DTI Brain Imaging Data

Description

Diffusion Tensor Imaging (DTI) data comparing 6 dyslexic children with 6 normal controls, from Figures 15.9 and 15.10. Z scores were computed at 15,443 three-dimensional brain coordinates (voxels).

Format

A data frame with 15443 rows and 4 variables:

x

Voxel coordinate: back to front

y

Voxel coordinate: left to right

z

Voxel coordinate: bottom to top

Zscore

Z score comparing dyslexic vs normal controls at this voxel

Source

https://hastie.su.domains/CASI_files/DATA/DTI.csv

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Figures 15.9 and 15.10.

Examples

data(DTI)
str(DTI)

ALS Data

Description

Data on amyotrophic lateral sclerosis (Lou Gehrig's disease) from Section 17.2. There are 1822 observations on individuals with ALS. The goal is to predict the rate of progression dFRS of a functional rating score, using 369 predictors based on measurements (and derivatives of these) obtained from patient visits.

Format

A data frame with 1822 rows and 371 variables. The key variables are testset (logical indicator for training/test split) and dFRS (response: rate of progression of the ALS functional rating score). The 369 predictor variables include:

• Demographics: Age, Sex.Male, Sex.Female, and race indicators (Race...Caucasian, Race...Asian, etc.)

• Family history of neurological diseases in relatives (e.g., Father, Mother, Brother, Sister)

• Neurological disease indicators (e.g., Neurological.Disease.ALS, Neurological.Disease.PARKINSON.S.DISEASE)

• Site of onset (Site.of.Onset.Onset..Bulbar, Site.of.Onset.Onset..Limb)

• Symptoms (Symptom.Atrophy, Symptom.Cramps, Symptom.Fasciculations, Symptom.Speech, etc.)

• Study arm indicators (Study.Arm.ACTIVE, Study.Arm.PLACEBO)

• Clinical measurements with summary statistics (first, last, min, max, mean, sd, slope): ALSFRS scores, blood pressure, forced/slow vital capacity (fvc.liters, svc.liters), respiratory rate, weight, height

• ALSFRS subscale items: climbing.stairs, cutting, dressing, handwriting, salivation, speech, swallowing, turning, walking

Details

These data were kindly provided by Lester Mackey and Lilly Fang, who won the DREAM challenge prediction prize in 2012 (Kuffner et al., 2015). It includes some additional variables created by them. Their winning entry used Bayesian trees, not too different from random forests.

Source

https://hastie.su.domains/CASI_files/DATA/ALS.txt

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Section 17.2.

Examples

data(als)
str(als)

Baseball Batting Averages

Description

Batting averages for 18 Major League players in the 1970 season, from Table 7.1. This dataset illustrates empirical Bayes estimation, where early-season performance is used to predict full-season batting averages.

Format

A data frame with 18 rows and 3 variables:

Player

Player ID number

MLE

Batting average based on the first 90 at-bats of the season

TRUTH

Batting average for the remainder of the 1970 season

Source

https://hastie.su.domains/CASI_files/DATA/baseball.txt

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Table 7.1.

Examples

data(baseball)
str(baseball)

Bivariate Normal Data

Description

40 points generated from a bivariate normal distribution, with some entries missing. From Figure 9.3.

Format

A data frame with 40 rows and 2 variables:

X1

First variable

X2

Second variable

Source

https://hastie.su.domains/CASI_files/DATA/bivnorm.csv

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Figure 9.3.

Examples

data(bivnorm)
str(bivnorm)

Butterfly Species Data

Description

Number of butterfly species seen a given number of times each in two years of trapping. From Table 6.2. This is a frequency data frame.

Format

A data frame with 24 rows and 2 variables:

k

Number of times a species was trapped

count

Number of species seen exactly k times (e.g., 118 species trapped just once, 74 trapped twice each)

Source

https://hastie.su.domains/CASI_files/DATA/butterfly.txt

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Table 6.2.

Examples

data(butterfly)
str(butterfly)

Cell Infusion Data

Description

Human cell colonies infused with mouse nuclei in 5 different ratios over 1 to 5 days. From Table 8.2.

Format

A data frame with 25 rows and 4 variables:

thrived

Number of cells that thrived

N

Colony size (number of cells)

ratio

Ratio of mouse nuclei to human cells (1-5)

time

Day of observation (1-5)

Source

https://hastie.su.domains/CASI_files/DATA/cellinfusion.txt

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Table 8.2.

Examples

data(cellinfusion)
str(cellinfusion)

Cholesterol Data

Description

Cholestyramine, a proposed cholesterol lowering drug, was administered to 164 men for an average of seven years each. From Figure 20.1.

