Package 'datasets'

Description

Base R datasets

Details

This package contains a variety of datasets. For a complete list, use library(help = "datasets").

Author(s)

R Core Team and contributors worldwide

Maintainer: R Core Team [email protected]

Description

Six tests were given to 112 individuals. The covariance matrix is given in this object.

Usage

ability.cov

Details

The tests are described as

general:

a non-verbal measure of general intelligence using Cattell's culture-fair test.

picture:

a picture-completion test

blocks:

block design

maze:

mazes

reading:

reading comprehension

vocab:

vocabulary

Bartholomew gives both covariance and correlation matrices, but these are inconsistent. Neither are in the original paper.

Source

Bartholomew, D. J. (1987). Latent Variable Analysis and Factor Analysis. Griffin.

Bartholomew, D. J. and Knott, M. (1990). Latent Variable Analysis and Factor Analysis. Second Edition, Arnold.

References

Smith, G. A. and Stanley G. (1983). Clocking $g$: relating intelligence and measures of timed performance. Intelligence, 7, 353–368. doi:10.1016/0160-2896(83)90010-7.

Examples

require(stats)
(ability.FA <- factanal(factors = 1, covmat = ability.cov))
update(ability.FA, factors = 2)
## The signs of factors and hence the signs of correlations are
## arbitrary with promax rotation.
update(ability.FA, factors = 2, rotation = "promax")

Description

The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

Usage

airmiles

Format

A time series of 24 observations; yearly, 1937–1960.

Source

F.A.A. Statistical Handbook of Aviation.

References

Brown, R. G. (1963) Smoothing, Forecasting and Prediction of Discrete Time Series. Prentice-Hall.

Examples

require(graphics)
plot(airmiles, main = "airmiles data",
     xlab = "Passenger-miles flown by U.S. commercial airlines", col = 4)

Description

The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

Usage

AirPassengers

Format

A monthly time series, in thousands.

Source

Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976) Time Series Analysis, Forecasting and Control. Third Edition. Holden-Day. Series G.

Examples

## The classic 'airline model', by full ML
(fit <- arima(log10(AirPassengers), c(0, 1, 1),
              seasonal = list(order = c(0, 1, 1), period = 12)))
update(fit, method = "CSS")
update(fit, x = window(log10(AirPassengers), start = 1954))
pred <- predict(fit, n.ahead = 24)
tl <- pred$pred - 1.96 * pred$se
tu <- pred$pred + 1.96 * pred$se
ts.plot(AirPassengers, 10^tl, 10^tu, log = "y", lty = c(1, 2, 2))

## full ML fit is the same if the series is reversed, CSS fit is not
ap0 <- rev(log10(AirPassengers))
attributes(ap0) <- attributes(AirPassengers)
arima(ap0, c(0, 1, 1), seasonal = list(order = c(0, 1, 1), period = 12))
arima(ap0, c(0, 1, 1), seasonal = list(order = c(0, 1, 1), period = 12),
      method = "CSS")

## Structural Time Series
ap <- log10(AirPassengers) - 2
(fit <- StructTS(ap, type = "BSM"))
par(mfrow = c(1, 2))
plot(cbind(ap, fitted(fit)), plot.type = "single")
plot(cbind(ap, tsSmooth(fit)), plot.type = "single")

Description

Daily air quality measurements in New York, May to September 1973.

Usage

airquality

Format

A data frame with 153 observations on 6 variables.

Details

Daily readings of the following air quality values for May 1, 1973 (a Tuesday) to September 30, 1973.

• Ozone: Mean ozone in parts per billion from 1300 to 1500 hours at Roosevelt Island

• Solar.R: Solar radiation in Langleys in the frequency band 4000–7700 Angstroms from 0800 to 1200 hours at Central Park

• Wind: Average wind speed in miles per hour at 0700 and 1000 hours at LaGuardia Airport

• Temp: Maximum daily temperature in degrees Fahrenheit at LaGuardia Airport.

Source

The data were obtained from the New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

References

Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983) Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

Examples

require(graphics)
pairs(airquality, panel = panel.smooth, main = "airquality data")

Description

Four $x$-$y$ datasets which have the same traditional statistical properties (mean, variance, correlation, regression line, etc.), yet are quite different.

Usage

anscombe

Format

A data frame with 11 observations on 8 variables.

Source

Tufte, Edward R. (1989). The Visual Display of Quantitative Information, 13–14. Graphics Press.

References

Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21. doi:10.2307/2682899.

Examples

require(stats); require(graphics)
summary(anscombe)

##-- now some "magic" to do the 4 regressions in a loop:
ff <- y ~ x
mods <- setNames(as.list(1:4), paste0("lm", 1:4))
for(i in 1:4) {
  ff[2:3] <- lapply(paste0(c("y","x"), i), as.name)
  ## or   ff[[2]] <- as.name(paste0("y", i))
  ##      ff[[3]] <- as.name(paste0("x", i))
  mods[[i]] <- lmi <- lm(ff, data = anscombe)
  print(anova(lmi))
}

## See how close they are (numerically!)
sapply(mods, coef)
lapply(mods, function(fm) coef(summary(fm)))

## Now, do what you should have done in the first place: PLOTS
op <- par(mfrow = c(2, 2), mar = 0.1+c(4,4,1,1), oma =  c(0, 0, 2, 0))
for(i in 1:4) {
  ff[2:3] <- lapply(paste0(c("y","x"), i), as.name)
  plot(ff, data = anscombe, col = "red", pch = 21, bg = "orange", cex = 1.2,
       xlim = c(3, 19), ylim = c(3, 13))
  abline(mods[[i]], col = "blue")
}
mtext("Anscombe's 4 Regression data sets", outer = TRUE, cex = 1.5)
par(op)

Description

This data gives peak accelerations measured at various observation stations for 23 earthquakes in California. The data have been used by various workers to estimate the attenuating affect of distance on ground acceleration.

Usage

attenu

Format

A data frame with 182 observations on 5 variables.

Source

Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, Ca.

References

Boore, D. M. and Joyner, W. B.(1982). The empirical prediction of ground motion, Bulletin of the Seismological Society of America, 72, S269–S268.

Bolt, B. A. and Abrahamson, N. A. (1982). New attenuation relations for peak and expected accelerations of strong ground motion. Bulletin of the Seismological Society of America, 72, 2307–2321.

Bolt B. A. and Abrahamson, N. A. (1983). Reply to W. B. Joyner & D. M. Boore's “Comments on: New attenuation relations for peak and expected accelerations for peak and expected accelerations of strong ground motion”, Bulletin of the Seismological Society of America, 73, 1481–1483.

Brillinger, D. R. and Preisler, H. K. (1984). An exploratory analysis of the Joyner-Boore attenuation data, Bulletin of the Seismological Society of America, 74, 1441–1449.

Brillinger, D. R. and Preisler, H. K. (1984). Further analysis of the Joyner-Boore attenuation data. Manuscript.

Examples

require(graphics)
## check the data class of the variables
sapply(attenu, data.class)
summary(attenu)
pairs(attenu, main = "attenu data")
coplot(accel ~ dist | as.factor(event), data = attenu, show.given = FALSE)
coplot(log(accel) ~ log(dist) | as.factor(event),
       data = attenu, panel = panel.smooth, show.given = FALSE)

Description

From a survey of the clerical employees of a large financial organization, the data are aggregated from the questionnaires of the approximately 35 employees for each of 30 (randomly selected) departments. The numbers give the percent proportion of favourable responses to seven questions in each department.

Usage

attitude

Format

A data frame with 30 observations on 7 variables. The first column are the short names from the reference, the second one the variable names in the data frame:

Source

Chatterjee, S. and Price, B. (1977) Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

Examples

require(stats); require(graphics)
pairs(attitude, main = "attitude data")
summary(attitude)
summary(fm1 <- lm(rating ~ ., data = attitude))
opar <- par(mfrow = c(2, 2), oma = c(0, 0, 1.1, 0),
            mar = c(4.1, 4.1, 2.1, 1.1))
plot(fm1)
summary(fm2 <- lm(rating ~ complaints, data = attitude))
plot(fm2)
par(opar)

Description

Numbers (in thousands) of Australian residents measured quarterly from March 1971 to March 1994. The object is of class "ts".

Usage

austres

Source

P. J. Brockwell and R. A. Davis (1996) Introduction to Time Series and Forecasting. Springer

Description

Reynolds (1994) describes a small part of a study of the long-term temperature dynamics of beaver Castor canadensis in north-central Wisconsin. Body temperature was measured by telemetry every 10 minutes for four females, but data from a one period of less than a day for each of two animals is used there.

Usage

beaver1
beaver2

Format

The beaver1 data frame has 114 rows and 4 columns on body temperature measurements at 10 minute intervals.

The beaver2 data frame has 100 rows and 4 columns on body temperature measurements at 10 minute intervals.

The variables are as follows:

day

Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

time

Time of observation, in the form 0330 for 3:30am

temp

Measured body temperature in degrees Celsius.

activ

Indicator of activity outside the retreat.

Note

The observation at 22:20 is missing in beaver1.

Source

P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. eds (1994) Case Studies in Biometry. New York: John Wiley and Sons.

