Immunoglobulin G

Description

Serum IgM in 298 children aged 6 months to 6 years.

Usage

IgM

Format

A single numeric vector (g/l).

Source

D.G. Altman (1991), Practical Statistics for Medical Research, Table 3.2, Chapman & Hall.

Examples

stripchart(IgM,method="stack")

Alkaline phosphatase data

Description

Repeated measurements of alkaline phosphatase in a randomized trial of Tamoxifen treatment of breast cancer patients.

Usage

alkfos

Format

A data frame with 43 observations on the following 8 variables.

grp

a numeric vector, group code (1=placebo, 2=Tamoxifen).

c0

a numeric vector, concentration at baseline.

c3

a numeric vector, concentration after 3 months.

c6

a numeric vector, concentration after 6 months.

c9

a numeric vector, concentration after 9 months.

c12

a numeric vector, concentration after 12 months.

c18

a numeric vector, concentration after 18 months.

c24

a numeric vector, concentration after 24 months.

Source

Original data.

References

B. Kristensen et al. (1994), Tamoxifen and bone metabolism in postmenopausal low-risk breast cancer patients: a randomized study. Journal of Clinical Oncology, 12(2):992–997.

Ashina's crossover trial

Description

The ashina data frame has 16 rows and 3 columns. It contains data from a crossover trial for the effect of an NO synthase inhibitor on headaches. Visual analog scale recordings of pain levels were made at baseline and at five time points after infusion of the drug or placebo. A score was calculated as the sum of the differences from baseline. Data were recorded during two sessions for each patient. Six patients were given treatment on the first occasion and the placebo on the second. Ten patients had placebo first and then treatment. The order of treatment and the placebo was randomized.

Usage

ashina

Format

This data frame contains the following columns:

vas.active

a numeric vector, summary score when given active substance.

vas.plac

a numeric vector, summary score when given placebo treatment.

grp

a numeric vector code, 1: placebo first, 2: active first.

Source

Original data.

References

M.Ashina et al. (1999), Effect of inhibition of nitric oxide synthase on chronic tension-type headache: a randomised crossover trial. Lancet 353, 287–289

Examples

plot(vas.active~vas.plac,pch=grp,data=ashina)
abline(0,1)

Breast cancer mortality

Description

Danish study on the effect of screening for breast cancer.

Usage

bcmort

Format

A data frame with 24 observations on the following 4 variables.

age

a factor with levels 50-54, 55-59, 60-64, 65-69, 70-74, and 75-79

.

cohort

a factor with levels Study gr., Nat.ctr., Hist.ctr., and Hist.nat.ctr..

bc.deaths

a numeric vector, number of breast cancer deaths.

p.yr

a numeric vector, person-years under study.

Details

Four cohorts were collected. The “study group” consists of the population of women in the appropriate age range in Copenhagen and Frederiksberg after the introduction of routine mammography screening. The “national control group” consisted of the population in the parts of Denmark in which routine mammography screening was not available. These two groups were both collected in the years 1991–2001. The “historical control group” and the “historical national control group” are similar cohorts from 10 years earlier (1981–1991), before the introduction of screening in Copenhagen and Frederiksberg. The study group comprises the entire population, not just those accepting the invitation to be screened.

Source

A.H. Olsen et al. (2005), Breast cancer mortality in Copenhagen after introduction of mammography screening. British Medical Journal, 330: 220–222.

Obesity and blood pressure

Description

The bp.obese data frame has 102 rows and 3 columns. It contains data from a random sample of Mexican-American adults in a small California town.

Usage

bp.obese

Format

This data frame contains the following columns:

sex

a numeric vector code, 0: male, 1: female.

obese

a numeric vector, ratio of actual weight to ideal weight from New York Metropolitan Life Tables.

bp

a numeric vector,systolic blood pressure (mm Hg).

Source

B.W. Brown and M. Hollander (1977), Statistics: A Biomedical Introduction, Wiley.

Examples

plot(bp~obese,pch = ifelse(sex==1, "F", "M"), data = bp.obese)

Caesarean section and maternal shoe size

Description

The table caesar.shoe contains the relation between caesarean section and maternal shoe size (UK sizes!).

