| Title: | Datasets for Use with Salvan, Sartori and Pace (2020) |
|---|---|
| Description: | Contains the datasets for use with the book Salvan, Sartori and Pace (2020, ISBN:978-88-470-4002-1) "Modelli Lineari Generalizzati". |
| Authors: | Nicola Sartori, Alessandra Salvan, Luigi Pace |
| Maintainer: | Nicola Sartori <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.0 |
| Built: | 2024-12-12 06:46:13 UTC |
| Source: | CRAN |
Data on the weight loss due to abrasion, hardness and tensile strength for 30 samples of rubber.
AbrasionAbrasion
A data frame with 30 observations on the following 3 variables
perditaweight loss (in grams per hour)
Dhardness (in degrees Shore)
Re tensile strength (in kg/cm)
Hand, D.J., Daly, F., Lunn, A.D., McConway, K.J., Ostrowski, E. (1994). Small Data Sets. London Chapman and Hall/CRC.
Number of AIDS deaths in a sequence of three-months periods between 1983 and 1986.
AidsAids
Data frame with 14 observations on the following 2 variables
casesnumber of deaths
periodonumber of period
Dobson, A.J. (1990). An Introduction to Generalized Linear Models. London: CRC Press.
Alligator food choice data
AlligatorsAlligators
A data frame with 40 rows and 4 variables:
foodchoiceprimary food type, in volume, found in an alligator’s stomach, with levels fish, invertebrate, reptile, bird, other
lakelake of capture with levels Hancock, Oklawaha, Trafford, George
sizesize of the alligator with levels <=2.3 meters long and >2.3 meters long
Freqnumber of alligators for each foodchoice, lake, gender and size combination
The alligators data set is analysed in Agresti (2002, Subsection 7.1.2).
This is an edited version of the original data set, which is available at http://www.stat.ufl.edu/~aa/glm/data/
Agresti, A. (2002). Categorical Data Analysis. New York: Wiley.
The dataset refers to an experiment carried out by some students of an Australian university.
AntsAnts
Data frame with 48 observations on the following 5 variables
Breadinteger indicator for the kind of bread (1=rye, 2=wheatmeal, 3=multigrain, 4=white)
Fillinginteger indicator for the kind of filling (1=vegemite, 2=peanut butter, 3=ham and pickles)
Butterindicator for butter (1=butter, -1=no butter)
Ant_countnumber of captured ants
Orderorder of the experiment
Mackisack, M. (2017). What is the use of experiments conducted by Statistics students? Journal of Statistics Education, 2, 12-15.
The data refers to the number of business that have closed their activity in the first trimester of 2005 in 16 Italian regions.
AziendeAziende
Data frame with 16 observations on the following 4 variables
regioneinteger indicator for the region
numeronumber of closed businesses
dimensioneaverage dimension of the businesses
salarioaverage individual salary
Salvan, A., Sartori, N., Pace, L. (2020). Modelli lineari generalizzati. Milano: Springer-Verlag.
In an experiment to investigate the effect of cutting length (two levels) and planting time (two levels) on the survival of plum root cuttings, 240 cuttings were planted for each of the 2 x 2 combinations of these factors, and their survival was later recorded.
BartlettBartlett
A 3-dimensional array resulting from cross-tabulating 3 variables for 960 observations. The variable names and their levels are:
| No | Name | Levels |
| 1 | Alive
|
"Alive", "Dead"
|
| 2 | Time
|
"Now", "Spring"
|
| 3 | Length
|
"Long", "Short"
|
Hand, D. and Daly, F. and Lunn, A. D.and McConway, K. J. and Ostrowski, E. (1994). A Handbook of Small Data Sets. London: Chapman & Hall, p. 15, # 19.
Package vcdExtra
Bartlett, M. S. (1935). Contingency Table Interactions Journal of the Royal Statistical Society, Supplement, 1935, 2, 248-252.
Bartlett2 for the same data in data frame format
In an experiment to investigate the effect of cutting length (two levels) and planting time (two levels) on the survival of plum root cuttings, 240 cuttings were planted for each of the 2 x 2 combinations of these factors, and their survival was later recorded.