Examples

require(graphics)
(yl <- range(beaver1$temp, beaver2$temp))

beaver.plot <- function(bdat, ...) {
  nam <- deparse(substitute(bdat))
  with(bdat, {
    # Hours since start of day:
    hours <- time %/% 100 + 24*(day - day[1]) + (time %% 100)/60
    plot (hours, temp, type = "l", ...,
          main = paste(nam, "body temperature"))
    abline(h = 37.5, col = "gray", lty = 2)
    is.act <- activ == 1
    points(hours[is.act], temp[is.act], col = 2, cex = .8)
  })
}
op <- par(mfrow = c(2, 1), mar = c(3, 3, 4, 2), mgp = 0.9 * 2:0)
 beaver.plot(beaver1, ylim = yl)
 beaver.plot(beaver2, ylim = yl)
par(op)

Description

The sales time series BJsales and leading indicator BJsales.lead each contain 150 observations. The objects are of class "ts".

Usage

BJsales
BJsales.lead

Source

The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at https://robjhyndman.com/TSDL/

References

G. E. P. Box and G. M. Jenkins (1976): Time Series Analysis, Forecasting and Control, Holden-Day, San Francisco, p. 537.

P. J. Brockwell and R. A. Davis (1991): Time Series: Theory and Methods, Second edition, Springer Verlag, NY, pp. 414.

Description

The BOD data frame has 6 rows and 2 columns giving the biochemical oxygen demand versus time in an evaluation of water quality.

Usage

BOD

Format

This data frame contains the following columns:

Time

A numeric vector giving the time of the measurement (days).

demand

A numeric vector giving the biochemical oxygen demand (mg/l).

Source

Bates, D.M. and Watts, D.G. (1988), Nonlinear Regression Analysis and Its Applications, Wiley, Appendix A1.4.

Originally from Marske (1967), Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface M.Sc. Thesis, University of Wisconsin – Madison.

Examples

require(stats)
# simplest form of fitting a first-order model to these data
fm1 <- nls(demand ~ A*(1-exp(-exp(lrc)*Time)), data = BOD,
           start = c(A = 20, lrc = log(.35)))
coef(fm1)
fm1

# using the plinear algorithm  (trace o/p differs by platform)
fm2 <- nls(demand ~ (1-exp(-exp(lrc)*Time)), data = BOD,
           start = c(lrc = log(.35)), algorithm = "plinear", trace = TRUE)

# using a self-starting model
fm3 <- nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD)
summary(fm3)

Description

The data give the speed of cars and the distances taken to stop. Note that the data were recorded in the 1920s.

Usage

cars

Format

A data frame with 50 observations on 2 variables.

Source

Ezekiel, M. (1930) Methods of Correlation Analysis. Wiley.

References

McNeil, D. R. (1977) Interactive Data Analysis. Wiley.

Examples

require(stats); require(graphics)
plot(cars, xlab = "Speed (mph)", ylab = "Stopping distance (ft)",
     las = 1)
lines(lowess(cars$speed, cars$dist, f = 2/3, iter = 3), col = "red")
title(main = "cars data")
plot(cars, xlab = "Speed (mph)", ylab = "Stopping distance (ft)",
     las = 1, log = "xy")
title(main = "cars data (logarithmic scales)")
lines(lowess(cars$speed, cars$dist, f = 2/3, iter = 3), col = "red")
summary(fm1 <- lm(log(dist) ~ log(speed), data = cars))
opar <- par(mfrow = c(2, 2), oma = c(0, 0, 1.1, 0),
            mar = c(4.1, 4.1, 2.1, 1.1))
plot(fm1)
par(opar)

## An example of polynomial regression
plot(cars, xlab = "Speed (mph)", ylab = "Stopping distance (ft)",
    las = 1, xlim = c(0, 25))
d <- seq(0, 25, length.out = 200)
for(degree in 1:4) {
  fm <- lm(dist ~ poly(speed, degree), data = cars)
  assign(paste("cars", degree, sep = "."), fm)
  lines(d, predict(fm, data.frame(speed = d)), col = degree)
}
anova(cars.1, cars.2, cars.3, cars.4)

Description

The ChickWeight data frame has 578 rows and 4 columns from an experiment on the effect of diet on early growth of chicks.

Usage

ChickWeight

Format

An object of class c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame") containing the following columns:

weight

a numeric vector giving the body weight of the chick (gm).

Time

a numeric vector giving the number of days since birth when the measurement was made.

Chick

an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

Diet

a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

Details

The body weights of the chicks were measured at birth and every second day thereafter until day 20. They were also measured on day 21. There were four groups on chicks on different protein diets.

This dataset was originally part of package nlme, and that has methods (including for [, as.data.frame, plot and print) for its grouped-data classes.

Source

Crowder, M. and Hand, D. (1990), Analysis of Repeated Measures, Chapman and Hall (example 5.3)

Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis, Chapman and Hall (table A.2)

Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS, Springer.

See Also

SSlogis for models fitted to this dataset.

Examples

require(graphics)
coplot(weight ~ Time | Chick, data = ChickWeight,
       type = "b", show.given = FALSE)

Description

An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

Usage

chickwts

Format

A data frame with 71 observations on the following 2 variables.

weight

a numeric variable giving the chick weight.

feed

a factor giving the feed type.

Details

Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

Source

Anonymous (1948) Biometrika, 35, 214.

References

McNeil, D. R. (1977) Interactive Data Analysis. New York: Wiley.

Examples

require(stats); require(graphics)
boxplot(weight ~ feed, data = chickwts, col = "lightgray",
    varwidth = TRUE, notch = TRUE, main = "chickwt data",
    ylab = "Weight at six weeks (gm)")
anova(fm1 <- lm(weight ~ feed, data = chickwts))
opar <- par(mfrow = c(2, 2), oma = c(0, 0, 1.1, 0),
            mar = c(4.1, 4.1, 2.1, 1.1))
plot(fm1)
par(opar)

Description

Atmospheric concentrations of CO$_2$ are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale.

Usage

co2

Format

A time series of 468 observations; monthly from 1959 to 1997.

Details

The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

Source

Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

https://scrippsco2.ucsd.edu/data/atmospheric_co2/.

Note that the data are subject to revision (based on recalibration of standard gases) by the Scripps institute, and hence may not agree exactly with the data provided by R.

References

Cleveland, W. S. (1993) Visualizing Data. New Jersey: Summit Press.

Examples

require(graphics)
plot(co2, ylab = expression("Atmospheric concentration of CO"[2]),
     las = 1)
title(main = "co2 data set")

Description

The CO2 data frame has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

Usage

CO2

Format

An object of class c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame") containing the following columns:

Plant

an ordered factor with levels Qn1 < Qn2 < Qn3 < ... < Mc1 giving a unique identifier for each plant.

Type

a factor with levels Quebec Mississippi giving the origin of the plant

Treatment

a factor with levels nonchilled chilled

conc

a numeric vector of ambient carbon dioxide concentrations (mL/L).

uptake

a numeric vector of carbon dioxide uptake rates ($\mu\mbox{mol}/m^2$ sec).

Details

The $CO_2$ uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient $CO_2$ concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

This dataset was originally part of package nlme, and that has methods (including for [, as.data.frame, plot and print) for its grouped-data classes.

Source

Potvin, C., Lechowicz, M. J. and Tardif, S. (1990) “The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures”, Ecology, 71, 1389–1400.

Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS, Springer.

Examples

require(stats); require(graphics)

coplot(uptake ~ conc | Plant, data = CO2, show.given = FALSE, type = "b")
## fit the data for the first plant
fm1 <- nls(uptake ~ SSasymp(conc, Asym, lrc, c0),
   data = CO2, subset = Plant == "Qn1")
summary(fm1)
## fit each plant separately
fmlist <- list()
for (pp in levels(CO2$Plant)) {
  fmlist[[pp]] <- nls(uptake ~ SSasymp(conc, Asym, lrc, c0),
      data = CO2, subset = Plant == pp)
}
## check the coefficients by plant
print(sapply(fmlist, coef), digits = 3)

Description

Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

Usage

crimtab

Format

A table object of integer counts, of dimension $42 \times 22$ with a total count, sum(crimtab) of 3000.

The 42 rownames ("9.4", "9.5", ...) correspond to midpoints of intervals of finger lengths whereas the 22 column names (colnames) ("142.24", "144.78", ...) correspond to (body) heights of 3000 criminals, see also below.

Details

Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

“Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (1902, p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample determined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

The table is in fact page 216 and not page 219 in MacDonell (1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has $4' 7''9/16 -- 8''9/16$, we have 142.24 which is 2.54*56 = 2.54*($4' 8''$).

MacDonell credited the source of data (page 178) as follows: The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that : The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.

Source

https://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

References

Garson, J.G. (1900). The metric system of identification of criminals, as used in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198. doi:10.2307/2842627.

MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227. doi:10.2307/2331487.

Student (1908). The probable error of a mean. Biometrika, 6, 1–25. doi:10.2307/2331554.