Usage

caesar.shoe

Format

A matrix with two rows and six columns.

Source

D.G. Altman (1991), Practical Statistics for Medical Research, Table 10.1, Chapman & Hall.

Examples

prop.trend.test(caesar.shoe["Yes",],margin.table(caesar.shoe,2))

Coking data

Description

The coking data frame has 18 rows and 3 columns. It contains the time to coking in an experiment where the oven width and temperature were varied.

Usage

coking

Format

This data frame contains the following columns:

width

a factor with levels 4, 8, and 12, giving the oven width in inches.

temp

a factor with levels 1600 and 1900, giving the temperature in Fahrenheit.

time

a numeric vector, time to coking.

Source

R.A. Johnson (1994), Miller and Freund's Probability and Statistics for Engineers, 5th ed., Prentice-Hall.

Examples

attach(coking)
matplot(tapply(time,list(width,temp),mean))
detach(coking)

Cystic fibrosis lung function data

Description

The cystfibr data frame has 25 rows and 10 columns. It contains lung function data for cystic fibrosis patients (7–23 years old).

Usage

cystfibr

Format

This data frame contains the following columns:

age

a numeric vector, age in years.

sex

a numeric vector code, 0: male, 1:female.

height

a numeric vector, height (cm).

weight

a numeric vector, weight (kg).

bmp

a numeric vector, body mass (% of normal).

fev1

a numeric vector, forced expiratory volume.

rv

a numeric vector, residual volume.

frc

a numeric vector, functional residual capacity.

tlc

a numeric vector, total lung capacity.

pemax

a numeric vector, maximum expiratory pressure.

Source

D.G. Altman (1991), Practical Statistics for Medical Research, Table 12.11, Chapman & Hall.

References

O'Neill et al. (1983), The effects of chronic hyperinflation, nutritional status, and posture on respiratory muscle strength in cystic fibrosis, Am. Rev. Respir. Dis., 128:1051–1054.

Lung cancer incidence in four Danish cities 1968–1971

Description

This data set contains counts of incident lung cancer cases and population size in four neighbouring Danish cities by age group.

Usage

eba1977

Format

A data frame with 24 observations on the following 4 variables:

city

a factor with levels Fredericia, Horsens, Kolding, and Vejle.

age

a factor with levels 40-54, 55-59, 60-64, 65-69, 70-74, and 75+.

pop

a numeric vector, number of inhabitants.

cases

a numeric vector, number of lung cancer cases.

Details

These data were “at the center of public interest in Denmark in 1974”, according to Erling Andersen's paper. The city of Fredericia has a substantial petrochemical industry in the harbour area.

Source

E.B. Andersen (1977), Multiplicative Poisson models with unequal cell rates, Scandinavian Journal of Statistics, 4:153–158.

References

J. Clemmensen et al. (1974), Ugeskrift for Læger, pp. 2260–2268.

Energy expenditure

Description

The energy data frame has 22 rows and 2 columns. It contains data on the energy expenditure in groups of lean and obese women.

Usage

energy

Format

This data frame contains the following columns:

expend

a numeric vector, 24 hour energy expenditure (MJ).

stature

a factor with levels lean and obese.

Source

D.G. Altman (1991), Practical Statistics for Medical Research, Table 9.4, Chapman & Hall.

Examples

plot(expend~stature,data=energy)

Rates of lung and nasal cancer mortality, and total mortality.

Description

England and Wales mortality rates from lung cancer, nasal cancer, and all causes, 1936–1980. The 1936 rates are repeated as 1931 rates in order to accommodate follow-up for the nickel study.

Usage

ewrates

Format

A data frame with 150 observations on the following 5 variables:

year

calendar period, 1931: 1931–35, 1936: 1936–40, ....

age

age class, 10: 10–14, 15:15–19, ....

lung

lung cancer mortality rate per 1 million person-years

nasal

nasal cancer mortality rate per 1 million person-years

other

all cause mortality rate per 1 million person-years

Details

Taken from the “Epi” package by Bendix Carstensen et al.

