tot.Yds

a numeric vector

X2.Off.Tot.Plays

a numeric vector

X3.Off.Tot.Yds.per.Ply

a numeric vector

X4.Off.Tot.1st.Dwns

a numeric vector

X5.Off.Pass.1st.Dwns

a numeric vector

X6.Off.Rush.1st.Dwns

a numeric vector

X7.Off.Tot.Turnovers

a numeric vector

X8.Off.Fumbles.Lost

a numeric vector

X9.Off.1st.Dwns.by.Penalty

a numeric vector

X10.Off.Pass.Comp

a numeric vector

X11.Off.Pass.Comp.

a numeric vector

X12.Off.Pass.Yds

a numeric vector

X13.Off.Pass.Tds

a numeric vector

X14.Off.Pass.INTs

a numeric vector

X15.Off.Pass.INT.

a numeric vector

X16.Off.Pass.Longest

a numeric vector

X17.Off.Pass.Yds.per.Att

a numeric vector

X18.Off.Pass.Adj.Yds.per.Att

a numeric vector

X19.Off.Pass.Yds.per.Comp

a numeric vector

X20.Off.Pass.Yds.per.Game

a numeric vector

X21.Off.Passer.Rating

a numeric vector

X22.Off.Pass.Sacks.Alwd

a numeric vector

X23.Off.Pass.Sack.Yds

a numeric vector

X24.Off.Pass.Net.Yds.per.Att

a numeric vector

X25.Off.Pass.Adj.Net.Yds.per.Att

a numeric vector

X26.Off.Pass.Sack.

a numeric vector

X27.Off.Game.Winning.Drives

a numeric vector

X28.Off.Rush.Yds

a numeric vector

X29.Off.Rush.Tds

a numeric vector

X30.Off.Rush.Longest

a numeric vector

X31.Off.Rush.Yds.per.Att

a numeric vector

X32.Off.Rush.Yds.per.Game

a numeric vector

X33.Off.Fumbles

a numeric vector

X34.Off.Punt.Returns

a numeric vector

X35.Off.PR.Yds

a numeric vector

X36.Off.PR.Tds

a numeric vector

X37.Off.PR.Longest

a numeric vector

X38.Off.PR.Yds.per.Att

a numeric vector

X39.Off.Kick.Returns

a numeric vector

X40.Off.KR.Yds

a numeric vector

X41.Off.KR.Tds

a numeric vector

X42.Off.KR.Longest

a numeric vector

X43.Off.KR.Yds.per.Att

a numeric vector

X44.Off.All.Purpose.Yds

a numeric vector

X45.X1.19.yd.FG.Att

a numeric vector

X46.X1.19.yd.FG.Made

a numeric vector

X47.X20.29.yd.FG.Att

a numeric vector

X48.X20.29.yd.FG.Made

a numeric vector

X49.X1.29.yd.FG.

a numeric vector

X50.X30.39.yd.FG.Att

a numeric vector

X51.X30.39.yd.FG.Made

a numeric vector

X52.X30.39.yd.FG.

a numeric vector

X53.X40.49.yd.FG.Att

a numeric vector

X54.X40.49.yd.FG.Made

a numeric vector

X55.X50yd.FG.Att

a numeric vector

X56.X50yd.FG.Made

a numeric vector

X57.X40yd.FG.

a numeric vector

X58.Total.FG.Att

a numeric vector

X59.Off.Tot.FG.Made

a numeric vector

X60.Off.Tot.FG.

a numeric vector

X61.Off.XP.Att

a numeric vector

X62.Off.XP.Made

a numeric vector

X63.Off.XP.