Bartlett2Bartlett2
A data frame with 4 rows and 4 columns related to the cross-classification of 960 observations. The variables are:
Alivenumber of plum root cuttings survived
Deadnumber of plum root cuttings dead
Timefactor w/ 2 levels (Now, Spring)
Lengthfactor w/ 2 levels (Long, Short)
Hand, D. and Daly, F. and Lunn, A. D.and McConway, K. J. and Ostrowski, E. (1994). A Handbook of Small Data Sets. London: Chapman & Hall, p. 15, # 19.
Bartlett, M. S. (1935). Contingency Table Interactions Journal of the Royal Statistical Society, Supplement, 1935, 2, 248-252.
Bartlett for the same data in table format
Number of adult flour beetles which died following a 5-hour exposure to gaseous carbon disulphide.
BeetlesBeetles
A data frame with 8 observations on the following 3 variables
numnumbers of beetles exposed
uccisinumbers of beetles dying
logdoseconcentration of carbon disulphide (mg. per litre) in logarithmic scale
Bliss, C. I. (1935).The calculation of the dosage-mortality curve. Annals of Applied BIology, 22, 134-167.
Beetles10 for an ungrouped version of this data
Survival adult flour beetles which died following a 5-hour exposure to gaseous carbon disulphide.
Beetles10Beetles10
A data frame with 481 observations on the following 2 variables
log.dose10concentration of carbon disulphide (mg. per litre) in logarithmic scale
uccisoindicator variable of death (0: survived, 1: dead)
Bliss, C. I. (1935).The calculation of the dosage-mortality curve. Annals of Applied BIology, 22, 134-167.
Beetles for a grouped version of these data
Number of events observed in a biological experiment with different dose exposure.
BioassayBioassay
A data frame with 10 observations on the following 3 variables
zdose level
dennumber of exposed
ynumber of observed events
Finney, D.J. (1947). Probit Analysis. Cambridge: Cambridge University Press.
A sample of 915 biochemistry graduate students.
BiochemistsBiochemists
Data frame with 915 observations on the following 6 variables
artcount of articles produced during last 3 years of Ph.D.
femfactor indicating gender of student, with levels Men and Women
marfactor indicating marital status of student, with levels Single and Married
kid5number of children aged 5 or younger
phdprestige of Ph.D. department
mentcount of articles produced by Ph.D. mentor during last 3 years
Package pscl
Long, J. Scott. 1990. The origins of sex differences in science. Social Forces. 68(3):1297-1316.
Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks, California: Sage.
Study on coronary deaths involving British doctors.
BritishdocBritishdoc
A data frame with 10 observations on the following 4 variables
agefactor with 5 levels (35-44, 45-54, 55-64, 65-74, 75-84)
smokefactor with 2 levels (n, y)
person.yearstotal number of observed person-years
deathsnumber of observed deaths by coronary disease
Agresti, A. (2015). Foundations of Linear and Generalized Linear Models. Hoboken: Wiley.
Data on the uptake of calcium by cells suspended in a radioactive solution, as a function of time.
A data frame with 27 observations on the following 2 variables
timeThe time (in minutes) that the cells were suspended in the solution
calThe amount of calcium uptake (nmoles/mg)
Howard Grimes from the Botany Department, North Carolina State University, conducted an experiment for biochemical analysis of intracellular storage and transport of calcium across plasma membrane. Cells were suspended in a solution of radioactive calcium for a certain length of time and then the amount of radioactive calcium that was absorbed by the cells was measured. The experiment was repeated independently with 9 different times of suspension each replicated 3 times.
Rawlings, J.O. (1988) Applied Regression Analysis. Wadsworth and Brooks/Cole Statistics/Probability Series.
Package SMPracticals
Davison, A. C. (2003) Statistical Models. Cambridge University Press. Page 469.
Experiment where different batches of cement were tested for tensile strength after different curing times.
CementCement
An object of class data.frame with 21 rows and 2 columns.
tempocuring times (in days)
resistenzatensile strength (kg/cm$^2$)
Hand, D.J., Daly, F., Lunn, A.D., McConway, K.J., Ostrowski, E. (1994). Small Data Sets. London Chapman and Hall/CRC.
These are the times in minutes taken for four chimpanzees to learn each of four words.