Source

N.E. Breslow, and N. Day (1987). Statistical Methods in Cancer Research. Volume II: The Design and Analysis of Cohort Studies, Appendix IX. IARC Scientific Publications, Lyon.

Trypsin by age groups

Description

The trypsin data frame has 271 rows and 3 columns. Serum levels of immunoreactive trypsin in healthy volunteers (faked!).

Usage

fake.trypsin

Format

This data frame contains the following columns:

trypsin

a numeric vector, serum-trypsin in ng/ml.

grp

a numeric vector, age coding. See below.

grpf

a factor with levels 1: age 10–19, 2: age 20–29, 3: age 30–39, 4: age 40–49, 5: age 50–59, and 6: age 60–69.

Details

Data have been simulated to match given group means and SD.

Source

D.G. Altman (1991), Practical Statistics for Medical Research, Table 9.12, Chapman & Hall.

Examples

plot(trypsin~grp, data=fake.trypsin)

Graft versus host disease

Description

The gvhd data frame has 37 rows and 7 columns. It contains data from patients receiving a nondepleted allogenic bone marrow transplant with the purpose of finding variables associated with the development of acute graft-versus-host disease.

Usage

graft.vs.host

Format

This data frame contains the following columns:

pnr

a numeric vector patient number.

rcpage

a numeric vector, age of recipient (years).

donage

a numeric vector, age of donor (years).

type

a numeric vector, type of leukaemia coded 1: AML, 2: ALL, 3: CML for acute myeloid, acute lymphatic, and chronic myeloid leukaemia.

preg

a numeric vector code indicating whether donor has been pregnant. 0: no, 1: yes.

index

a numeric vector giving an index of mixed epidermal cell-lymphocyte reactions.

gvhd

a numeric vector code, graft-versus-host disease, 0: no, 1: yes.

time

a numeric vector, follow-up time

dead

a numeric vector code, 0: no (censored), 1: yes

Source

D.G. Altman (1991), Practical Statistics for Medical Research, Exercise 12.3, Chapman & Hall.

Examples

plot(jitter(gvhd,0.2)~index,data=graft.vs.host)

Heart rates after enalaprilat

Description

The heart.rate data frame has 36 rows and 3 columns. It contains data for nine patients with congestive heart failure before and shortly after administration of enalaprilat, in a balanced two-way layout.

Usage

heart.rate

Format

This data frame contains the following columns:

hr

a numeric vector, heart rate in beats per minute.

subj

a factor with levels 1 to 9.

time

a factor with levels 0 (before), 30, 60, and 120 (minutes after administration).

Source

D.G. Altman (1991), Practical Statistics for Medical Research, Table 12.2, Chapman & Hall.

Examples

evalq(interaction.plot(time,subj,hr), heart.rate)

Growth of Tetrahymena cells

Description

The hellung data frame has 51 rows and 3 columns. diameter and concentration of Tetrahymena cells with and without glucose added to growth medium.

Usage

hellung

Format

This data frame contains the following columns:

glucose

a numeric vector code, 1: yes, 2: no.

conc

a numeric vector, cell concentration (counts/ml).

diameter

a numeric vector, cell diameter (\mu \mathrm{m}).

Source

D. Kronborg and L.T. Skovgaard (1990), Regressionsanalyse, Table 1.1, FADLs Forlag (in Danish).

Examples

plot(diameter~conc,pch=glucose,log="xy",data=hellung)

Energy intake

Description

The intake data frame has 11 rows and 2 columns. It contains paired values of energy intake for 11 women.

Usage

intake

Format

This data frame contains the following columns:

pre

a numeric vector, premenstrual intake (kJ).

post

a numeric vector, postmenstrual intake (kJ).

Source

D.G. Altman (1991), Practical Statistics for Medical Research, Table 9.3, Chapman & Hall.

Examples

plot(intake$pre, intake$post)

Juul's IGF data

Description

The juul data frame has 1339 rows and 6 columns. It contains a reference sample of the distribution of insulin-like growth factor (IGF-I), one observation per subject in various ages, with the bulk of the data collected in connection with school physical examinations.