a numeric vector

X64.Off.Times.Punted

a numeric vector

X65.Off.Punt.Yards

a numeric vector

X66.Off.Longest.Punt

a numeric vector

X67.Off.Times.Had.Punt.Blocked

a numeric vector

X68.Off.Yards.Per.Punt

a numeric vector

X69.Fmbl.Tds

a numeric vector

X70.Def.INT.Tds.Scored

a numeric vector

X71.Blocked.Kick.or.Missed.FG.Ret.Tds

a numeric vector

X72.Total.Tds.Scored

a numeric vector

X73.Off.2pt.Conv.Made

a numeric vector

X74.Def.Safeties.Scored

a numeric vector

X75.Def.Tot.Yds.Alwd

a numeric vector

X76.Def.Tot.Plays.Alwd

a numeric vector

X77.Def.Tot.Yds.per.Play.Alwd

a numeric vector

X78.Def.Tot.1st.Dwns.Alwd

a numeric vector

X79.Def.Pass.1st.Dwns.Alwd

a numeric vector

X80.Def.Rush.1st.Dwns.Alwd

a numeric vector

X81.Def.Turnovers.Created

a numeric vector

X82.Def.Fumbles.Recovered

a numeric vector

X83.Def.1st.Dwns.Alwd.by.Penalty

a numeric vector

X84.Def.Pass.Comp.Alwd

a numeric vector

X85.Def.Pass.Att.Alwd

a numeric vector

X86.Def.Pass.Comp..Alwd

a numeric vector

X87.Def.Pass.Yds.Alwd

a numeric vector

X88.Def.Pass.Tds.Alwd

a numeric vector

X89.Def.Pass.TDAlwd

a numeric vector

X90.Def.Pass.INTs

a numeric vector

X91.Def.Pass.INT.

a numeric vector

X92.Def.Pass.Yds.per.Att.Alwd

a numeric vector

X93.Def.Pass.Adj.Yds.per.Att.Alwd

a numeric vector

X94.Def.Pass.Yds.per.Comp.Alwd

a numeric vector

X95.Def.Pass.Yds.per.Game.Alwd

a numeric vector

X96.Def.Passer.Rating.Alwd

a numeric vector

X97.Def.Pass.Sacks

a numeric vector

X98.Def.Pass.Sack.Yds

a numeric vector

X99.Def.Pass.Net.Yds.per.Att.Alwd

a numeric vector

X100.Def.Pass.Adj.Net.Yds.per.Att.Alwd

a numeric vector

X101.Def.Pass.Sack.

a numeric vector

X102.Def.Rush.Yds.Alwd

a numeric vector

X103.Def.Rush.Tds.Alwd

a numeric vector

X104.Def.Rush.Yds.per.Att.Alwd

a numeric vector

X105.Def.Rush.Yds.per.Game.Alwd

a numeric vector

X106.Def.Punt.Returns.Alwd

a numeric vector

X107.Def.PR.Tds.Alwd

a numeric vector

X108.Def.Kick.Returns.Alwd

a numeric vector

X109.Def.KR.Yds.Alwd

a numeric vector

X110.Def.KR.Tds.Alwd

a numeric vector

X111.Def.KR.Yds.per.Att.Alwd

a numeric vector

X112.Def.Tot.FG.Att.Alwd

a numeric vector

X113.Def.Tot.FG.Made.Alwd

a numeric vector

X114.Def.Tot.FG..Alwd

a numeric vector

X115.Def.XP.Att.Alwd

a numeric vector

X116.Def.XP.Made.Alwd

a numeric vector

X117.Def.XP..Alwd

a numeric vector

X118.Def.Punts.Alwd

a numeric vector

X119.Def.Punt.Yds.Alwd

a numeric vector

X120.Def.Punt.Yds.per.Att.Alwd

a numeric vector

X121.Def.2pt.Conv.Alwd

a numeric vector

X122.Off.Safeties

a numeric vector

X123.Off.Rush.Success.Rate

a numeric vector

X124.Head.Coach.Disturbance.

a factor with levels No Yes

X125.QB.Disturbance

a factor with levels No Yes

X126.RB.Disturbance

a factor with levels ? No Yes

X127.Off.Run.Pass.Ratio

a numeric vector

X128.Off.Pass.Ply.

a numeric vector

X129.Off.Run.Ply.

a numeric vector

X130.Off.Yds.Pt

a numeric vector

X131.Def.Yds.Pt

a numeric vector

X132.Off.Pass.Drop.rate

a numeric vector

X133.Def.Pass.Drop.Rate

a numeric vector

Details

See NFL for more details. This dataset is actually a more complete version of NFL and contains additional variables such as the year, team, next year's wins of the team, etc., and could be used in place of the NFL data

Description

Bike data for Exercise 1 in Chapter 4

Usage

data("EX4.BIKE")

Format

A data frame with 414 observations on the following 5 variables.

Demand

a numeric vector, total number of rental bikes

AvgTemp

a numeric vector, average temperature of the day

EffectiveAvgTemp

a numeric vector, average temperature it feels like (taking into account dewpoint) for the day

AvgHumidity

a numeric vector, average humidity for the day

AvgWindspeed

a numeric vector, average wind speed for the day

Details

Adapted from the bike sharing dataset on the UCI data repository http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset. This concerns the demand for rental bikes in the DC area.

Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues.

Apart from interesting real world applications of bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important events in the city could be detected via monitoring these data.

References

Fanaee-T, Hadi, and Gama, Joao, Event labeling combining ensemble detectors and background knowledge, Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg.

Description

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

Usage

data("EX4.STOCKPREDICT")

Format

A data frame with 5 observations on the following 40 variables.