A data frame with 40 observations on the following 3 variables
chimpa factor with levels 1-4
worda factor with 1-10
ylearning time (minutes)
Brown, B. W. and Hollander, M. (1977) Statistics: A Biomedical Introduction. New York: Wiley.
Package SMPracticals
Davison, A. C. (2003) Statistical Models. Cambridge University Press. Page 485.
Bioassay on the action of the herbicide chlorsulfuron on the callus area of colonies of Brassica napus L. The experiment consists of 51 measurements for 10 different dose levels. The design is unbalanced: the number of replicates per dose varies from a minimum of 5 to a maximum of 8.
ChlorsulfuronChlorsulfuron
A data frame with 51 observations on the following 3 variables
gruppoindicator variable for each tested dose
dosethe tested dose (nmol/l)
areathe callus area (mm^2)
Package nlreg
Seiden, P., Kappel, D. e Streibig, J.C. (1998). Response of Brassica napus L. tissue culture to metsulfuron methyl and chlorsulfuron. Weed Research, 38, 221-228.
Mean blood clotting times in seconds for nine percentage concentrations of normal plasma and two lots of clotting agent.
ClottingClotting
Data frame with 18 observations on the following 3 variables
uplasma concentration (in precentage)
tempoclotting time (in seconds)
lottolot (factor with two levels: uno, due)
McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models (2nd Edition). London: Chapman and Hall.
Data for 1000 clients of a south german bank, 700 good payers and 300 bad payers. They are used to construct a credit scoring method.
Data frame with 1000 observations on the following 8 variables
Ya factor with levels
buen
mal, the response variable. buen is the good payers.
Cuentaa factor with levels
no
good running
bad running, quality of the credit clients bank account.
Mesa numeric vector, duration of loan in months.
Ppaga factor with levels
pre buen pagador
pre mal pagador, if the client previosly have been a
good or bad payer.
Usoa factor with levels
privado
profesional, the use to which the loan is made.
DMa numeric vector, the size of loan in german marks.
Sexoa factor with levels
mujer
hombre, sex of the client.
Estca factor with levels
no vive solo
vive solo, civil state of the client.
Fahrmeier, L. and Tutz, G. (2001) Multivariate Generalized Linear Models. New York: Springer Verlag.
Package Fahrmeir
Survey on the customer satisfaction among passengers of a certain bus line.
CustomerCustomer
A data frame with 12231 observations on the following 2 variables
ylevel of satisfaction, factor with 5 levels (Neutral, Satisfied, Unsatisfied, Very satisfied, Very unsatisfied)
delaybus delay (in minutes)
Madsen, H. e Thyregod, P. (2010). Introduction to General and Generalized Linear Models. Boca Raton, CRC Press.
Customer3 for the same data in table format
Survey on the customer satisfaction among passengers of a certain bus line.
Customer3Customer3
The data are stored as a frequency table. Data frame with 4 observations on the following 6 variables
delaybus delay (in minutes)
Verydissatisfiedfrequency of "Very dissatisfied" replies to the survey
Dissatisfiedfrequency of "Dissatisfied" replies to the survey
Neutralfrequency of "Neutral" replies to the survey
Satisfiedfrequency of "Satisfied" replies to the survey
Verysatisfiedfrequency of "Very satisfied" replies to the survey
Madsen, H. e Thyregod, P. (2010). Introduction to General and Generalized Linear Models. Boca Raton, CRC Press.
Customer for the individual level data
Measurements of left ventricular volume and parallel conductance volume on five dogs under eight different load conditions
DogsDogs
Data frame with 40 observations on the following 4 variables
dogdog number
conditionload condition
yleft ventricular volume
xparallel conductance volume
Package dobson
Dobson, A. J. and Barnett A. (2008). An Introduction to Generalized Linear Models, 3rd ed. Boca Raton: CRC Press.
Boltwood, C. M., R. Appleyard, and S. A. Glantz (1989). Left ventricular volume measurement by conductance catheter in intact dogs: the parallel conductance volume increases with end-systolic volume. Circulation 80, 1360–1377.
Survey on alcohol, cigarettes, or marijuana use collected on 2276 students in their final year of high school in a rural area near Dayton, Ohio.