Usage

juul

Format

This data frame contains the following columns:

age

a numeric vector (years).

menarche

a numeric vector. Has menarche occurred (code 1: no, 2: yes)?

sex

a numeric vector (1: boy, 2: girl).

igf1

a numeric vector, insulin-like growth factor (\mu\mathrm{g}/\mathrm{l}).

tanner

a numeric vector, codes 1–5: Stages of puberty ad modum Tanner.

testvol

a numeric vector, testicular volume (ml).

Source

Original data.

Examples

plot(igf1~age, data=juul)

Juul's IGF data, extended version

Description

The juul2 data frame has 1339 rows and 8 columns; extended version of |juul|.

Usage

juul2

Format

This data frame contains the following columns:

age

a numeric vector (years).

height

a numeric vector (cm).

menarche

a numeric vector. Has menarche occurred (code 1: no, 2: yes)?

sex

a numeric vector (1: boy, 2: girl).

igf1

a numeric vector, insulin-like growth factor (\mu\mathrm{g}/\mathrm{l}).

tanner

a numeric vector, codes 1–5: Stages of puberty ad modum Tanner.

testvol

a numeric vector, testicular volume (ml).

weight

a numeric vector, weight (kg).

Source

Original data.

Examples

plot(igf1~age, data=juul2)

Breast-feeding data

Description

The kfm data frame has 50 rows and 7 columns. It was collected by Kim Fleischer Michaelsen and contains data for 50 infants of age approximately 2 months. They were weighed immediately before and after each breast feeding. and the measured intake of breast milk was registered along with various other data.

Usage

kfm

Format

This data frame contains the following columns:

no

a numeric vector, identification number.

dl.milk

a numeric vector, breast-milk intake (dl/24h).

sex

a factor with levels boy and girl.

weight

a numeric vector, weight of child (kg).

ml.suppl

a numeric vector, supplementary milk substitute (ml/24h).

mat.weight

a numeric vector, weight of mother (kg).

mat.height

a numeric vector, height of mother (cm).

Note

The amount of supplementary milk substitute refers to a period before the data collection.

Source

Original data.

Examples

plot(dl.milk~mat.height,pch=c(1,2)[sex],data=kfm)

Methods for determining lung volume

Description

The lung data frame has 18 rows and 3 columns. It contains data on three different methods of determining human lung volume.

Usage

lung

Format

This data frame contains the following columns:

volume

a numeric vector, measured lung volume.

method

a factor with levels A, B, and C.

subject

a factor with levels 1–6.

Source

Anon. (1977), Exercises in Applied Statistics, Exercise 4.15, Dept.\ of Theoretical Statistics, Aarhus University.

Malaria antibody data

Description

The malaria data frame has 100 rows and 4 columns.

Usage

malaria

Format

This data frame contains the following columns:

subject

subject code.

age

age in years.

ab

antibody level.

mal

a numeric vector code, Malaria: 0: no, 1: yes.

Details

A random sample of 100 children aged 3–15 years from a village in Ghana. The children were followed for a period of 8 months. At the beginning of the study, values of a particular antibody were assessed. Based on observations during the study period, the children were categorized into two groups: individuals with and without symptoms of malaria.

Source

Unpublished data.

Examples

summary(malaria)

Survival after malignant melanoma

Description

The melanom data frame has 205 rows and 7 columns. It contains data relating to the survival of patients after an operation for malignant melanoma, collected at Odense University Hospital by K.T. Drzewiecki.

Usage

melanom

Format

This data frame contains the following columns:

no

a numeric vector, patient code.

status

a numeric vector code, survival status; 1: dead from melanoma, 2: alive, 3: dead from other cause.

days

a numeric vector, observation time.

ulc

a numeric vector code, ulceration; 1: present, 2: absent.

thick

a numeric vector, tumor thickness (1/100 mm).

sex

a numeric vector code; 1: female, 2: male.

Source

P.K. Andersen, Ø. Borgan, R.D. Gill, and N. Keiding (1991), Statistical Models Based on Counting Processes, Appendix 1, Springer-Verlag.