Examples

require(stats)
dim(crimtab)
utils::str(crimtab)
## for nicer printing:
local({cT <- crimtab
       colnames(cT) <- substring(colnames(cT), 2, 3)
       print(cT, zero.print = " ")
})

## Repeat Student's experiment:

# 1) Reconstitute 3000 raw data for heights in inches and rounded to
#    nearest integer as in Student's paper:

(heIn <- round(as.numeric(colnames(crimtab)) / 2.54))
d.hei <- data.frame(height = rep(heIn, colSums(crimtab)))

# 2) shuffle the data:

set.seed(1)
d.hei <- d.hei[sample(1:3000), , drop = FALSE]

# 3) Make 750 samples each of size 4:

d.hei$sample <- as.factor(rep(1:750, each = 4))

# 4) Compute the means and standard deviations (n) for the 750 samples:

h.mean <- with(d.hei, tapply(height, sample, FUN = mean))
h.sd   <- with(d.hei, tapply(height, sample, FUN = sd)) * sqrt(3/4)

# 5) Compute the difference between the mean of each sample and
#    the mean of the population and then divide by the
#    standard deviation of the sample:

zobs <- (h.mean - mean(d.hei[,"height"]))/h.sd

# 6) Replace infinite values by +/- 6 as in Student's paper:

zobs[infZ <- is.infinite(zobs)] # none of them 
zobs[infZ] <- 6 * sign(zobs[infZ])

# 7) Plot the distribution:

require(grDevices); require(graphics)
hist(x = zobs, probability = TRUE, xlab = "Student's z",
     col = grey(0.8), border = grey(0.5),
     main = "Distribution of Student's z score  for 'crimtab' data")

Description

The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

Usage

discoveries

Format

A time series of 100 values.

Source

The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

References

McNeil, D. R. (1977) Interactive Data Analysis. Wiley.

Examples

require(graphics)
plot(discoveries, ylab = "Number of important discoveries",
     las = 1)
title(main = "discoveries data set")

Description

The DNase data frame has 176 rows and 3 columns of data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

Usage

DNase

Format

An object of class c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame") containing the following columns:

Run

an ordered factor with levels 10 < ... < 3 indicating the assay run.

conc

a numeric vector giving the known concentration of the protein.

density

a numeric vector giving the measured optical density (dimensionless) in the assay. Duplicate optical density measurements were obtained.

Details

This dataset was originally part of package nlme, and that has methods (including for [, as.data.frame, plot and print) for its grouped-data classes.

Source

Davidian, M. and Giltinan, D. M. (1995) Nonlinear Models for Repeated Measurement Data, Chapman & Hall (section 5.2.4, p. 134)

Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS, Springer.

Examples

require(stats); require(graphics)

coplot(density ~ conc | Run, data = DNase,
       show.given = FALSE, type = "b")
coplot(density ~ log(conc) | Run, data = DNase,
       show.given = FALSE, type = "b")
## fit a representative run
fm1 <- nls(density ~ SSlogis( log(conc), Asym, xmid, scal ),
    data = DNase, subset = Run == 1)
## compare with a four-parameter logistic
fm2 <- nls(density ~ SSfpl( log(conc), A, B, xmid, scal ),
    data = DNase, subset = Run == 1)
summary(fm2)
anova(fm1, fm2)

Description

Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

Usage

esoph

Format

A data frame with records for 88 age/alcohol/tobacco combinations.

Author(s)

Thomas Lumley

Source

Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. IARC Lyon / Oxford University Press.

Examples

require(stats)
require(graphics) # for mosaicplot
summary(esoph)
## effects of alcohol, tobacco and interaction, age-adjusted
model1 <- glm(cbind(ncases, ncontrols) ~ agegp + tobgp * alcgp,
              data = esoph, family = binomial())
anova(model1)
## Try a linear effect of alcohol and tobacco
model2 <- glm(cbind(ncases, ncontrols) ~ agegp + unclass(tobgp)
                                         + unclass(alcgp),
              data = esoph, family = binomial())
summary(model2)
## Re-arrange data for a mosaic plot
ttt <- table(esoph$agegp, esoph$alcgp, esoph$tobgp)
o <- with(esoph, order(tobgp, alcgp, agegp))
ttt[ttt == 1] <- esoph$ncases[o]
tt1 <- table(esoph$agegp, esoph$alcgp, esoph$tobgp)
tt1[tt1 == 1] <- esoph$ncontrols[o]
tt <- array(c(ttt, tt1), c(dim(ttt),2),
            c(dimnames(ttt), list(c("Cancer", "control"))))
mosaicplot(tt, main = "esoph data set", color = TRUE)

Description

Conversion rates between the various Euro currencies.

Usage

euro
euro.cross

Format

euro is a named vector of length 11, euro.cross a matrix of size 11 by 11, with dimnames.

Details

The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

The data set euro.cross contains conversion rates between the various Euro currencies, i.e., the result of outer(1 / euro, euro).

Examples

cbind(euro)

## These relations hold:
euro == signif(euro, 6) # [6 digit precision in Euro's definition]
all(euro.cross == outer(1/euro, euro))

## Convert 20 Euro to Belgian Franc
20 * euro["BEF"]
## Convert 20 Austrian Schilling to Euro
20 / euro["ATS"]
## Convert 20 Spanish Pesetas to Italian Lira
20 * euro.cross["ESP", "ITL"]

require(graphics)
dotchart(euro,
         main = "euro data: 1 Euro in currency unit")
dotchart(1/euro,
         main = "euro data: 1 currency unit in Euros")
dotchart(log(euro, 10),
         main = "euro data: log10(1 Euro in currency unit)")

Description

The eurodist gives the road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

UScitiesD gives “straight line” distances between 10 cities in the US.

Usage

eurodist
UScitiesD

Format

dist objects based on 21 and 10 objects, respectively. (You must have the stats package loaded to have the methods for this kind of object available).

Source

Crystal, D. Ed. (1990) The Cambridge Encyclopaedia. Cambridge: Cambridge University Press,

The US cities distances were provided by Pierre Legendre.

Description

Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

Usage

EuStockMarkets

Format

A multivariate time series with 1860 observations on 4 variables. The object is of class "mts".

Source

The data were kindly provided by Erste Bank AG, Vienna, Austria.

Description

Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

Usage

faithful

Format

A data frame with 272 observations on 2 variables.

Details

A closer look at faithful$eruptions reveals that these are heavily rounded times originally in seconds, where multiples of 5 are more frequent than expected under non-human measurement. For a better version of the eruption times, see the example below.

There are many versions of this dataset around: Azzalini and Bowman (1990) use a more complete version.

Source

W. Härdle.

References

Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365. doi:10.2307/2347385.

See Also

geyser in package MASS for the Azzalini–Bowman version.

Examples

require(stats); require(graphics)
f.tit <-  "faithful data: Eruptions of Old Faithful"

ne60 <- round(e60 <- 60 * faithful$eruptions)
all.equal(e60, ne60)             # relative diff. ~ 1/10000
table(zapsmall(abs(e60 - ne60))) # 0, 0.02 or 0.04
faithful$better.eruptions <- ne60 / 60
te <- table(ne60)
te[te >= 4]                      # (too) many multiples of 5 !
plot(names(te), te, type = "h", main = f.tit, xlab = "Eruption time (sec)")

plot(faithful[, -3], main = f.tit,
     xlab = "Eruption time (min)",
     ylab = "Waiting time to next eruption (min)")
lines(lowess(faithful$eruptions, faithful$waiting, f = 2/3, iter = 3),
      col = "red")

Description

These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromotropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

Usage

Formaldehyde

Format

A data frame with 6 observations on 2 variables.

Source

Bennett, N. A. and N. L. Franklin (1954) Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

References

McNeil, D. R. (1977) Interactive Data Analysis. New York: Wiley.

Examples

require(stats); require(graphics)
plot(optden ~ carb, data = Formaldehyde,
     xlab = "Carbohydrate (ml)", ylab = "Optical Density",
     main = "Formaldehyde data", col = 4, las = 1)
abline(fm1 <- lm(optden ~ carb, data = Formaldehyde))
summary(fm1)
opar <- par(mfrow = c(2, 2), oma = c(0, 0, 1.1, 0))
plot(fm1)
par(opar)

Description

Freeny's data on quarterly revenue and explanatory variables.

Usage

freeny
freeny.x
freeny.y

Format

There are three ‘freeny’ data sets.

freeny.y is a time series with 39 observations on quarterly revenue from (1962,2Q) to (1971,4Q).

freeny.x is a matrix of explanatory variables. The columns are freeny.y lagged 1 quarter, price index, income level, and market potential.

Finally, freeny is a data frame with variables y, lag.quarterly.revenue, price.index, income.level, and market.potential obtained from the above two data objects.

Source

A. E. Freeny (1977) A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Examples

require(stats); require(graphics)
summary(freeny)
pairs(freeny, main = "freeny data")
# gives warning: freeny$y has class "ts"

summary(fm1 <- lm(y ~ ., data = freeny))
opar <- par(mfrow = c(2, 2), oma = c(0, 0, 1.1, 0),
            mar = c(4.1, 4.1, 2.1, 1.1))
plot(fm1)
par(opar)

Description

Hip and knee angle (in degrees) through a 20 point movement cycle for 39 boys.