DrugsDrugs
A data frame with 8 observations on the following 4 variables
alcalcohol use, factor with 2 levels (no, yes)
sigsigarettes use, factor with 2 levels (no, yes)
marmarijuana use, factor with 2 levels (no, yes)
countfrequency of students in the cross classification of the previous three variables
Agresti, A. (2015). Foundations of Linear and Generalized Linear Models. Hoboken: Wiley.
Drugs2 for a different format of the same data and Drugs3
for an extended version of the data with additional variables.
Survey on alcohol, cigarettes, or marijuana use made on 2276 students in their final year of high school in a rural area near Dayton, Ohio.
Drugs2Drugs2
A data frame with 4 observations on the following 5 variables
alcalcohol use, factor with 2 levels (no, yes)
sigsigarettes use, factor with 2 levels (no, yes)
M_yesfrequency of students that have tried marijuana
M_nofrequency of students that have never tried marijuana
nfrequency of students in the cross classification of variables alc and sig
Agresti, A. (2015). Foundations of Linear and Generalized Linear Models. Hoboken: Wiley.
Drugs for a different format of the same data and Drugs3
for an extended version of the data with additional variables.
Survey on alcohol, cigarettes, or marijuana use made on 2276 students in their final year of high school in a rural area near Dayton, Ohio.
Drugs3Drugs3
A data frame with 32 observations on the following 6 variables
alcoholalcohol use, factor with 2 levels (no, yes)
cigarettecigarettes use, factor with 2 levels (no, yes)
marijuanamarijuana use, factor with 2 levels (no, yes)
genderfactor with 2 levels (Female, Male)
racefactor with 2 levels (Other, White)
Freqfrequency of students in the cross classification of the previous five variables
Agresti, A. (2015). Foundations of Linear and Generalized Linear Models. Hoboken: Wiley.
Drugs and Drugs2 for a reduced version of this data,
with fewer variables, in two different formats.
Survey on the effect of recreational activities on university performance collected on 485 students.
EsitoEsito
A data frame with 18 observations on the following 4 variables
freqfrequency of students in the in the cross classification of the following three variables
sexfactor with 2 levels (f, m)
oreweekly hours of recreational activities, factor with 3 levels (m10, less than 10 hours;
m15, between 10 and 15 hours; m20, more than 15 hours)
votouniversity performance in a given exam, factor with 3 levels (ins, not sufficient;
suff, sufficient; buono, good)
Salvan, A., Sartori, N., Pace, L. (2020). Modelli lineari generalizzati. Milano: Springer-Verlag.
Factorial experiment on the germination of two different kind of seeds (Orobanche aegyptiaca 75 and Orobanche aegyptiaca 73) in two different experimental conditions (bean or cucumber root).
GerminationGermination
Data frame with 21 observations in the following 4 variables
snumber of germinated seeds
mtotal number of seeds
seedseed indicator, factor with 2 levels (073, 075)
rootroot indicator, factor with 2 levels (C, F)
Cox, D.R. e Snell, E.J. (1989). Analysis of Binary Data, 2nd ed. London: Chapman & Hall/CRC.
Data on diagnosed heart attacks in a sample of 360 patients hospitalized with suspected heart attack.
HeartHeart
Data frame with 13 observations and the following 4 variables
mckcentral value of the class of Creatinine kinase level in variable ck
ckclass of Creatinine kinase level (in IU per litre), factor with 13 levels
(Below 40, 40-80, ..., 480 and over)
hanumber of patients with diagnosed heart attack
nhanumber of patients without heart attack
Hand, D.J., Daly, F., Lunn, A.D., McConway, K.J., Ostrowski, E. (1994). Small Data Sets. London Chapman and Hall/CRC.
Survey on number of victims of murder known in the past year by race.
HomicideHomicide
A data frame with 1308 observations on the following 2 variables
raceindicator of self-identified race (0, white; 1, black)
countnumber of known victims of murder in the last year
Agresti, A. (2015). Foundations of Linear and Generalized Linear Models. Hoboken: Wiley.
http://www.stat.ufl.edu/~aa/glm/data
Study that relates the survival of infants to length of gestation, age and smoking habit of mothers.