Examples

require(survival)
plot(survfit(Surv(days,status==1)~1,data=melanom))

Nickel smelters in South Wales

Description

The data concern a cohort of nickel smelting workers in South Wales, with information on exposure, follow-up period, and cause of death.

Usage

nickel

Format

A data frame containing 679 observations of the following 7 variables:

id

subject identifier (numeric).

icd

ICD cause of death if dead, 0 otherwise (numeric).

exposure

exposure index for workplace (numeric)

dob

date of birth (numeric).

age1st

age at first exposure (numeric).

agein

age at start of follow-up (numeric).

ageout

age at end of follow-up (numeric).

Details

Taken from the “Epi” package by Bendix Carstensen et al. For comparison purposes, England and Wales mortality rates (per 1,000,000 per annum) from lung cancer (ICDs 162 and 163), nasal cancer (ICD 160), and all causes, by age group and calendar period, are supplied in the data set ewrates.

Source

N.E. Breslow and N. Day (1987). Statistical Methods in Cancer Research. Volume II: The Design and Analysis of Cohort Studies, IARC Scientific Publications, Lyon.

Nickel smelters in South Wales, expanded

Description

The data concern a cohort of nickel smelting workers in South Wales, with information on exposure, follow-up period, and cause of death, as in the nickel data. This version has follow-up times split according to age groups and is merged with the mortality rates in ewrates.

Usage

nickel.expand

Format

A data frame with 3724 observations on the following 12 variables:

agr

age class: 10: 10–14, 15: 15–19, ....

ygr

calendar period, 1931: 1931–35, 1936: 1936–40, ... .

id

subject identifier (numeric).

icd

ICD cause of death if dead, 0 otherwise (numeric).

exposure

exposure index for workplace (numeric).

dob

date of birth (numeric).

age1st

age at first exposure (numeric).

agein

age at start of follow-up (numeric).

ageout

age at end of follow-up (numeric).

lung

lung cancer mortality rate per 1 million person-years.

nasal

nasal cancer mortality rate per 1 million person-years.

other

all cause mortality rate per 1 million person-years.

Source

Computed from nickel and ewrates data sets.

Dose response data

Description

Four small experiments with the purpose of estimating the EC50 of a biological dose-response relation.

Usage

philion

Format

A data frame with 30 observations on the following 3 variables:

experiment

a numeric vector; codes 1 through 4 denote the experiment number.

dose

a numeric vector, the dose.

response

a numeric vector, the response (counts).

Details

These data were discussed on the R mailing lists, initially suggesting a log-linear Poisson regression, but actually a relation like y=y_{\rm max}/(1+(x/\beta)^\alpha) is more suitable.

Source

Original data from Vincent Philion, IRDA, Qu\'ebec.

References

https://stat.ethz.ch/pipermail/r-help/2003-July/036828.html (Thread on R-help mailing list: "inverse prediction and Poisson regression", started by Vincent Philion on July 25, 2003.)

Tuberculin reactions

Description

The numeric vector react contains differences between two nurses' determinations of 334 tuberculin reaction sizes.

Usage

react

Format

A single vector, differences between reaction sizes in mm.

Source

Anon. (1977), Exercises in Applied Statistics, Exercise 2.9, Dept.\ of Theoretical Statistics, Aarhus University.

Examples

hist(react) # not good because of discretization effects...
plot(density(react))

Red cell folate data

Description

The folate data frame has 22 rows and 2 columns. It contains data on red cell folate levels in patients receiving three different methods of ventilation during anesthesia.

Usage

red.cell.folate

Format

This data frame contains the following columns:

folate

a numeric vector, folate concentration (\mu\mathrm{g}/\mathrm{l}).

ventilation

a factor with levels N2O+O2,24h: 50% nitrous oxide and 50% oxygen, continuously for 24 hours; N2O+O2,op: 50% nitrous oxide and 50% oxygen, only during operation; O2,24h: no nitrous oxide but 35%–50% oxygen for 24 hours.

Source

D.G. Altman (1991), Practical Statistics for Medical Research, Table 9.10, Chapman & Hall.