Usage

gait

Format

A 3-dimensional array with dimensions c(20, 39, 2) giving the "Hip Angle" and "Knee Angle" (in degrees) for 39 repetitions of a 20 point gait cycle (over standardized gait times).

Details

The named components of dimnames(gait) are as follows:

Time

seq(from = 0.025, to = 0.975, by = 0.05)

Subject

"boy1", "boy2", ..., "boy39"

Variable

"Hip Angle" and "Knee Angle"

Note

This is the version of the data as in the fda package and corresponding textbooks, but with named dimensions. One record appears to be duplicated from the original paper, Olshen et al. (1989), which analyses a sample of 38 boys. The gait dataset has 39 boys but boy19 and boy26 have identical measurements.

Source

In the FDA book (2006), p.8, “The Motion Analysis Laboratory at Children's Hospital, San Diego, collected these data”.

References

Olshen, R. A., Biden, E. N., Wyatt, M. P., and Sutherland, D. (1989) Gait Analysis and the Bootstrap. Annals of Statistics 17, 4, 1419–1440. doi:10.1214/AOS/1176347372

Ramsay, J. O., and Silverman, B. W. (2006) Functional Data Analysis, 2nd ed., New York: Springer.

Ramsay, J. (2023) fda: Functional Data Analysis. R package version 6.1.4, https://CRAN.R-project.org/package=fda.

Examples

plot(gait[, 1, ], type = "b",
     xlim = range(gait[,,1]), ylim = range(gait[,,2]), 
     xlab = "Hip Angle", ylab = "Knee Angle", main = "'gait' data : Boy 1")
mtext("all other boys", col = "thistle"); grid()
matlines(gait[, -1, 1], gait[, -1, 2], type = "l", lty = 1, col = adjustcolor("thistle", 1/3))

## The data array, two matrices :
op <- options(width = 128) # on a wide console
aperm(gait, c(2:1, 3))
options(op)

Description

Distribution of hair and eye color and sex in 592 statistics students.

Usage

HairEyeColor

Format

A 3-dimensional array resulting from cross-tabulating 592 observations on 3 variables. The variables and their levels are as follows:

Details

The Hair $\times$ Eye table comes from a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

Source

http://www.datavis.ca/sas/vcd/catdata/haireye.sas

Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

References

Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12. doi:10.2307/2683520.

Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://datavis.ca/papers/sugi/sugi17.pdf

Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.datavis.ca/papers/asa92.html

Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

See Also

chisq.test, loglin, mosaicplot

Examples

require(graphics)
## Full mosaic
mosaicplot(HairEyeColor)
## Aggregate over sex (as in Snee's original data)
x <- apply(HairEyeColor, c(1, 2), sum)
x
mosaicplot(x, main = "Relation between hair and eye color")

Description

A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

Usage

Harman23.cor

Source

Harman, H. H. (1976) Modern Factor Analysis, Third Edition Revised, University of Chicago Press, Table 2.3.

Examples

require(stats)
(Harman23.FA <- factanal(factors = 1, covmat = Harman23.cor))
for(factors in 2:4) print(update(Harman23.FA, factors = factors))

Description

A correlation matrix of 24 psychological tests given to 145 seventh and eight-grade children in a Chicago suburb by Holzinger and Swineford.

Usage

Harman74.cor

Source

Harman, H. H. (1976) Modern Factor Analysis, Third Edition Revised, University of Chicago Press, Table 7.4.

Examples

require(stats)
(Harman74.FA <- factanal(factors = 1, covmat = Harman74.cor))
for(factors in 2:5) print(update(Harman74.FA, factors = factors))
Harman74.FA <- factanal(factors = 5, covmat = Harman74.cor,
                        rotation = "promax")
print(Harman74.FA$loadings, sort = TRUE)

Description

The Indometh data frame has 66 rows and 3 columns of data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

Usage

Indometh

Format

An object of class c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame") containing the following columns:

Subject

an ordered factor with containing the subject codes. The ordering is according to increasing maximum response.

time

a numeric vector of times at which blood samples were drawn (hr).

conc

a numeric vector of plasma concentrations of indometacin (mcg/ml).

Details

Each of the six subjects were given an intravenous injection of indometacin.

This dataset was originally part of package nlme, and that has methods (including for [, as.data.frame, plot and print) for its grouped-data classes.

Source

Kwan, Breault, Umbenhauer, McMahon and Duggan (1976) Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

Davidian, M. and Giltinan, D. M. (1995) Nonlinear Models for Repeated Measurement Data, Chapman & Hall (section 5.2.4, p. 129)

Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS, Springer.

See Also

SSbiexp for models fitted to this dataset.

Description

This is a matched case-control study dating from before the availability of conditional logistic regression.

Usage

infert

Format

Note

One case with two prior spontaneous abortions and two prior induced abortions is omitted.

Source

Trichopoulos, D., Handanos, N., Danezis, J., Kalandidi, A., & Kalapothaki, V. (1976). Induced abortion and secondary infertility. British Journal of Obstetrics and Gynaecology, 83, 645–650. doi:10.1111/j.1471-0528.1976.tb00904.x.

Examples

require(stats)
model1 <- glm(case ~ spontaneous+induced, data = infert, family = binomial())
summary(model1)
## adjusted for other potential confounders:
summary(model2 <- glm(case ~ age+parity+education+spontaneous+induced,
                     data = infert, family = binomial()))
## Really should be analysed by conditional logistic regression
## which is in the survival package
if(require(survival)){
  model3 <- clogit(case ~ spontaneous+induced+strata(stratum), data = infert)
  print(summary(model3))
  detach()  # survival (conflicts)
}

Description

The counts of insects in agricultural experimental units treated with different insecticides.

Usage

InsectSprays

Format

A data frame with 72 observations on 2 variables.

Source

Beall, G., (1942) The Transformation of data from entomological field experiments, Biometrika, 29, 243–262.

References

McNeil, D. (1977) Interactive Data Analysis. New York: Wiley.

Examples

require(stats); require(graphics)
boxplot(count ~ spray, data = InsectSprays,
        xlab = "Type of spray", ylab = "Insect count",
        main = "InsectSprays data", varwidth = TRUE, col = "lightgray")
fm1 <- aov(count ~ spray, data = InsectSprays)
summary(fm1)
opar <- par(mfrow = c(2, 2), oma = c(0, 0, 1.1, 0))
plot(fm1)
fm2 <- aov(sqrt(count) ~ spray, data = InsectSprays)
summary(fm2)
plot(fm2)
par(opar)

Description

This famous (Fisher's or Anderson's) iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.

Usage

iris
iris3

Format

iris is a data frame with 150 cases (rows) and 5 variables (columns) named Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, and Species.

iris3 gives the same data arranged as a 3-dimensional array of size 50 by 4 by 3, as once provided by S-PLUS. The first dimension gives the case number within the species subsample, the second the measurements with names Sepal L., Sepal W., Petal L., and Petal W., and the third the species.

Source

Fisher, R. A. (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179–188. doi:10.1111/j.1469-1809.1936.tb02137.x.

The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula, Bulletin of the American Iris Society, 59, 2–5.

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole. (has iris3 as iris.)

See Also

matplot some examples of which use iris.

Examples

summary(iris)

## Fisher's (1936) research question: whether (compound measurements of)
## Iris versicolor "differs twice as much from I. setosa as from I. virginica"
pairs(iris[1:4], col = iris$Species)
legend(0.5, 1, levels(iris$Species), fill = 1:3, bty = "n",
       horiz = TRUE, xjust = 0.5, yjust = 0, xpd = TRUE)

## equivalence of legacy array (iris3) and data.frame (iris) representation
dni3 <- dimnames(iris3)
ii <- data.frame(matrix(aperm(iris3, c(1,3,2)), ncol = 4,
                        dimnames = list(NULL, sub(" L.",".Length",
                                        sub(" W.",".Width", dni3[[2]])))),
    Species = gl(3, 50, labels = sub("S", "s", sub("V", "v", dni3[[3]]))))
stopifnot(all.equal(ii, iris))

Description

The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

Usage

islands

Format

A named vector of length 48.

Source

The World Almanac and Book of Facts, 1975, page 406.

References

McNeil, D. R. (1977) Interactive Data Analysis. Wiley.

Examples

require(graphics)
dotchart(log(islands, 10),
   main = "islands data: log10(area) (log10(sq. miles))")
dotchart(log(islands[order(islands)], 10),
   main = "islands data: log10(area) (log10(sq. miles))")

Description

Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

Usage

JohnsonJohnson

Format

A quarterly time series

Source

Shumway, R. H. and Stoffer, D. S. (2000) Time Series Analysis and its Applications. Second Edition. Springer. Example 1.1.

Examples

require(stats); require(graphics)
JJ <- log10(JohnsonJohnson)
plot(JJ)
## This example gives a possible-non-convergence warning on some
## platforms, but does seem to converge on x86 Linux and Windows.
(fit <- StructTS(JJ, type = "BSM"))
tsdiag(fit)
sm <- tsSmooth(fit)
plot(cbind(JJ, sm[, 1], sm[, 3]-0.5), plot.type = "single",
     col = c("black", "green", "blue"))
abline(h = -0.5, col = "grey60")

monthplot(fit)

Description

Annual measurements of the level, in feet, of Lake Huron 1875–1972.