InfantInfant
A data frame with 16 observations on the following 5 variables
survivalsurvival of the infant, factor with 2 levels (No, Yes)
gestationlength of gestation (in days), factor with 2 levels (<=260, >260)
smokingnumber of cigarettes per day smoked by the mother, factor with 2 levels (<5, >5)
ageage of the mother (in years), factor with 2 levels (<30, >30)
Freqfrequency of infant in the cross classification of the previous 4 variables
Agresti, A. (2013). Categorical Data Analysis, 3rd ed. New York: Wiley.
Data on children who have had corrective spinal surgery.
KyphosisKyphosis
Data frame with 81 observations on the following 4 variables
Kyphosisa factor with levels
absent
present
indicating if a kyphosis (a type of deformation)
was present after the operation.
Agein months
Numberthe number of vertebrae involved
Startthe number of the first (topmost) vertebra operated on.
Hastie, T.J. and Tibshirani, R.J. (1990). Generalized Additive Models. London: Chapman & Hall/CRC.
The dataset contains information on 8204 individuals enrolled in concurrent school and community cross-sectional surveys, conducted in 46 school clusters in the western Kenyan highlands. Malaria was assessed by rapid diagnostic test (RDT).
MalariaMalaria
The data frame has 8204 observations on the following variables
Clusterunique ID for each of the 46 school clusters
Longlongitude coordinate of the household location
Latlatitude coordinate of the household location
RDTbinary variable indicating the outcome of the RDT (1, positive; 0, negative)
Genderfactor variable indicating the gender of the sampled individual (Female, Male)
Ageage of the sampled individual (in years)
NetUsebinary variable indicating whether the sampled individual
slept under a bed net the previous night (1, yes; 0, no)
MosqCntlbinary variable indicating whether the household has used some kind of mosquito control,
such as sprays and coils (1, yes; 0, no)
IRSbinary variables in indicating whether there has been indoor residual spraying (IRS) in the
house in the last 12 months (1, yes; 0, no)
Travelbinary variable indicating whether the sampled individual has travelled outside the village
in the last three months (1, yes; 0, no)
SESordinal variable indicating the socio-economic status (SES) of the household.
The variable is an integer score from 1 (poor) to 5 (rich)
Districtfactor variable indicating the village of the sampled individual (Kisii Central, Rachuonyo)
Surveyfactor variables indicating the survey in which the participant was enrolled (community, school)
Stevenson, J.C., Stresman, G.H., Gitonga, C.W., Gillig, J., Owaga, C., Marube, E., Odongo, W., Okoth, A., China, P., Oriango, R. e Brooker, S.J. (2013). Reliability of school surveys in estimating geographic variation in malaria transmission in the western Kenyan highlands. PLoS One, 8, e77641.
Study of mental health for a random sample of adult residents of Alachua County, Florida.
MentalMental
Data frame with 40 observations in the following 3 variables
menommental health status on an ordinal scale (1, well; 2, mild symptom formation;
3, moderate symptom formation; 4, impaired)
sseSocioeconomic status (1, high; 0, low)
eventilife events index, a composite measure of the number and severity of important life events that occurred to the subject within the past 3 years, such as the birth of a child, a new job, a divorce, or a death in the family
Agresti, A. (2015). Foundations of Linear and Generalized Linear Models. Hoboken: Wiley.
Data on the weight at birth, the duration of the gestation, and the smoke habit of the mother for 32 newborns.
NeonatiNeonati
Data frame with 32 observations on the following 3 variables
pesoweigth at birth (in grams)
durataduration of gestation (in weeks)
fumoa factor with levels F (smoker), NF (non smoker)
Daniel, W.W. (1999). Biostatistics: A Foundation for Analysis in the Health Sciences. New York: Wiley.
The dataset is a subset of the six-city study, a longitudinal study of the health effects of air pollution.
OhioOhio
Data frame with 2148 observations on the following 4 variables
respan indicator of wheeze status (1=yes, 0=no)
ida numeric vector for subject id
agea numeric vector of age, 0 is 9 years old
smokean indicator of maternal smoking at the first year of the study
Package geepack
Fitzmaurice, G.M. and Laird, N.M. (1993) A likelihood-based method for analyzing longitudinal binary responses, Biometrika 80: 141–151.