AAPLlag2

a numeric vector

AXPlag2

a numeric vector

BAlag2

a numeric vector

BAClag2

a numeric vector

CATlag2

a numeric vector

CSCOlag2

a numeric vector

CVXlag2

a numeric vector

DDlag2

a numeric vector

DISlag2

a numeric vector

GElag2

a numeric vector

HDlag2

a numeric vector

HPQlag2

a numeric vector

IBMlag2

a numeric vector

INTClag2

a numeric vector

JNJlag2

a numeric vector

JPMlag2

a numeric vector

KOlag2

a numeric vector

MCDlag2

a numeric vector

MMMlag2

a numeric vector

MRKlag2

a numeric vector

MSFTlag2

a numeric vector

PFElag2

a numeric vector

PGlag2

a numeric vector

Tlag2

a numeric vector

TRVlag2

a numeric vector

UNHlag2

a numeric vector

VZlag2

a numeric vector

WMTlag2

a numeric vector

XOMlag2

a numeric vector

Australialag2

a numeric vector

Copperlag2

a numeric vector

DollarIndexlag2

a numeric vector

Europelag2

a numeric vector

Exchangelag2

a numeric vector

GlobalDowlag2

a numeric vector

HongKonglag2

a numeric vector

Indialag2

a numeric vector

Japanlag2

a numeric vector

Oillag2

a numeric vector

Shanghailag2

a numeric vector

Details

The data frame for which you are to predict the closing price of Alcoa stock based on the model built using EX4.STOCKS. The actual closing prices are not given.

Description

Stock data for Exercise 2 in Chapter 4

Usage

data("EX4.STOCKS")

Format

A data frame with 216 observations on the following 41 variables.

AA

a numeric vector

AAPLlag2

a numeric vector

AXPlag2

a numeric vector

BAlag2

a numeric vector

BAClag2

a numeric vector

CATlag2

a numeric vector

CSCOlag2

a numeric vector

CVXlag2

a numeric vector

DDlag2

a numeric vector

DISlag2

a numeric vector

GElag2

a numeric vector

HDlag2

a numeric vector

HPQlag2

a numeric vector

IBMlag2

a numeric vector

INTClag2

a numeric vector

JNJlag2

a numeric vector

JPMlag2

a numeric vector

KOlag2

a numeric vector

MCDlag2

a numeric vector

MMMlag2

a numeric vector

MRKlag2

a numeric vector

MSFTlag2

a numeric vector

PFElag2

a numeric vector

PGlag2

a numeric vector

Tlag2

a numeric vector

TRVlag2

a numeric vector

UNHlag2

a numeric vector

VZlag2

a numeric vector

WMTlag2

a numeric vector

XOMlag2

a numeric vector

Australialag2

a numeric vector

Copperlag2

a numeric vector

DollarIndexlag2

a numeric vector

Europelag2

a numeric vector

Exchangelag2

a numeric vector

GlobalDowlag2

a numeric vector

HongKonglag2

a numeric vector

Indialag2

a numeric vector

Japanlag2

a numeric vector

Oillag2

a numeric vector

Shanghailag2

a numeric vector

Details

The goal is to predict the closing price of Alcoa stock (AA) from the closing prices of other stocks and commodities two days prior (IMBlag2, HongKonglag2, etc.). If this were possible, and if the association between the prices continued into the future, it would be possible to use this information to make smart trades.

Source

Compiled from various sources on the internet, e.g., Yahoo historical prices.

Description

BIKE dataset for Exercise 4 Chapter 5

Usage

data("EX5.BIKE")

Format

A data frame with 413 observations on the following 9 variables.

Demand

a numeric vector

Day

a factor with levels Friday Monday Saturday Sunday Thursday Tuesday Wednesday

Workingday

a factor with levels no yes

Holiday

a factor with levels no yes

Weather

a factor with levels No rain Rain

AvgTemp

a numeric vector

EffectiveAvgTemp

a numeric vector

AvgHumidity

a numeric vector

AvgWindspeed

a numeric vector

Details

Adapted from the bike sharing dataset on the UCI data repository http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset. This concerns the demand for rental bikes in the DC area. This is an expanded version of EX4.BIKE with more variables and without the row containing bad data.

Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues.

Apart from interesting real world applications of bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important events in the city could be detected via monitoring these data.

References

Fanaee-T, Hadi, and Gama, Joao, Event labeling combining ensemble detectors and background knowledge, Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg.

Description

DONOR dataset for Exercise 4 in Chapter 5

Usage

data("EX5.DONOR")

Format

A data frame with 8132 observations on the following 18 variables.