Usage

LakeHuron

Format

A time series of length 98.

Source

Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. Springer, New York. Series A, page 555.

Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. Springer, New York. Sections 5.1 and 7.6.

Description

A regular time series giving the luteinizing hormone in blood samples at 10 mins intervals from a human female, 48 samples.

Usage

lh

Source

P.J. Diggle (1990) Time Series: A Biostatistical Introduction. Oxford, table A.1, series 3

Description

Data on the savings ratio 1960–1970.

Usage

LifeCycleSavings

Format

A data frame with 50 observations on 5 variables.

Details

Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

Source

The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

References

Sterling, Arnie (1977) Unpublished BS Thesis. Massachusetts Institute of Technology.

Belsley, D. A., Kuh. E. and Welsch, R. E. (1980) Regression Diagnostics. New York: Wiley.

Examples

require(stats); require(graphics)
pairs(LifeCycleSavings, panel = panel.smooth,
      main = "LifeCycleSavings data")
fm1 <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings)
summary(fm1)

Description

The Loblolly data frame has 84 rows and 3 columns of records of the growth of Loblolly pine trees.

Usage

Loblolly

Format

An object of class c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame") containing the following columns:

height

a numeric vector of tree heights (ft).

age

a numeric vector of tree ages (yr).

Seed

an ordered factor indicating the seed source for the tree. The ordering is according to increasing maximum height.

Details

This dataset was originally part of package nlme, and that has methods (including for [, as.data.frame, plot and print) for its grouped-data classes.

Source

Kung, F. H. (1986), Fitting logistic growth curve with predetermined carrying capacity, in Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS, Springer.

Examples

require(stats); require(graphics)
plot(height ~ age, data = Loblolly, subset = Seed == 329,
     xlab = "Tree age (yr)", las = 1,
     ylab = "Tree height (ft)",
     main = "Loblolly data and fitted curve (Seed 329 only)")
fm1 <- nls(height ~ SSasymp(age, Asym, R0, lrc),
           data = Loblolly, subset = Seed == 329)
age <- seq(0, 30, length.out = 101)
lines(age, predict(fm1, list(age = age)))

Description

A macroeconomic data set which provides a well-known example for a highly collinear regression.

Usage

longley

Format

A data frame with 7 economical variables, observed yearly from 1947 to 1962 ($n=16$).

GNP.deflator

GNP implicit price deflator ($1954=100$)

GNP

Gross National Product.

Unemployed

number of unemployed.

Armed.Forces

number of people in the armed forces.

Population

‘noninstitutionalized’ population $\ge$ 14 years of age.

Year

the year (time).

Employed

number of people employed.

The regression lm(Employed ~ .) is known to be highly collinear.

Source

J. W. Longley (1967) An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association 62, 819–841.

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Examples

require(stats); require(graphics)
## give the data set in the form it was used in S-PLUS:
longley.x <- data.matrix(longley[, 1:6])
longley.y <- longley[, "Employed"]
pairs(longley, main = "longley data")
summary(fm1 <- lm(Employed ~ ., data = longley))
opar <- par(mfrow = c(2, 2), oma = c(0, 0, 1.1, 0),
            mar = c(4.1, 4.1, 2.1, 1.1))
plot(fm1)
par(opar)

Description

Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

Usage

lynx

Source

Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. Springer. Series G (page 557).

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Wadsworth & Brooks/Cole.

Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society Series A, 140, 411–431. doi:10.2307/2345277.

Description

A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

Usage

morley

Format

A data frame with 100 observations on the following 3 variables.

Expt

The experiment number, from 1 to 5.

Run

The run number within each experiment.

Speed

Speed-of-light measurement.

Details

The data is here viewed as a randomized block experiment with ‘experiment’ and ‘run’ as the factors. ‘run’ may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

Note

This is the same dataset as michelson in package MASS.

Source

A. J. Weekes (1986) A Genstat Primer. London: Edward Arnold.

S. M. Stigler (1977) Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

A. A. Michelson (1882) Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers 1 135–8. U.S. Nautical Almanac Office. (See Table 24.)

Examples

require(stats); require(graphics)
michelson <- transform(morley,
                       Expt = factor(Expt), Run = factor(Run))
xtabs(~ Expt + Run, data = michelson)  # 5 x 20 balanced (two-way)
plot(Speed ~ Expt, data = michelson,
     main = "Speed of Light Data", xlab = "Experiment No.")
fm <- aov(Speed ~ Run + Expt, data = michelson)
summary(fm)
fm0 <- update(fm, . ~ . - Run)
anova(fm0, fm)

Description

The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

Usage

mtcars

Format

A data frame with 32 observations on 11 (numeric) variables.

Note

Henderson and Velleman (1981) comment in a footnote to Table 1: ‘Hocking [original transcriber]'s noncrucial coding of the Mazda's rotary engine as a straight six-cylinder engine and the Porsche's flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.’

Source

Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391–411.

Examples

require(graphics)
pairs(mtcars, main = "mtcars data", gap = 1/4)
coplot(mpg ~ disp | as.factor(cyl), data = mtcars,
       panel = panel.smooth, rows = 1)
## possibly more meaningful, e.g., for summary() or bivariate plots:
mtcars2 <- within(mtcars, {
   vs <- factor(vs, labels = c("V", "S"))
   am <- factor(am, labels = c("automatic", "manual"))
   cyl  <- ordered(cyl)
   gear <- ordered(gear)
   carb <- ordered(carb)
})
summary(mtcars2)

Description

The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

Usage

nhtemp

Format

A time series of 60 observations.

Source

Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

References

McNeil, D. R. (1977) Interactive Data Analysis. New York: Wiley.

Examples

require(stats); require(graphics)
plot(nhtemp, main = "nhtemp data",
  ylab = "Mean annual temperature in New Haven, CT (deg. F)")

Description

Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in $10^8 m^3$, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

Usage

Nile

Format

A time series of length 100.

Source

Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford University Press.

References

Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92. doi:10.2307/1391308.

Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51. doi:10.2307/2335202.

Examples

require(stats); require(graphics)
par(mfrow = c(2, 2))
plot(Nile)
acf(Nile)
pacf(Nile)
ar(Nile) # selects order 2
cpgram(ar(Nile)$resid)
par(mfrow = c(1, 1))
arima(Nile, c(2, 0, 0))

## Now consider missing values, following Durbin & Koopman
NileNA <- Nile
NileNA[c(21:40, 61:80)] <- NA
arima(NileNA, c(2, 0, 0))
plot(NileNA)
pred <-
   predict(arima(window(NileNA, 1871, 1890), c(2, 0, 0)), n.ahead = 20)
lines(pred$pred, lty = 3, col = "red")
lines(pred$pred + 2*pred$se, lty = 2, col = "blue")
lines(pred$pred - 2*pred$se, lty = 2, col = "blue")
pred <-
   predict(arima(window(NileNA, 1871, 1930), c(2, 0, 0)), n.ahead = 20)
lines(pred$pred, lty = 3, col = "red")
lines(pred$pred + 2*pred$se, lty = 2, col = "blue")
lines(pred$pred - 2*pred$se, lty = 2, col = "blue")

## Structural time series models
par(mfrow = c(3, 1))
plot(Nile)
## local level model
(fit <- StructTS(Nile, type = "level"))
lines(fitted(fit), lty = 2)              # contemporaneous smoothing
lines(tsSmooth(fit), lty = 2, col = 4)   # fixed-interval smoothing
plot(residuals(fit)); abline(h = 0, lty = 3)
## local trend model
(fit2 <- StructTS(Nile, type = "trend")) ## constant trend fitted
pred <- predict(fit, n.ahead = 30)
## with 50% confidence interval
ts.plot(Nile, pred$pred,
        pred$pred + 0.67*pred$se, pred$pred -0.67*pred$se)

## Now consider missing values
plot(NileNA)
(fit3 <- StructTS(NileNA, type = "level"))
lines(fitted(fit3), lty = 2)
lines(tsSmooth(fit3), lty = 3)
plot(residuals(fit3)); abline(h = 0, lty = 3)

Description

A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

Usage

nottem

Source

Anderson, O. D. (1976) Time Series Analysis and Forecasting: The Box-Jenkins approach. Butterworths. Series R.

Examples

require(stats); require(graphics)
nott <- window(nottem, end = c(1936,12))
fit <- arima(nott, order = c(1,0,0), list(order = c(2,1,0), period = 12))
nott.fore <- predict(fit, n.ahead = 36)
ts.plot(nott, nott.fore$pred, nott.fore$pred+2*nott.fore$se,
        nott.fore$pred-2*nott.fore$se, gpars = list(col = c(1,1,4,4)))

Description

A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

Usage

npk

Format

The npk data frame has 24 rows and 5 columns:

block

which block (label 1 to 6).

N

indicator (0/1) for the application of nitrogen.

P

indicator (0/1) for the application of phosphate.

K

indicator (0/1) for the application of potassium.

yield

Yield of peas, in pounds/plot (the plots were (1/70) acre).