Halekoh, U., Højsgaard, S. e Yan, J. (2005). The R package geepack for generalized estimating equations. Journal of Statistical Software, 15, 1-11.
Study of the change in an orthdontic measurement over time for 27 young subjects.
OrthodontOrthodont
Data frame with 27 observations in the following 5 variables
generegender of the subject, factor with 2 levels (F, M)
dist8ameasurement of the orthodontic distance (in mm) at age 8
dist10ameasurement of the orthodontic distance (in mm) at age 10
dist12ameasurement of the orthodontic distance (in mm) at age 12
dist14ameasurement of the orthodontic distance (in mm) at age 14
Pinheiro, J.C. and Bates, D.M. (2000). Mixed Effects Models in S and S-PLUS. New York: Springer.
Package nlme
Potthoff, R.F. and Roy, S.N. (1964). A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika, 51, 313-326.
Orthodont1 for the same data in an different format
Study of the change in an orthdontic measurement over time for 27 young subjects.
Orthodont1Orthodont1
Data frame with 108 observations in the following 4 variables
casosubject index
generegender of the subject, factor with 2 levels (F, M)
etaage of the subject
ymeasurement of the orthodontic distance (in mm)
Pinheiro, J.C. and Bates, D.M. (2000). Mixed Effects Models in S and S-PLUS. New York: Springer.
Package nlme
Potthoff, R.F. and Roy, S.N. (1964). A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika, 51, 313-326.
Orthodont for the same data in a different version
This gives the degree of pneumoconiosis (normal, present, or severe) in a group of coalminers as a function of the number of years worked at the coalface. The degree of the disease was assessed radiologically and is qualitative.
PneuPneu
A data frame with 8 observations on the following 4 variables
YearsPeriod of exposure (years worked at the coalface)
NormalNumber of miners with normal lungs
PresentNumber of miners with disease present
SevereNumber of miners with severe disease
Ashford, J. R. (1959) An approach to the analysis of data for semi-quantal responses in biological assay. Biometrics, 15, 573–581.
Package SMPracticals
Davison, A. C. (2003) Statistical Models. Cambridge University Press. Page 509.
Teratology experiment investigating effects of dietary regimens or chemical agents on the fetal development of rats in a laboratory setting. The experiment, as describred in Agresti (2015, Section 8.2.4), regards female rats on iron-deficient diets, assigned to four groups. Rats in group 1 were given placebo injections, and rats in other groups were given injections of an iron supplement. This was done on days 7 and 10 in group 2, on days 0 and 7 in group 3, and weekly in group 4. The 58 rats were made pregnant, sacrificed after 3 weeks, and then the total number of dead fetuses was counted in each litter, as was the mother’s hemoglobin level.
RatsRats
A data frame with 58 observations on the following 5 variables
litterlitter index
groupgroup index (1, ..., 4)
hhemoglobin level of the mother
nnumber of fetuses in the litter
snumber of dead fetuses in the litter
Agresti, A. (2015). Foundations of Linear and Generalized Linear Models. Hoboken: Wiley.
Package catdata
Moore, D.F. and Tsiatis, A. (1991). Robust estimation of the variance in moment methods for extra-binomial and extra-Poisson variation. Biometrics, 47, 383-401.
This is an artificial dataset representing an experiment relating probability of germination of seeds to the level of fertilizer used.
SeedSeed
A data frame with 20 observations on the following 2 variables
fertlevel of fertilizer used
xindicator of germination of the seed(1, yes; 0, no)
Salvan, A., Sartori, N., Pace, L. (2020). Modelli lineari generalizzati. Milano: Springer-Verlag.
Data from a report of a survey which investigated whether snoring was related to heart disease. Those surveyed were classified according to the amount they snored, on the basis of reports from their spouses.
SnoreSnore
Data frame with 8 observations on the following 3 variables
patpresence of heart disease, factor with 2 levels (no, si)
russlevel of snoring, factor with 4 levels (mai, no snoring;
a volte, occasional snoring; spesso, snoring nearly every night;
sempre, alwayssnoring;)
freqfrequency observed in the cross classification of the previous 2 variables
Hand, D.J., Daly, F., Lunn, A.D., McConway, K.J., Ostrowski, E. (1994). Small Data Sets. London Chapman and Hall/CRC.