Donate

a factor with levels No Yes

LastAmount

a numeric vector

AccountAge

a numeric vector

Age

a numeric vector

Setting

a factor with levels Rural Suburban Urban

Homeowner

a factor with levels No Yes

Gender

a factor with levels Female Male Unknown

Phone

a factor with levels Listed Unlisted

Source

a factor with levels B M N P, source from which the donor was match; B is both sources and N is neither

MedianHomeValue

a numeric vector

MedianIncome

a numeric vector

PercentOwnerOccupied

a numeric vector, of the neighborhood in which donor lives

Recent

a factor with levels No Yes

RecentResponsePercent

a numeric vector

RecentAvgAmount

a numeric vector

MonthsSinceLastGift

a numeric vector

TotalAmount

a numeric vector

TotalDonations

a numeric vector

Details

See DONOR for details. This data is a subset, though attributes have been renamed.

Description

CLICK data for Exercise 2 in Chapter 6

Usage

data("EX6.CLICK")

Format

A data frame with 13594 observations on the following 15 variables.

Click

a factor with levels No Yes

BannerPosition

a factor with levels Pos1 Pos2, location of ad

SiteID

a factor with levels S1 S2 S3 S4 S5 S6 S7 S8

SiteDomain

a factor with levels SD1 SD2 SD3 SD4 SD5 SD6 SD7 SD8

SiteCategory

a factor with levels SCat1 SCat2 SCat3 SCat4 SCat5

AppDomain

a factor with levels AD1 AD2 AD3

AppCategory

a factor with levels AC1 AC2

DeviceModel

a factor with levels D1 D10 D11 D12 D13 D14 D15 D16 D17 D18 D2 D3 D4 D5 D6 D7 D8 D9

x1

a numeric vector

x2

a factor with levels A B C D E F G H I J K L M N O P Q R

x3

a factor with levels a b c d e f

x4

a factor with levels val1 val2 val3

x5

a factor with levels type1 type2 type3 type4

x6

a factor with levels class1 class2 class3 class4

x7

a factor with levels AA BB CC DD EE

Details

Inspired from a competition to predict the click-thru rates of ads displayed on mobile devices https://www.kaggle.com/c/avazu-ctr-prediction. Does the click-thru rate vary based on where the ad placed, what kind of site and device is used to view the ad, something else? All variables are anonymized.

Description

DONOR dataset for Exercise 1 in Chapter 6

Usage

data("EX6.DONOR")

Format

A data frame with 8132 observations on the following 18 variables.

Donate

a factor with levels No Yes

LastAmount

a numeric vector

AccountAge

a numeric vector

Age

a numeric vector

Setting

a factor with levels Rural Suburban Urban

Homeowner

a factor with levels No Yes

Gender

a factor with levels Female Male Unknown

Phone

a factor with levels Listed Unlisted

Source

a factor with levels B M N P

MedianHomeValue

a numeric vector

MedianIncome

a numeric vector

PercentOwnerOccupied

a numeric vector

Recent

a factor with levels No Yes

RecentResponsePercent

a numeric vector

RecentAvgAmount

a numeric vector

MonthsSinceLastGift

a numeric vector

TotalAmount

a numeric vector

TotalDonations

a numeric vector

Details

Identical to EX5.DONOR, so see that for details

Description

WINE data for Exercise 3 Chapter 6

Usage

data("EX6.WINE")

Format

A data frame with 2700 observations on the following 12 variables.

Quality

a factor with levels High Low

fixed.acidity

a numeric vector

volatile.acidity

a numeric vector

citric.acid

a numeric vector

residual.sugar

a numeric vector

free.sulfur.dioxide

a numeric vector

total.sulfur.dioxide

a numeric vector

density

a numeric vector

pH

a numeric vector

sulphates

a numeric vector

alcohol

a numeric vector

chlorides

a factor with levels Little Lots

Details

Adapted from the wine quality dataset at the UCI data repository. In this case, the original quality metric has been recoded from a score between 0 and 10 to either High or Low, and the chlorides is treated here as a categorical variable instead of a quantitative variable.

Source

https://archive.ics.uci.edu/ml/datasets/Wine+Quality

P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.

Description

BIKE dataset for Exercise 1 Chapters 7 and 8

Usage

data("EX7.BIKE")

Format

A data frame with 410 observations on the following 9 variables.

Demand

a numeric vector

Day

a factor with levels Friday Monday Saturday Sunday Thursday Tuesday Wednesday

Workingday

a factor with levels no yes

Holiday

a factor with levels no yes

Weather

a factor with levels No rain Rain

AvgTemp

a numeric vector

EffectiveAvgTemp

a numeric vector

AvgHumidity

a numeric vector

AvgWindspeed

a numeric vector

Details

Identical to EX5.BIKE except with three additional rows deleted. See that dataset for details.