Source

Imperial College, London, M.Sc. exercise sheet.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Examples

options(contrasts = c("contr.sum", "contr.poly"))
npk.aov <- aov(yield ~ block + N*P*K, npk)
npk.aov
summary(npk.aov)
coef(npk.aov)
options(contrasts = c("contr.treatment", "contr.poly"))
npk.aov1 <- aov(yield ~ block + N + K, data = npk)
summary.lm(npk.aov1)
se.contrast(npk.aov1, list(N=="0", N=="1"), data = npk)
model.tables(npk.aov1, type = "means", se = TRUE)

Description

Cross-classification of a sample of British males according to each subject's occupational status and his father's occupational status.

Usage

occupationalStatus

Format

A table of counts, with classifying factors origin (father's occupational status; levels 1:8) and destination (son's occupational status; levels 1:8).

Source

Goodman, L. A. (1979) Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

The data set has been in package gnm and been provided by the package authors.

Examples

require(stats); require(graphics)

plot(occupationalStatus)

##  Fit a uniform association model separating diagonal effects
Diag <- as.factor(diag(1:8))
Rscore <- scale(as.numeric(row(occupationalStatus)), scale = FALSE)
Cscore <- scale(as.numeric(col(occupationalStatus)), scale = FALSE)
modUnif <- glm(Freq ~ origin + destination + Diag + Rscore:Cscore,
               family = poisson, data = occupationalStatus)

summary(modUnif)
plot(modUnif) # 4 plots, with warning about  h_ii ~= 1

Description

The Orange data frame has 35 rows and 3 columns of records of the growth of orange trees.

Usage

Orange

Format

An object of class c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame") containing the following columns:

Tree

an ordered factor indicating the tree on which the measurement is made. The ordering is according to increasing maximum diameter.

age

a numeric vector giving the age of the tree (days since 1968/12/31)

circumference

a numeric vector of trunk circumferences (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

Details

This dataset was originally part of package nlme, and that has methods (including for [, as.data.frame, plot and print) for its grouped-data classes.

Source

Draper, N. R. and Smith, H. (1998), Applied Regression Analysis (3rd ed), Wiley (exercise 24.N).

Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS, Springer.

Examples

require(stats); require(graphics)
coplot(circumference ~ age | Tree, data = Orange, show.given = FALSE)
fm1 <- nls(circumference ~ SSlogis(age, Asym, xmid, scal),
           data = Orange, subset = Tree == 3)
plot(circumference ~ age, data = Orange, subset = Tree == 3,
     xlab = "Tree age (days since 1968/12/31)",
     ylab = "Tree circumference (mm)", las = 1,
     main = "Orange tree data and fitted model (Tree 3 only)")
age <- seq(0, 1600, length.out = 101)
lines(age, predict(fm1, list(age = age)))

Description

An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

Usage

OrchardSprays

Format

A data frame with 64 observations on 4 variables.

Details

Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

An $8 \times 8$ Latin square design was used and the treatments were coded as follows:

Source

Finney, D. J. (1947) Probit Analysis. Cambridge.

References

McNeil, D. R. (1977) Interactive Data Analysis. New York: Wiley.

Examples

require(graphics)
pairs(OrchardSprays, main = "OrchardSprays data")

Description

Data on adult penguins covering three species found on three islands in the Palmer Archipelago, Antarctica, including their size (flipper length, body mass, bill dimensions), and sex.

The columns of penguins are a subset of the more extensive penguins_raw data frame, which includes nesting observations and blood isotope data. There are differences in the column names and data types. See the ‘Format’ section for details.

Usage

penguins
penguins_raw

Format

penguins is a data frame with 344 rows and 8 variables:

species

factor, with levels Adelie, Chinstrap, and Gentoo

island

factor, with levels Biscoe, Dream, and Torgersen)

bill_len

numeric, bill length (millimeters)

bill_dep

numeric, bill depth (millimeters)

flipper_len

integer, flipper length (millimeters)

body_mass

integer, body mass (grams)

sex

factor, with levels female and male

year

integer, study year: 2007, 2008, or 2009

penguins_raw is a data frame with 344 rows and 17 variables. 8 columns correspond to columns in penguins, though with different variable names and/or classes:

Species

character

Island

character

Culmen Length (mm)

numeric, bill length

Culmen Depth (mm)

numeric, bill depth

Flipper Length (mm)

numeric, flipper length

Body Mass (g)

numeric, body mass

Sex

character

Date Egg

Date, when study nest observed with 1 egg. The year component is the year column in penguins

There are 9 further columns in penguins_raw:

studyName

character, expedition during which the data was collected

Sample Number

numeric, continuous numbering sequence for each sample

Region

character, the region of Palmer LTER sampling grid

Stage

character, denoting reproductive stage at sampling

Individual ID

character, unique ID for each individual in dataset

Clutch Completion

character, if the study nest was observed with a full clutch, i.e., 2 eggs

Delta 15 N (o/oo)

numeric, the ratio of stable isotopes 15N:14N

Delta 13 C (o/oo)

numeric, the ratio of stable isotopes 13C:12C

Comments

character, additional relevant information

Details

Gorman et al. (2014) used the data to study sex dimorphism separately for the three species.

Horst et al. (2022) popularized the data as an illustration for different statistical methods, as an alternative to the iris data.

Kaye et al. (2025) provide the scripts used to create these data sets from the original source data, and a notebook reproducing results from Gorman et al. (2014).

Note

These data sets are also available in the palmerpenguins package. See the package website for further details and resources.

The penguins data has some shorter variable names than the palmerpenguins version, for compact code and data display.

Source

Adélie penguins:

Palmer Station Antarctica LTER and K. Gorman (2020). Structural size measurements and isotopic signatures of foraging among adult male and female Adélie penguins (Pygoscelis adeliae) nesting along the Palmer Archipelago near Palmer Station, 2007-2009 ver 5. Environmental Data Initiative, doi:10.6073/pasta/98b16d7d563f265cb52372c8ca99e60f.

Gentoo penguins:

Palmer Station Antarctica LTER and K. Gorman (2020). doi:10.6073/pasta/7fca67fb28d56ee2ffa3d9370ebda689.

Chinstrap penguins:

Palmer Station Antarctica LTER and K. Gorman (2020). doi:10.6073/pasta/c14dfcfada8ea13a17536e73eb6fbe9e.

The title naming convention for the source for the Gentoo and Chinstrap data is that same as for Adélie penguins.

References

Gorman, K. B., Williams, T. D. and Fraser, W. R. (2014) Ecological Sexual Dimorphism and Environmental Variability within a Community of Antarctic Penguins (Genus Pygoscelis). PLoS ONE 9, 3, e90081; doi:10.1371/journal.pone.0090081.

Horst, A. M., Hill, A. P. and Gorman, K. B. (2022) Palmer Archipelago Penguins Data in the palmerpenguins R Package - An Alternative to Anderson's Irises. R Journal 14, 1; doi:10.32614/RJ-2022-020.

Kaye, E., Turner, H., Gorman, K. B., Horst, A. M. and Hill, A. P. (2025) Preparing the Palmer Penguins Data for the datasets Package in R. doi:10.5281/zenodo.14902740.

Examples

## view summaries
summary(penguins)
summary(penguins_raw) # not useful for character vectors
## convert character vectors to factors first
dFactor <- function(dat) {
  dat[] <- lapply(dat, \(.) if (is.character(.)) as.factor(.) else .)
  dat
}
summary(dFactor(penguins_raw))

## visualise distribution across factors
plot(island ~ species, data = penguins)
plot(sex ~ interaction(island, species, sep = "\n"), data = penguins)

## bill depth vs. length by species (color) and sex (symbol):
## positive correlations for all species, males tend to have bigger bills
sym <- c(1, 16)
pal <- c("darkorange","purple","cyan4")
plot(bill_dep ~ bill_len, data = penguins, pch = sym[sex], col = pal[species])

## simplified sex dimorphism analysis for Adelie species:
## proportion of males increases with several size measurements
adelie <- subset(penguins, species == "Adelie")
plot(sex ~ bill_len, data = adelie)
plot(sex ~ bill_dep, data = adelie)
plot(sex ~ body_mass, data = adelie)
m <- glm(sex ~ bill_len + bill_dep + body_mass, data = adelie, family = binomial)
summary(m)

## Produce the long variable names as from {palmerpenguins} pkg:
long_nms <- sub("len", "length_mm",
                sub("dep","depth_mm",
                    sub("mass", "mass_g", colnames(penguins))))
## compare long and short names:
noquote(rbind(long_nms, nms = colnames(penguins)))

## Not run:  # << keeping shorter 'penguins' names in this example:
    colnames(penguins) <- long_nms

## End(Not run)

Description

Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

Usage

PlantGrowth

Format

A data frame of 30 cases on 2 variables.

The levels of group are ‘ctrl’, ‘trt1’, and ‘trt2’.

Source

Dobson, A. J. (1983) An Introduction to Statistical Modelling. London: Chapman and Hall.

Examples

## One factor ANOVA example from Dobson's book, cf. Table 7.4:
require(stats); require(graphics)
boxplot(weight ~ group, data = PlantGrowth, main = "PlantGrowth data",
        ylab = "Dried weight of plants", col = "lightgray",
        notch = TRUE, varwidth = TRUE)
anova(lm(weight ~ group, data = PlantGrowth))

Description

The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

Usage

precip

Format

A named vector of length 70.