Subjects in a 1989 General Social Survey from the National Opinion Research Center in the U.S. were asked their opinions about government spending on the environment (e), health (h), assistance to big cities (c), and law enforcement (l).
SpendingSpending
A data frame with 81 observations on the following 5 variables
eopinion on spending on the environment (1, too little; 2, about right;
3, too much)
hopinion on spending on the health (1, too little; 2, about right;
3, too much)
copinion on spending on assistance to big cities (1, too little; 2, about right;
3, too much)
lopinion on spending on law enforcement (1, too little; 2, about right;
3, too much)
countfrequency of subjects in the cross classification of the previous 4 variables
Agresti, A. (2013). Categorical Data Analysis, 3rd ed. New York: Wiley.
http://users.stat.ufl.edu/~aa/cda/data.html
Longitudinal data from an experiment to promote the recovery of stroke patients in wide format. The response variable is the Bartel index with higher scores meaning better outcomes and a maximum score of 100.
StrokeStroke
A tibble with 24 observations and the following 10 variables
Subjectsubject number
Groupgroup; A=new occupational therapy intervention, B = existing stroke rehabilitation program in the same hospital as A, C = usual care in a different hospital
week1Bartel index in week 1
week2Bartel index in week 2
week3Bartel index in week 3
week4Bartel index in week 4
week5Bartel index in week 5
week6Bartel index in week 6
week7Bartel index in week 7
week8Bartel index in week 8
Dobson, A. J. and Barnett A. (2008). An Introduction to Generalized Linear Models, 3-rd ed. Boca Raton: CRC Press.
Package dobson
Stroke1 for the same data in an extended format.
Longitudinal data from an experiment to promote the recovery of stroke patients in wide format. The response variable is the Bartel index with higher scores meaning better outcomes and a maximum score of 100.
Stroke1Stroke1
A data frame with 192 observations on the following 4 variables
Subjectsubject indicator
Groupgroup indicator, factor with 3 levels (A, B, C)
Weekweek indicator
yBartel index
Dobson, A. J. and Barnett A. (2008). An Introduction to Generalized Linear Models, 3-rd ed. Boca Raton: CRC Press.
Stroke for the same data in a different format
Admission test for Statistical Sciences bachelor course at University of Padova in 2014/15. The data refers to the answers of 63 candidates to 10 questions on text comprehension.
TestingressoTestingresso
A data frame with 630 observations on the following 3 variables
yindicator variable of correct answer (1, correct; 0, wrong)
subjectcandidate indicator (1, ..., 63)
itemquestion indicator (1, ..., 10)
Salvan, A., Sartori, N., Pace, L. (2020). Modelli lineari generalizzati. Milano: Springer-Verlag.
Data from an insurance company, which record for each contract the kind of vehicle, together with some additional variables.
VehicleVehicle
A data frame with 2067 observations on the following 4 variables
ageage of the owner
mengender (1, man; 0, female)
urbanresidential area (1, urban; 0, rural)
vehkind of vehicle, factor with 3 levels (C, car; F, fourwheel; M, motorcycle)
http://www.ub.edu/rfa/R/regression_with_categorical_dependent_variables.html
Guillén, M. (2014). Regression with categorical dependent variables. In Predictive Modeling Applications in Actuarial Science - Volume I: Predictive Modeling Techniques, E.W. Frees, R.A. Derrig and G. Meyers (Eds.) pp. 65-86. Cambridge: Cambridge University Press.
The data show the number of cycles to failure of samples of worsted yarn under cycles of repeated loading. There are three experimental conditions arranged in a 3 x 3 x 3 factorial design.
WoolWool
Data frame with 27 observations on the following 4 variables
x1length of test specimen (-1, 250 mm; 0, 300 mm; 1, 350 mm)
x2amplitude of loading cycle (-1, 8 mm; 0, 9 mm; 1, 10 mm)
x3load (-1, 40 g; 0, 45 g; 1, 50 g)
ycycles to failure
Hand, D.J., Daly, F., Lunn, A.D., McConway, K.J., Ostrowski, E. (1994). Small Data Sets. London Chapman and Hall/CRC.