Description

CATALOG data for Exercise 2 in Chapters 7 and 8

Usage

data("EX7.CATALOG")

Format

A data frame with 4000 observations on the following 7 variables.

Buy

a factor with levels No Yes, whether customer made a purchase through the catalog next quarter

QuartersWithPurchase

a numeric vector, number of quarters where customer made a purchase through the catalog

PercentQuartersWithPurchase

a numeric vector, percentage of quarters where customer made a purchase through the catalog

CatalogsReceived

a numeric vector, total number of catalogs customer has received

DaysSinceLastPurchase

a numeric vector, number of days since customer placed his or her last order

AvgOrderSize

a numeric vector, the typical number of items per order when customers buys through the catalog

LifetimeOrder

a numeric vector, the number of orders the customer has placed through the catalog

Details

The original source of this data is lost, but it is likely adapted from real data.

Description

Birthweight dataset for Exercise 1 in Chapter 9

Usage

data("EX9.BIRTHWEIGHT")

Format

A data frame with 553 observations on the following 13 variables.

Birthweight

a numeric vector, grams

Gestation

a numeric vector, weeks

MotherRace

a factor with levels Asian Black Mexican Mixed White, self-reported

MotherAge

a numeric vector, self-reported

MotherEducation

a factor with levels below HS College HS, self-reported

MotherHeight

a numeric vector, inches

MotherWeight

a numeric vector, pounds

FatherRace

a factor with levels Asian Black Mexican Mixed White, self-reported

FatherAge

a numeric vector, self-reported

Father_Education

a factor with levels below HS College HS, self-reported

FatherHeight

a numeric vector, inches

FatherWeight

a numeric vector, pounds

Smoking

a factor with levels never now, self-reported

Details

An examination of birthweights and their link to gestation, mother and father characteristics, and whether the mother smoked during pregnancy.

Source

Adapted from a subset of a study from Nolan and Speed (2000) consisting of male, single births which survived for at least 28 days. Some rows that contained bad data have been omitted. http://had.co.nz/stat645/week-05/birthweight.txt

Description

NFL data for Exercise 2 Chapter 9

Usage

data("EX9.NFL")

Format

A data frame with 352 observations on the following 26 variables.

Wins

a numeric vector

X1.OffTotPlays

a numeric vector

X2.OffTotYdsperPly

a numeric vector

X3.OffPass1stDwns

a numeric vector

X4.OffRush1stDwns

a numeric vector

X5.OffFumblesLost

a numeric vector

X6.OffPassComp

a numeric vector

X7.OffPassINT

a numeric vector

X8.OffPassLongest

a numeric vector

X9.OffPassYdsperAtt

a numeric vector

X10.OffPassYdsperComp

a numeric vector

X11.OffPassSackYds

a numeric vector

X12.OffPassSack

a numeric vector

X13.OffRushLongest

a numeric vector

X14.OffRushYdsperAtt

a numeric vector

X15.OffRushYdsperGame

a numeric vector

X16.OffFumbles

a numeric vector

X17.1to29ydFG

a numeric vector

X18.30to39ydFG

a numeric vector

X19.40.ydFG

a numeric vector

X20.TotalFGAtt

a numeric vector

X21.OffTimesPunted

a numeric vector

X22.OffTimesHadPuntBlocked

a numeric vector

X23.OffYardsPerPunt

a numeric vector

X24.Off2ptConvMade

a numeric vector

X25.OffSafeties

a numeric vector

Details

A subset of the NFL data (see entry for details) containing statistics on the offense.

Description

Data for Exercise 3 Chapter 9

Usage

data("EX9.STORE")

Format

A data frame with 1500 observations on the following 68 variables.