Note

The dataset version up to Nov.16, 2016 had a typo in "Cincinnati"'s name. The examples show how to recreate that version.

Source

Statistical Abstracts of the United States, 1975.

References

McNeil, D. R. (1977) Interactive Data Analysis. New York: Wiley.

Examples

require(graphics)
dotchart(precip[order(precip)], main = "precip data")
title(sub = "Average annual precipitation (in.)")

## Old ("wrong") version of dataset (just name change):
precip.O <- local({
   p <- precip; names(p)[names(p) == "Cincinnati"] <- "Cincinati" ; p })
stopifnot(all(precip == precip.O),
	  match("Cincinnati", names(precip)) == 46,
	  identical(names(precip)[-46], names(precip.O)[-46]))

Description

The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

Usage

presidents

Format

A time series of 120 values.

Details

The data are actually a fudged version of the approval ratings. See McNeil's book for details.

Source

The Gallup Organisation.

References

McNeil, D. R. (1977) Interactive Data Analysis. New York: Wiley.

Examples

require(stats); require(graphics)
plot(presidents, las = 1, ylab = "Approval rating (%)",
     main = "presidents data")

Description

Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

Usage

pressure

Format

A data frame with 19 observations on 2 variables.

Source

Weast, R. C., ed. (1973) Handbook of Chemistry and Physics. CRC Press.

References

McNeil, D. R. (1977) Interactive Data Analysis. New York: Wiley.

Examples

require(graphics)
plot(pressure, xlab = "Temperature (deg C)",
     ylab = "Pressure (mm of Hg)",
     main = "pressure data: Vapor Pressure of Mercury")
plot(pressure, xlab = "Temperature (deg C)",  log = "y",
     ylab = "Pressure (mm of Hg)",
     main = "pressure data: Vapor Pressure of Mercury")

Description

The Puromycin data frame has 23 rows and 3 columns of the reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

Usage

Puromycin

Format

This data frame contains the following columns:

conc

a numeric vector of substrate concentrations (ppm)

rate

a numeric vector of instantaneous reaction rates (counts/min/min)

state

a factor with levels treated untreated

Details

Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

Source

Bates, D.M. and Watts, D.G. (1988), Nonlinear Regression Analysis and Its Applications, Wiley, Appendix A1.3.

Treloar, M. A. (1974), Effects of Puromycin on Galactosyltransferase in Golgi Membranes, M.Sc. Thesis, U. of Toronto.

See Also

SSmicmen for other models fitted to this dataset.

Examples

require(stats); require(graphics)

plot(rate ~ conc, data = Puromycin, las = 1,
     xlab = "Substrate concentration (ppm)",
     ylab = "Reaction velocity (counts/min/min)",
     pch = as.integer(Puromycin$state),
     col = as.integer(Puromycin$state),
     main = "Puromycin data and fitted Michaelis-Menten curves")
## simplest form of fitting the Michaelis-Menten model to these data
fm1 <- nls(rate ~ Vm * conc/(K + conc), data = Puromycin,
           subset = state == "treated",
           start = c(Vm = 200, K = 0.05))
fm2 <- nls(rate ~ Vm * conc/(K + conc), data = Puromycin,
           subset = state == "untreated",
           start = c(Vm = 160, K = 0.05))
summary(fm1)
summary(fm2)
## add fitted lines to the plot
conc <- seq(0, 1.2, length.out = 101)
lines(conc, predict(fm1, list(conc = conc)), lty = 1, col = 1)
lines(conc, predict(fm2, list(conc = conc)), lty = 2, col = 2)
legend(0.8, 120, levels(Puromycin$state),
       col = 1:2, lty = 1:2, pch = 1:2)

## using partial linearity
fm3 <- nls(rate ~ conc/(K + conc), data = Puromycin,
           subset = state == "treated", start = c(K = 0.05),
           algorithm = "plinear")

Description

The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

Usage

quakes

Format

A data frame with 1000 observations on 5 variables.

Details

There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

Source

This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

Examples

require(graphics)
pairs(quakes, main = "Fiji Earthquakes, N = 1000", cex.main = 1.2, pch = ".")

Description

400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

Usage

randu

Format

A data frame with 400 observations on 3 variables named x, y and z which give the first, second and third random number in the triple.

Details

In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

Under VMS versions 2.0 and higher, this problem has been fixed.

Source

David Donoho

Examples

## We could re-generate the dataset by the following R code
seed <- as.double(1)
RANDU <- function() {
    seed <<- ((2^16 + 3) * seed) %% (2^31)
    seed/(2^31)
}
myrandu <- matrix(NA_real_, 400, 3, dimnames = list(NULL, c("x","y","z")))
for(i in 1:400) {
    U <- c(RANDU(), RANDU(), RANDU(), RANDU(), RANDU())
    myrandu[i,] <- round(U[1:3], 6)
}
stopifnot(all.equal(randu, as.data.frame(myrandu), tolerance = 1e-5))

Description

This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

Usage

rivers

Format

A vector containing 141 observations.

Source

World Almanac and Book of Facts, 1975, page 406.

References

McNeil, D. R. (1977) Interactive Data Analysis. New York: Wiley.

Description

Measurements on 48 rock samples from a petroleum reservoir.

Usage

rock

Format

A data frame with 48 rows and 4 numeric columns.

Details

Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

Source

Data from BP Research, image analysis by Ronit Katz, U. Oxford.

Description

Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

Usage

sleep

Format

A data frame with 20 observations on 3 variables.

Details

The group variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

Source

Cushny, A. R. and Peebles, A. R. (1905) The action of optical isomers: II hyoscines. The Journal of Physiology 32, 501–510.

Student (1908) The probable error of the mean. Biometrika, 6, 20.

References

Scheffé, Henry (1959) The Analysis of Variance. New York, NY: Wiley.

Examples

require(stats)
## Student's paired t-test
with(sleep,
     t.test(extra[group == 1],
            extra[group == 2], paired = TRUE))

## The sleep *prolongations*
sleep1 <- with(sleep, extra[group == 2] - extra[group == 1])
summary(sleep1)
stripchart(sleep1, method = "stack", xlab = "hours",
           main = "Sleep prolongation (n = 10)")
boxplot(sleep1, horizontal = TRUE, add = TRUE,
        at = .6, pars = list(boxwex = 0.5, staplewex = 0.25))

Description

Operational data of a plant for the oxidation of ammonia to nitric acid.

Usage

stackloss

stack.x
stack.loss

Format

stackloss is a data frame with 21 observations on 4 variables.

For historical compatibility with S-PLUS, the data sets stack.x, a matrix with the first three (independent) variables of the data frame, and stack.loss, the numeric vector giving the fourth (dependent) variable, are also provided.

Details

“Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH$_3$) to nitric acid (HNO$_3$). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

Air Flow represents the rate of operation of the plant. Water Temp is the temperature of cooling water circulated through coils in the absorption tower. Acid Conc. is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. stack.loss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

Source

Brownlee, K. A. (1960, 2nd ed. 1965) Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Dodge, Y. (1996) The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber's 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

Examples

require(stats)
summary(lm.stack <- lm(stack.loss ~ stack.x))

Description

Data sets related to the 50 states of the United States of America.

Usage

state.abb
state.area
state.center
state.division
state.name
state.region
state.x77

Details

R currently contains the following “state” data sets. Note that all data are arranged according to alphabetical order of the state names.

state.abb:

character vector of 2-letter abbreviations for the state names.

state.area:

numeric vector of state areas (in square miles).

state.center:

list with components named x and y giving the approximate geographic center of each state in negative longitude and latitude. Alaska and Hawaii are placed just off the West Coast. See ‘Examples’ on how to “correct”.

state.division:

factor giving state divisions (New England, Middle Atlantic, South Atlantic, East South Central, West South Central, East North Central, West North Central, Mountain, and Pacific).

state.name:

character vector giving the full state names.

state.region:

factor giving the region (Northeast, South, North Central, West) that each state belongs to.

state.x77:

matrix with 50 rows and 8 columns giving the following statistics in the respective columns.

Population:

population estimate as of July 1, 1975

Income:

per capita income (1974)

Illiteracy:

illiteracy (1970, percent of population)

Life Exp:

life expectancy in years (1969–71)

Murder:

murder and non-negligent manslaughter rate per 100,000 population (1976)

HS Grad:

percent high-school graduates (1970)

Frost:

mean number of days with minimum temperature below freezing (1931–1960) in capital or large city

Area:

land area in square miles

Note that a square mile is by definition exactly (cm(1760 * 3 * 12) / 100 / 1000)^2 $km^2$, i.e., $2.589988110336 km^2$.