Format

A data frame with 164 rows and 2 variables:

compliance

Fraction of intended dose actually taken (standardized)

cholesterol.decrease

Decrease in cholesterol level over the course of the experiment

Source

https://hastie.su.domains/CASI_files/DATA/cholesterol.txt

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Figure 20.1.

Examples

data(cholesterol)
str(cholesterol)

Diabetes Data

Description

Data from 442 diabetes patients used in Section 7.3. The response is a quantitative measure of disease progression one year after baseline. There are ten baseline predictors: age, sex, body-mass index, average blood pressure, and six blood serum measurements.

Format

A data frame with 442 rows and 12 variables:

X

Row index

age

Age of patient

sex

Sex of patient

bmi

Body mass index

map

Average blood pressure (mean arterial pressure)

tc

Total cholesterol (serum measurement)

ldl

Low-density lipoproteins (serum measurement)

hdl

High-density lipoproteins (serum measurement)

tch

Total cholesterol / HDL (serum measurement)

ltg

Log of triglycerides (serum measurement)

glu

Blood sugar level (serum measurement)

prog

Response: quantitative measure of disease progression

Details

First used in the LARS paper (Efron et al., 2004).

Note: In Table 7.2, the centered predictor variables were standardized to unit L2 norm. In Table 20.1 they were standardized to unit variance.

Source

https://hastie.su.domains/CASI_files/DATA/diabetes.csv

References

Efron, B., Hastie, T., Johnstone, I. and Tibshirani, R. (2004). Least Angle Regression. Annals of Statistics, 32(2), 407-499.

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Section 7.3.

Examples

data(diabetes)
str(diabetes)

Dose Response Data

Description

Data from 11 groups of mice (10 each) exposed to drug Xilathon at different doses. From Figure 8.2.

Format

A data frame with 11 rows and 2 variables:

Dose

Log dose level (each step is a doubling)

Proportion

Proportion of mice that died at that dose

Source

https://hastie.su.domains/CASI_files/DATA/doseresponse.txt

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Figure 8.2.

Examples

data(doseresponse)
str(doseresponse)

Galaxy Data

Description

Counts of galaxies binned by redshift and magnitude, from Table 8.5. The data have been reshaped into long format with variables for magnitude, redshift category, and frequency count.

Format

A data frame with 270 rows and 3 variables:

mag

Magnitude category (1-18)

red

Redshift category (1-15)

freq

Number of galaxies in this bin

Source

https://hastie.su.domains/CASI_files/DATA/galaxy.txt

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Table 8.5.

Examples

data(galaxy)
str(galaxy)

library(car)

## Fit a main effects Poisson GLM
# This treats `mag` and `red` as numeric
galaxy.mod0 <- glm(freq ~ mag + red,
                   data = galaxy, family = poisson)
Anova(galaxy.mod0)

## Fit response surface model
galaxy.mod1 <- glm(freq ~ poly(mag,2) +
                     poly(red, 2) +
                     mag : red,
                   data = galaxy, family = poisson)
Anova(galaxy.mod1)
summary(galaxy.mod1)

Human Ancestry Haplotype Data

Description

Genotype data for 197 US individuals from 4 racial groups (African American, European, Japanese, and African) at 100 SNP locations. From Section 13.5.

Format

A data frame with 197 rows and 102 variables. The first column X is a row index, race is the racial/ethnic group identifier, and the remaining 100 columns (Snp1 through Snp100) contain genotype values (0, 1, or 2) at each SNP location, with NA for missing values.

Source

https://hastie.su.domains/CASI_files/DATA/haplotype.csv

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Section 13.5.

Examples

data(haplotype)
str(haplotype)

Insurance Life Table Data

Description

Insurance company life table from Table 9.1. At each age, gives the number of policy holders and the number of deaths.

Format

A data frame with rows for each age group and 3 variables:

age

Age of policy holders

n

Number of policy holders at this age

y

Number of deaths at this age

Source

https://hastie.su.domains/CASI_files/DATA/insurance.txt

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Table 9.1.

Examples

data(insurance)
str(insurance)

Leukemia Gene Expression Data (Small)

Description

Gene expression measurements on 72 leukemia patients: 47 ALL (acute lymphoblastic leukemia) and 25 AML (acute myeloid leukemia). From the landmark Golub et al. (1999) Science paper. This smaller subset contains 3571 genes and is used in Section 19.1.

Format

A data frame with 3571 rows (genes) and 72 columns (patients). Column names indicate the class label (ALL or AML) for each patient.

Details

A larger dataset with 7128 genes is also available from the CASI website.