Store1

a factor with levels Buy No

Store2

a factor with levels Buy No

Store3

a factor with levels Buy No

Store4

a factor with levels Buy No

Store5

a factor with levels Buy No

Store6

a factor with levels Buy No

Store7

a factor with levels Buy No

Store8

a factor with levels Buy No

Store9

a factor with levels Buy No

Store10

a factor with levels Buy No

Store11

a factor with levels Buy No

Store12

a factor with levels Buy No

Store13

a factor with levels Buy No

Store14

a factor with levels Buy No

Store15

a factor with levels Buy No

Store16

a factor with levels Buy No

Store17

a factor with levels Buy No

Store18

a factor with levels Buy No

Store19

a factor with levels Buy No

Store20

a factor with levels Buy No

Store21

a factor with levels Buy No

Store22

a factor with levels Buy No

Store23

a factor with levels Buy No

Store24

a factor with levels Buy No

Store25

a factor with levels Buy No

Store26

a factor with levels Buy No

Store27

a factor with levels Buy No

Store28

a factor with levels Buy No

Store29

a factor with levels Buy No

Store30

a factor with levels Buy No

Store31

a factor with levels Buy No

Store32

a factor with levels Buy No

Store33

a factor with levels Buy No

Store34

a factor with levels Buy No

Store35

a factor with levels Buy No

Store36

a factor with levels Buy No

Store37

a factor with levels Buy No

Store38

a factor with levels Buy No

Store39

a factor with levels Buy No

Store40

a factor with levels Buy No

Store41

a factor with levels Buy No

Store42

a factor with levels Buy No

Store43

a factor with levels Buy No

Store44

a factor with levels Buy No

Store45

a factor with levels Buy No

Store46

a factor with levels Buy No

Store47

a factor with levels Buy No

Store48

a factor with levels Buy No

Store49

a factor with levels Buy No

Store50

a factor with levels Buy No

Store51

a factor with levels Buy No

Store52

a factor with levels Buy No

Store53

a factor with levels Buy No

Store54

a factor with levels Buy No

Store55

a factor with levels Buy No

Store56

a factor with levels Buy No

Store57

a factor with levels Buy No

Store58

a factor with levels Buy No

Store59

a factor with levels Buy No

Store60

a factor with levels Buy No

Store61

a factor with levels Buy No

Store62

a factor with levels Buy No

Store63

a factor with levels Buy No

Store64

a factor with levels Buy No

Store65

a factor with levels Buy No

Store66

a factor with levels Buy No

Store67

a factor with levels Buy No

Store68

a factor with levels Buy No

Details

The data consists of a random sample of 1500 credit card customers and their shopping habits regarding 68 different stores (whether they did or did not make a purchase in the last 90 days). Shoppers don't pick and choose places to shop at random, so it is interesting to study which stores appear together in a customers' history.

Source

Consultation with an anonymous client. Stores have been anonymized to protect the source.

Description

This function computes the Mahalanobis distance of points as a check for potential extrapolation.

Usage

extrapolation_check(M,newdata)

Arguments

Details

This function computes the shape of the predictor data cloud and calculates the distances of points from the center (with respect to the shape of the data cloud). Extrapolation occurs at a combination of predictors that is far from combinations used to build the model. An observation with a large Mahalanobis distance MAY be far from the observations used to build the model and thus MAY require extrapolation.

Note: analysis assumes the predictor data cloud is roughly elliptical (this may not be a good assumptions).

The function reports the percentiles of the Mahalanobis distances of the points in newdata. Percentiles are the fraction of observations used in model that are CLOSER to the center than the point(s) in question. Large values of these percentages indicate a greater risk for extrapolation. If Percentile is about 99 you may be extrapolating.

The method is sensitive to outliers clusters of outliers and gives only a crude idea of the potential for extrapolation.

Author(s)

Adam Petrie

References

Introduction to Regression and Modeling

See Also

mahalanobis

Examples

data(SALARY)
  M <- lm(Salary~Education*Experience+Months,data=SALARY)
  newdata <- data.frame(Education=c(0,5,10),Experience=c(15,15,15),Months=c(0,0,0))
  extrapolation_check(M,newdata) 
  #Individuals 1 and 3 are rather unusual (though not terribly) while individual 2 is typical.

Description

This function takes a simple linear regression model and finds the transformation of x and y that results in the highest R2

Usage

find_transformations(M,powers=seq(from=-3,to=3,by=.25),threshold=0.02,...)

Arguments

Details

The relationship between y and x may not be linear. However, some transformation of y may have a linear relationship with some transformation of x. This function considers simple linear regression with x and y raised to powers between -3 and 3 (in 0.25 increments) by default. The function outputs a list of the top models as gauged by R^2 (all models within 0.02 of the highest R^2). Note: there is no guarantee that these "best" transformations are actually good, since a large R^2 can be produced by outliers created during transformations. A plot of the transformation is also provided.

It is exceedingly rare that the "best" transformation is raising x and y to the 1 power (i.e., the original variables). Transformations are typically used only when there are issues in the residuals plots, highly skewed variables, or physical/logical justifications.

Note: if a variable has 0s or negative numbers, only integer transformations are considered.