Source

U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Examples

(dst <- dxy <- data.frame(state.center, row.names=state.abb))
## Alaska and Hawaii are placed just off the West Coast (for compact map drawing):
dst[c("AK", "HI"),]
## state.center2 := version of state.center with "correct" coordinates for AK & HI:
## From https://pubs.usgs.gov/gip/Elevations-Distances/elvadist.html#Geographic%20Centers
##   Alaska   63°50' N., 152°00' W., 60 miles northwest of Mount McKinley
##   Hawaii   20°15' N., 156°20' W., off Maui Island
dxy["AK",] <- c(-152.  , 63.83) # or  c(-152.11, 65.17)
dxy["HI",] <- c(-156.33, 20.25) # or  c(-156.69, 20.89)
state.center2 <- as.list(dxy)

plot(dxy, asp=1.2, pch=3, col=2)
text(state.center2, state.abb, cex=1/2, pos=4, offset=1/4)
i <- c("AK","HI")
do.call(arrows, c(setNames(c(dst[i,], dxy[i,]), c("x0","y0", "x1","y1")),
                  col=adjustcolor(4, .7), length=1/8))
points(dst[i,], col=2)
if(FALSE) { # if(require("maps")) {
   map("state", interior = FALSE,          add = TRUE)
   map("state", boundary = FALSE, lty = 2, add = TRUE)
}

Description

Monthly numbers of sunspots, as from the World Data Center, aka SIDC. sunspot.month is the version of the data that will occasionally be updated when new counts become available (or the numbers are recalibrated). Note that around 2014–2015 there has been an international effort to recalibrate the sunspots numbers; since R 4.5.0 (2025), the recalibrated series is used.
The version in (too long) use from 2014 to 2024 is provided as sunspot.m2014 for historical and reproducibility reasons. For strict reproducibility, hence use that, or sunspots instead of sunspot.month.

Usage

sunspot.month
sunspot.m2014

Format

The univariate time series sunspot.year, sunspot.m2014 and sunspot.month contain 289, 3177, and currently 3310 observations, respectively, where the latter will increase over time. The objects are of class "ts".

Author(s)

R Core Team.

Source

WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS Currently at https://www.sidc.be/SILSO/datafiles

From around 2015, expert astronomers decided to recalibrate historical sunspot numbers, see the ‘References’. This is not yet visible in current sunspot.month but may well change by the next release of R.

References

Clette, Frédéric and Lefèvre, Laure (2016) The New Sunspot Number: Assembling All Corrections. Solar Physics 291, 2629–2651. doi:10.1007/s11207-016-1014-y

Clette, F., Lefèvre, L., Chatzistergos, T. et al. (2023) Recalibration of the Sunspot-Number: Status Report. Solar Physics 298: 44. doi:10.1007/s11207-023-02136-3

See Also

sunspot.month is a more up-to-date and hence also longer version of sunspots; the latter runs until 1983 and is kept fixed (for reproducibility as example dataset).

Examples

require(stats); require(graphics)
## Compare the monthly series
plot (sunspot.month,
      main="sunspot.month & sunspots [package 'datasets']", col=2)
lines(sunspots) # -> clear recalibration (to *larger* values)
at. <- seq(1750, 2030, by=10)
atyr <- at.[at. %% 50 != 0] #%
Axis(time(sunspot.month), at = atyr, side = 1,
     tck= -1/100, padj = -1.5, cex.axis = 3/4)

## Now look at the difference :
all(tsp(sunspots)     [c(1,3)] ==
    tsp(sunspot.month)[c(1,3)]) ## Start & Periodicity are the same
n1 <- length(sunspots)
table(eq <- sunspots == sunspot.m2014[1:n1]) #>  143  are different !
i <- which(!eq)
rug(time(eq)[i])
s1 <- sunspots[i] ; s2 <- sunspot.m2014[i]
cbind(i = i, time = time(sunspots)[i], sunspots = s1, ss.month = s2,
      perc.diff = round(100*2*abs(s1-s2)/(s1+s2), 1))

## How to recreate the "old" sunspot.month (R <= 3.0.3) =: sunspot.month.0
.sunspot.diff <- cbind(
    i = c(1202L, 1256L, 1258L, 1301L, 1407L, 1429L, 1452L, 1455L,
          1663L, 2151L, 2329L, 2498L, 2594L, 2694L, 2819L),
    res10 = c(1L, 1L, 1L, -1L, -1L, -1L, 1L, -1L,
          1L, 1L, 1L, 1L, 1L, 20L, 1L))
ssm0 <- sunspot.m2014[1:2988]
with(as.data.frame(.sunspot.diff), ssm0[i] <<- ssm0[i] - res10/10)
sunspot.month.0 <- ts(ssm0, start = 1749, frequency = 12)
stopifnot(length(sunspot.month.0) == 2988)

Description

Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

Note that monthly numbers are available as sunspot.month, though starting slightly later.

Usage

sunspot.year

Format

The univariate time series sunspot.year contains 289 observations, and is of class "ts".

Source

H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

See Also

For monthly sunspot numbers, see sunspot.month and sunspots.

Regularly updated yearly sunspot numbers are available from WDC-SILSO, Royal Observatory of Belgium, at https://www.sidc.be/SILSO/datafiles

Examples

utils::str(sm <- sunspots)# the monthly version we keep unchanged
utils::str(sy <- sunspot.year)
## The common time interval
(t1 <- c(max(start(sm), start(sy)),     1)) # Jan 1749
(t2 <- c(min(  end(sm)[1],end(sy)[1]), 12)) # Dec 1983 (will not be updated!)
s.m <- window(sm, start=t1, end=t2)
s.y <- window(sy, start=t1, end=t2[1])
stopifnot(length(s.y) * 12 == length(s.m),
          ## The yearly series *is* close to the averages of the monthly one:
          all.equal(s.y, aggregate(s.m, FUN = mean), tolerance = 0.0020))
## NOTE: Strangely, correctly weighting the number of days per month
##       (using 28.25 for February) is *not* closer than the simple mean:
ndays <- c(31, 28.25, rep(c(31,30, 31,30, 31), 2))
all.equal(s.y, aggregate(s.m, FUN = mean))                     # 0.0013
all.equal(s.y, aggregate(s.m, FUN = weighted.mean, w = ndays)) # 0.0017

Description

Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

Usage

sunspots

Format

A time series of monthly data from 1749 to 1983.

Source

Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

See Also

sunspot.month has a longer (and a bit different) series, sunspot.year is a much shorter one. See there for getting more current sunspot numbers.

Examples

require(graphics)
plot(sunspots, main = "sunspots data", xlab = "Year",
     ylab = "Monthly sunspot numbers")

Description

Standardized fertility measure and socioeconomic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

Usage

swiss

Format

A data frame with 47 observations on 6 variables, each of which is in percent, i.e., in $[0, 100]$.

All variables but Fertility give proportions of the population.

Details

(paraphrasing Mosteller and Tukey):

Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

The data collected are for 47 French-speaking “provinces” at about 1888.

Here, all variables are scaled to $[0, 100]$, where in the original, all but Catholic were scaled to $[0, 1]$.

Note

Files for all 182 districts in 1888 and other years have been available at https://oprdata.princeton.edu/archive/pefp/switz.aspx.

They state that variables Examination and Education are averages for 1887, 1888 and 1889.

Source

Project “16P5”, pages 549–551 in

Mosteller, F. and Tukey, J. W. (1977) Data Analysis and Regression: A Second Course in Statistics. Addison-Wesley, Reading Mass.

indicating their source as “Data used by permission of Franice van de Walle. Office of Population Research, Princeton University, 1976. Unpublished data assembled under NICHD contract number No 1-HD-O-2077.”

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Examples

require(stats); require(graphics)
pairs(swiss, panel = panel.smooth, main = "swiss data",
      col = 3 + (swiss$Catholic > 50))
summary(lm(Fertility ~ . , data = swiss))

Description

The Theoph data frame has 132 rows and 5 columns of data from an experiment on the pharmacokinetics of theophylline.

Usage

Theoph

Format

An object of class c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame") containing the following columns:

Subject

an ordered factor with levels 1, ..., 12 identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

Wt

weight of the subject (kg).

Dose

dose of theophylline administered orally to the subject (mg/kg).

Time

time since drug administration when the sample was drawn (hr).

conc

theophylline concentration in the sample (mg/L).

Details

Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

This dataset was originally part of package nlme, and that has methods (including for [, as.data.frame, plot and print) for its grouped-data classes.

Source

Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994), NONMEM Users Guide: Part V, NONMEM Project Group, University of California, San Francisco.

Davidian, M. and Giltinan, D. M. (1995) Nonlinear Models for Repeated Measurement Data, Chapman & Hall (section 5.5, p. 145 and section 6.6, p. 176)

Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS, Springer (Appendix A.29)

See Also

SSfol

Examples

require(stats); require(graphics)

coplot(conc ~ Time | Subject, data = Theoph, show.given = FALSE)
Theoph.4 <- subset(Theoph, Subject == 4)
fm1 <- nls(conc ~ SSfol(Dose, Time, lKe, lKa, lCl),
           data = Theoph.4)
summary(fm1)
plot(conc ~ Time, data = Theoph.4,
     xlab = "Time since drug administration (hr)",
     ylab = "Theophylline concentration (mg/L)",
     main = "Observed concentrations and fitted model",
     sub  = "Theophylline data - Subject 4 only",
     las = 1, col = 4)
xvals <- seq(0, par("usr")[2], length.out = 55)
lines(xvals, predict(fm1, newdata = list(Time = xvals)),
      col = 4)