Source

https://hastie.su.domains/CASI_files/DATA/leukemia_small.csv

References

Golub, T.R., et al. (1999). Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science, 286, 531-537.

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Section 19.1.

Examples

data(leukemia_small)
str(leukemia_small)

NCOG Head and Neck Cancer Data

Description

Head and neck cancer survival data from the Northern California Oncology Group (NCOG), from Section 9.2. Patients were randomized to one of two treatment arms.

Format

A data frame with survival time information and variables:

t

Time in months until death or censoring

d

Death indicator: 1 = death observed, 0 = censored

arm

Treatment arm: "A" = Chemotherapy, "B" = Chemotherapy + Radiation

day

Day of event/censoring

month

Month of event/censoring

year

Year of event/censoring

Source

https://hastie.su.domains/CASI_files/DATA/ncog.txt

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Section 9.2.

Examples

data(ncog)
str(ncog)

Lymph Nodes Cancer Data

Description

Data on lymph nodes removed from 844 cancer patients, from Figure 6.3.

Format

A data frame with 844 rows and 2 variables:

n

Number of lymph nodes removed

x

Number of nodes found to be positive (malignant)

Source

https://hastie.su.domains/CASI_files/DATA/nodes.txt

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Figure 6.3.

Examples

data(nodes)
str(nodes)

Pediatric Cancer Survival Data

Description

Survival data on 1620 children with cancer, from Section 9.4 and Table 9.6.

Format

A data frame with 1620 rows and 7 variables:

sex

Sex: 1 = male, 2 = female

race

Race: 1 = white, 2 = nonwhite

age

Age in years

entry

Calendar date of entry in days since July 1, 2001

far

Home distance from treatment center in miles

t

Survival time in days

d

Death indicator: 1 = death observed, 0 = censored

Source

https://hastie.su.domains/CASI_files/DATA/pediatric.txt

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Section 9.4, Table 9.6.

Examples

data(pediatric)
str(pediatric)

Police Racial Bias Data

Description

Z scores for 2749 New York City police officers, from Figure 15.7. A large value suggests racial bias in policing behavior.

Format

A data frame with 2749 rows and 1 variable:

z

Z score measuring potential racial bias

Source

https://hastie.su.domains/CASI_files/DATA/police.txt

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Figure 15.7.

Examples

data(police)
str(police)

Prostate Cancer Z-values

Description

Vector of 6033 z-values comparing gene expression between prostate cancer patients and controls, as pictured in Figure 3.4. These were computed as described on page 272.

Format

A data frame with 6033 rows and 1 variable:

z

Z-value for each gene comparing cancer vs control expression

Source

https://hastie.su.domains/CASI_files/DATA/prostz.txt

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Section 3.3, Figure 3.4.

Examples

data(prostz)
str(prostz)

Student Score Data

Description

Test scores for 22 students on 5 different exams, from Tables 3.1 and 10.1.

Format

A data frame with 22 rows and 5 variables:

mech

Mechanics exam score

vecs

Vectors exam score

alg

Algebra exam score

analy

Analysis exam score

stat

Statistics exam score

Source

https://hastie.su.domains/CASI_files/DATA/student_score.txt

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Tables 3.1 and 10.1.

Examples

data(student_score)
str(student_score)

Type Ia Supernova Data

Description

Measurements from 39 Type Ia supernovas, from Figure 12.1 and Table 12.1. These supernovas were close enough to Earth to observe their actual magnitudes. The goal is to predict magnitude from spectral energy measurements.

Format

A data frame with 39 rows and 11 variables:

Magnitude

Actual observed magnitude of the supernova

E1

Spectral energy in frequency band 1

E2

Spectral energy in frequency band 2

E3

Spectral energy in frequency band 3

E4

Spectral energy in frequency band 4

E5

Spectral energy in frequency band 5

E6

Spectral energy in frequency band 6

E7

Spectral energy in frequency band 7

E8

Spectral energy in frequency band 8

E9

Spectral energy in frequency band 9

E10

Spectral energy in frequency band 10

Source

https://hastie.su.domains/CASI_files/DATA/supernova.txt

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Figure 12.1, Table 12.1.

Examples

data(supernova)
str(supernova)

Vasoconstriction Data

Description

Data on vasoconstriction (lung constriction) response, from Table 13.2.

Format

A data frame with 39 rows and 2 variables:

volume

Volume measurement

constriction

Logical: TRUE if constriction occurred, FALSE otherwise

Source

https://hastie.su.domains/CASI_files/DATA/vasoconstriction.txt

References

Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Table 13.2.

Examples

data(vasoconstriction)
str(vasoconstriction)