Author(s)

Adam Petrie

References

Introduction to Regression and Modeling

Examples

#Straightforward example
  data(BULLDOZER)
	M <- lm(SalePrice~YearMade,data=BULLDOZER)
	find_transformations(M,pch=20,cex=0.3)

  #Results are very misleading since selected models have high R2 due to outliers
  data(MOVIE)
  M <- lm(Total~Weekend,data=MOVIE)
	find_transformations(M,powers=seq(-2,2,by=0.5),threshold=0.05)

Description

Examining the relationship between how likely someone would be friends with a person based on that person's level of attractiveness

Usage

data("FRIEND")

Format

A data frame with 54 observations on the following 2 variables.

Attractiveness

a numeric vector - the average scores (1-5) from about 80 male students who rated the attractiveness of the women in each picture

FriendshipPotential

a numeric vector - the average scores (1-5) from about 30 female students who rated how likely they would be to be friends with the pictured woman

Details

The data contain information on 54 pictures of women posted on the (now defunct/renamed) site hotornot.com. The women in two classes of introductory statistics at the University of Tennessee rated how likely they would be friends with the pictured women (on a scale of 1-5, 1 being very unlikely and 5 being very likely). The men in three (different) classes of introductory statistics gave an attractiveness score to each woman (on a scale of 1-5, 1 being very unattractive and 5 being very attractive). The numbers presented are the averages over all student ratings.

Source

Surveys administered to introductory statistics students at the University of Tennessee from 2008-2010.

Description

Wins vs. Fumbles of an NFL team

Usage

data("FUMBLES")

Format

A data frame with 352 observations on the following 2 variables.

Wins

a numeric vector, number of wins (0-16) of an NFL team over the course of a season

FumblesLost

a numeric vector, the number of fumbles lost by that team over the course of a season

Details

This is a subset of the NFL data. Data is from the 2002-2012 seasons.

Source

Collected by an undergraduate student from available web data in 2013.

Description

This function takes a linear regression from lm, logistic regression from glm, partition model from rpart, or random forest from randomForest and calculates the generalization error on a dataframe.

Usage

generalization_error(MODEL,HOLDOUT,Kfold=FALSE,K=5,R=10,seed=NA)

Arguments

Details

This function calculates the error on MODEL, its estimated generalization error from repeated K-fold cross-validation (for regression models only), and the actual generalization error on HOLDOUT. If the response is quantitative, the RMSE is reported. If the response is categorical, the confusion matrices and misclassification rates are returned.

Author(s)

Adam Petrie

References

Introduction to Regression and Modeling

Examples

#Education analytics
  data(STUDENT)
  set.seed(1010)
  train.rows <- sample(1:nrow(STUDENT),0.7*nrow(STUDENT))
  TRAIN <- STUDENT[train.rows,]
  HOLDOUT <- STUDENT[-train.rows,]
  M <- lm(CollegeGPA~.,data=TRAIN)
  #Also estimate the generalization error of the model
  generalization_error(M,HOLDOUT,Kfold=TRUE,seed=5020)
  #Try partition and randomforest, though they do not perform as well as regression here
  TREE <- rpart(CollegeGPA~.,data=TRAIN)
  FOREST <- randomForest(CollegeGPA~.,data=TRAIN)
  generalization_error(TREE,HOLDOUT)
  generalization_error(FOREST,HOLDOUT) 

  #Wine
  data(WINE)
  set.seed(2020)
  train.rows <- sample(1:nrow(WINE),0.7*nrow(WINE))
  TRAIN <- WINE[train.rows,]
  HOLDOUT <- WINE[-train.rows,]
  M <- glm(Quality~.^2,data=TRAIN,family=binomial)
  generalization_error(M,HOLDOUT)
  #Random forest predicts best on the holdout sample
  TREE <- rpart(Quality~.,data=TRAIN)
  FOREST <- randomForest(Quality~.,data=TRAIN)
  generalization_error(TREE,HOLDOUT)
  generalization_error(FOREST,HOLDOUT)

Description

A simple function to take the output of a partition model created with rpart and return information abouthe complexity parameter and performance of varies models.

Usage

getcp(TREE)

Arguments

Details

This function prints out a table of the complexity parameter, number of splits, relative error, cross validation error, and standard deviation of cross validation error for a partition model. It adds helpful advice for what the value of CP is for the tree that had the lowest cross validation error and also the value of CP for the simplest tree with a cross validation error at most 1 standard deviation above the lowest.

Further, a plot is made of the estimated generalization error (xerror) versus the number of splits to illustrate when the tree stops improving. Vertical lines are draw at the number of splits corresponding to the lowest estimated generalization error to the tree selected by the one standard deviation rule.