Examples

plot(folate~ventilation,data=red.cell.folate)

Resting metabolic rate

Description

The rmr data frame has 44 rows and 2 columns. It contains the resting metabolic rate and body weight data for 44 women.

Usage

rmr

Format

This data frame contains the following columns:

body.weight

a numeric vector, body weight (kg).

metabolic.rate

a numeric vector, metabolic rate (kcal/24hr).

Source

D.G. Altman (1991), Practical Statistics for Medical Research, Exercise 11.2, Chapman & Hall.

Examples

plot(metabolic.rate~body.weight,data=rmr)

Birth weight and ultrasonography

Description

The secher data frame has 107 rows and 4 columns. It contains ultrasonographic measurements of fetuses immediately before birth and their subsequent birth weight.

Usage

secher

Format

This data frame contains the following columns:

bwt

a numeric vector, birth weight (g).

bpd

a numeric vector, biparietal diameter (mm).

ad

a numeric vector, abdominal diameter (mm).

no

a numeric vector, observation number.

Source

D. Kronborg and L.T. Skovgaard (1990), Regressionsanalyse, Table 3.1, FADLs Forlag (in Danish).

Secher et al. (1987), European Journal of Obstetrics, Gynecology, and Reproductive Biology, 24: 1–11.

Examples

plot(bwt~ad, data=secher, log="xy")

Secretin-induced blood glucose changes

Description

The secretin data frame has 50 rows and 6 columns. It contains data from a glucose response experiment.

Usage

secretin

Format

This data frame contains the following columns:

gluc

a numeric vector, blood glucose level.

person

a factor with levels A–E.

time

a factor with levels 20, 30, 60, 90 (minutes since injection), and pre (before injection).

repl

a factor with levels a: 1st sample; b: 2nd sample.

time20plus

a factor with levels 20+: 20 minutes or longer since injection; pre: before injection.

time.comb

a factor with levels 20: 20 minutes since injection; 30+: 30 minutes or longer since injection; pre: before injection.

Details

Secretin is a hormone of the duodenal mucous membrane. An extract was administered to five patients with arterial hypertension. Primary registrations (double determination) of blood glucose were on graph paper and later quantified with the smallest of the two measurements recorded first.

Source

Anon. (1977), Exercises in Applied Statistics, Exercise 5.8, Dept.\ of Theoretical Statistics, Aarhus University.

Estonian stroke data

Description

All cases of stroke in Tartu, Estonia, during the period 1991–1993, with follow-up until January 1, 1996.

Usage

stroke

Format

A data frame with 829 observations on the following 10 variables.

sex

a factor with levels Female and Male.

died

a Date, date of death.

dstr

a Date, date of stroke.

age

a numeric vector, age at stroke.

dgn

a factor, diagnosis, with levels ICH (intracranial haemorrhage), ID (unidentified). INF (infarction, ischaemic), SAH (subarchnoid haemorrhage).

coma

a factor with levels No and Yes, indicating whether patient was in coma after the stroke.

diab

a factor with levels No and Yes, history of diabetes.

minf

a factor with levels No and Yes, history of myocardial infarction.

han

a factor with levels No and Yes, history of hypertension.

obsmonths

a numeric vector, observation times in months (set to 0.1 for patients dying on the same day as the stroke).

dead

a logical vector, whether patient died during the study.

Source

Original data.

References

J. Korv, M. Roose, and A.E. Kaasik (1997). Stroke Registry of Tartu, Estonia, from 1991 through 1993. Cerebrovascular Disorders 7:154–162.

Tuberculin dilution assay

Description

The tb.dilute data frame has 18 rows and 3 columns. It contains data from a drug test involving dilutions of tuberculin.

Usage

tb.dilute

Format

This data frame contains the following columns:

reaction

a numeric vector, reaction sizes (average of diameters) for tuberculin skin pricks.

animal

a factor with levels 1–6.

logdose

a factor with levels 0.5, 0, and -0.5.

Details

The actual dilutions were 1:100, 1:100\sqrt{10}, 1:1000. Setting the middle one to 1 and using base-10 logarithms gives the logdose values.