Author(s)

Adam Petrie

References

Introduction to Regression and Modeling

See Also

rpart

Examples

data(JUNK)
	TREE <- rpart(Junk~.,data=JUNK,control=rpart.control(cp=0,xval=10,minbucket=5))
	getcp(TREE)

Description

This function plots the leverage vs. deleted studentized residuals for a regression model, highlighting points that are influent based on these two factors as well as Cook's distance

Usage

influence_plot(M,large.cook,cooks=FALSE)

Arguments

Details

A point is influential if its addition to the data changes the regression substantially. One way of measuring influence is by looking at the point's leverage (distance from the center of the predictor's datacloud with respect to it shape) and deleted studentized residual (relative size of the residual with respect to a regression made without that point). Points with leverages larger than 2(k+1)/n (where k is the number of predictors) and deleted studentized residuals larger than 2 in magnitude are considered influential.

Influence can also be measured by Cook's distance, which essentially combines the above two measures. This function considers the Cook's distances to be large when it exceeds 4/n, but the user can specify another cutoff.

The radius of a point is proportional to the square root of the Cook's distance. Influential points according to leverage/residual criteria have an X through them while influential points according to Cook's distance are bolded.

The function returns the row numbers of influential observations.

Value

A list with the row numbers of influential points according to Cook's distance ($Cooks) and according to leverage/residual criteria ($Leverage).

Author(s)

Adam Petrie

References

Introduction to Regression and Modeling

See Also

cooks.distance, hatvalues, rstudent

Examples

data(TIPS)
  M <- lm(TipPercentage~.-Tip,data=TIPS)
	influence_plot(M)

Description

Building a junk mail classifier based on word and character frequencies

Usage

data("JUNK")

Format

A data frame with 4601 observations on the following 58 variables.

Junk

a factor with levels Junk Safe

make

a numeric vector, the percentage (0-100) of words in the email that are the word make

address

a numeric vector

all

a numeric vector

X3d

a numeric vector, the percentage (0-100) of words in the email that are the word 3d

our

a numeric vector

over

a numeric vector

remove

a numeric vector

internet

a numeric vector

order

a numeric vector

mail

a numeric vector

receive

a numeric vector

will

a numeric vector

people

a numeric vector

report

a numeric vector

addresses

a numeric vector

free

a numeric vector

business

a numeric vector

email

a numeric vector

you

a numeric vector

credit

a numeric vector

your

a numeric vector

font

a numeric vector

X000

a numeric vector, the percentage (0-100) of words in the email that are the word 000

money

a numeric vector

hp

a numeric vector

hpl

a numeric vector

george

a numeric vector

X650

a numeric vector

lab

a numeric vector

labs

a numeric vector

telnet

a numeric vector

X857

a numeric vector

data

a numeric vector

X415

a numeric vector

X85

a numeric vector

technology

a numeric vector

X1999

a numeric vector

parts

a numeric vector

pm

a numeric vector

direct

a numeric vector

cs

a numeric vector

meeting

a numeric vector

original

a numeric vector

project

a numeric vector

re

a numeric vector

edu

a numeric vector

table

a numeric vector

conference

a numeric vector

semicolon

a numeric vector, the percentage (0-100) of characters in the email that are semicolons

parenthesis

a numeric vector

bracket

a numeric vector

exclamation

a numeric vector

dollarsign

a numeric vector

hashtag

a numeric vector

capital_run_length_average

a numeric vector, average length of uninterrupted sequence of capital letters

capital_run_length_longest

a numeric vector, length of longest uninterrupted sequence of capital letters

capital_run_length_total

a numeric vector, total number of capital letters in the email

Details

The collection of junk emails came from the postmaster and individuals who classified the email as junk. The collection of safe emails were from work and personal emails. Note that most of the variables are percents and can vary from 0-100, though most values are much less than 1 (1%).

Source

Adapted from the Spambase Data Set at the UCI data repository https://archive.ics.uci.edu/ml/datasets/Spambase. Creators: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt; Hewlett-Packard Labs, 1501 Page Mill Rd., Palo Alto, CA 94304. Donor: George Forman (gforman at nospam hpl.hp.com)

Description

Interest in frequent flier program (artificial)

Usage

data("LARGEFLYER")

Format

A data frame with 100000 observations on the following 2 variables.

Gender

a factor with levels Female Male

Interest

a factor with levels No Yes

Details

This artificial datasets tabulates the interest in a new frequent flyer program based on gender. It illustrates that a statistically significant association may have absolutely no practical significance.

Description

The profit of newly released products over the first few months of their release

Usage

data("LAUNCH")

Format

A data frame with 652 observations on the following 420 variables.

Profit

an anonymized numeric vector, the profit from the product over the first few months of release

x1

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