Source

Anon. (1977), Exercises in Applied Statistics, part of Exercise 4.15, Dept.\ of Theoretical Statistics, Aarhus University.

Ventricular shortening velocity

Description

The thuesen data frame has 24 rows and 2 columns. It contains ventricular shortening velocity and blood glucose for type 1 diabetic patients.

Usage

thuesen

Format

This data frame contains the following columns:

blood.glucose

a numeric vector, fasting blood glucose (mmol/l).

short.velocity

a numeric vector, mean circumferential shortening velocity (%/s).

Source

D.G. Altman (1991), Practical Statistics for Medical Research, Table 11.6, Chapman & Hall.

Examples

plot(short.velocity~blood.glucose, data=thuesen)

Total lung capacity

Description

The tlc data frame has 32 rows and 4 columns. It contains data on pretransplant total lung capacity (TLC) for recipients of heart-lung transplants by whole-body plethysmography.

Usage

tlc

Format

This data frame contains the following columns:

age

a numeric vector, age of recipient (years).

sex

a numeric vector code, female: 1, male: 2.

height

a numeric vector, height of recipient (cm).

tlc

a numeric vector, total lung capacity (l).

Source

D.G. Altman (1991), Practical Statistics for Medical Research, Exercise 12.5, 10.1, Chapman & Hall.

Examples

plot(tlc~height,data=tlc)

Vital capacity

Description

The vitcap data frame has 24 rows and 3 columns. It contains data on vital capacity for workers in the cadmium industry. It is a subset of the vitcap2 data set.

Usage

vitcap

Format

This data frame contains the following columns:

group

a numeric vector; group codes are 1: exposed > 10 years, 3: not exposed.

age

a numeric vector, age in years.

vital.capacity

a numeric vector, vital capacity (a measure of lung volume) in liters.

Source

P. Armitage and G. Berry (1987), Statistical Methods in Medical Research, 2nd ed., Blackwell, p.286.

Examples

plot(vital.capacity~age, pch=group, data=vitcap)

Vital capacity, full data set

Description

The vitcap2 data frame has 84 rows and 3 columns. Age and vital capacity for workers in the cadmium industry.

Usage

vitcap2

Format

This data frame contains the following columns:

group

a numeric vector; group codes are 1: exposed > 10 years, 2: exposed < 10 years, 3: not exposed.

age

a numeric vector, age in years.

vital.capacity

a numeric vector, vital capacity (a measure of lung volume) (l).

Source

P. Armitage and G. Berry (1987), Statistical Methods in Medical Research, 2nd ed., Blackwell, p.286.

Examples

plot(vital.capacity~age, pch=group, data=vitcap2)

Comparison of Wright peak-flow meters

Description

The wright data frame has 17 rows and 2 columns. It contains data on peak expiratory flow rate with two different flow meters on each of 17 subjects.

Usage

wright

Format

This data frame contains the following columns:

std.wright

a numeric vector, data from large flow meter (l/min).

mini.wright

a numeric vector, data from mini flow meter (l/min).

Source

J.M. Bland and D.G. Altman (1986), Statistical methods for assessing agreement between two methods of clinical measurement, Lancet, 1:307–310.

Examples

plot(wright)
abline(0,1)

Age at walking

Description

The zelazo object is a list with four components.

Usage

zelazo

Format

This is a list containing data on age at walking (in months) for four groups of infants:

active

test group receiving active training; these children had their walking and placing reflexes trained during four three-minute sessions that took place every day from their second to their eighth week of life.

passive

passive training group; these children received the same types of social and gross motor stimulation, but did not have their specific walking and placing reflexes trained.

none

no training; these children had no special training, but were tested along with the children who underwent active or passive training.

ctr.8w

eighth-week controls; these children had no training and were only tested at the age of 8 weeks.

Note

When asked to enter these data from a text source, many students will use one vector per group and will need to reformat data into a data frame for some uses. The rather unusual format of this data set mimics that situation.

Source

P.R. Zelazo, N.A. Zelazo, and S. Kolb (1972), “Walking” in the newborn, Science, 176: 314–315.