The R DAAG Package

Description

Various data sets and functions used or referred to in the book Maindonald, J.H. and Braun, W.J. (3rd edn 2010) "Data Analysis and Graphics Using R", plus other selected datasets and functions.

Details

For a list of , use library(help="DAAG").

Author(s)

Author: John H Maindonald

Maintainer: W John Braun braun@stats.uwo.ca

Aberrant Crypt Foci in Rat Colons

Description

Numbers of aberrant crypt foci (ACF) in the section 1 of the colons of 22 rats subjected to a single dose of the carcinogen azoxymethane (AOM), sacrificed at 3 different times.

Usage

ACF1

Format

This data frame contains the following columns:

count

Observed number of ACF in section 1 of each rat colon

endtime

Time of sacrifice, in weeks following injection of AOM

Source

Ranjana P. Bird, Faculty of Human Ecology, University of Manitoba, Winnipeg, Canada.

References

E.A. McLellan, A. Medline and R.P. Bird. Dose response and proliferative characteristics of aberrant crypt foci: putative preneoplastic lesions in rat colon. Carcinogenesis, 12(11): 2093-2098, 1991.

Examples

sapply(split(ACF1$count,ACF1$endtime),var)
plot(count ~ endtime, data=ACF1, pch=16)
pause()
print("Poisson Regression - Example 8.3")
ACF.glm0 <- glm(formula = count ~ endtime, family = poisson, data = ACF1)
summary(ACF.glm0)

# Is there a quadratic effect?
pause()

ACF.glm <- glm(formula = count ~ endtime + I(endtime^2),
  family = poisson, data = ACF1)
summary(ACF.glm)

# But is the data really Poisson?  If not, try quasipoisson:
pause()

ACF.glm <- glm(formula = count ~ endtime + I(endtime^2),
  family = quasipoisson, data = ACF1)
summary(ACF.glm)

Cross-Validation for Regression with a Binary Response

Description

These functions give training (internal) and cross-validation measures of predictive accuracy for regression with a binary response. The data are randomly divided between a number of ‘folds’. Each fold is removed, in turn, while the remaining data are used to re-fit the regression model and to predict at the omitted observations.

Usage

CVbinary(obj, rand=NULL, nfolds=10, print.details=TRUE)

cv.binary(obj, rand=NULL, nfolds=10, print.details=TRUE)

Arguments

Value

Note

The term ‘training’ seems preferable to the term ‘internal’ in connection with predicted values, and the accuracy measure, that are based on the observations used to derive the model.

Author(s)

J.H. Maindonald

See Also

glm

Examples

frogs.glm <- glm(pres.abs ~ log(distance) + log(NoOfPools),
                 family=binomial,data=frogs)
CVbinary(frogs.glm)
mifem.glm <- glm(outcome ~ ., family=binomial, data=mifem)
CVbinary(mifem.glm)

Cross-Validation for Linear Regression

Description

This function gives internal and cross-validation measures of predictive accuracy for multiple linear regression. (For binary logistic regression, use the CVbinary function.) The data are randomly assigned to a number of ‘folds’. Each fold is removed, in turn, while the remaining data is used to re-fit the regression model and to predict at the deleted observations.

Usage

CVlm(data = DAAG::houseprices, form.lm = formula(sale.price ~ area),
              m = 3, dots = FALSE, seed = 29, plotit = c("Observed","Residual"),
              col.folds=NULL,               
              main="Small symbols show cross-validation predicted values",
              legend.pos="topleft", 
              printit = TRUE, ...)
cv.lm(data = DAAG::houseprices, form.lm = formula(sale.price ~ area),
              m = 3, dots = FALSE, seed = 29, plotit = c("Observed","Residual"),
              col.folds=NULL,               
              main="Small symbols show cross-validation predicted values",
              legend.pos="topleft", printit = TRUE, ...)
              

Arguments

Details

When plotit="Residual" and there is more than one explanatory variable, the fitted lines that are shown for the individual folds are approximations.

Value

The input data frame is returned, with additional columns Predicted (Predicted values using all observations) and cvpred (cross-validation predictions). The cross-validation residual sum of squares (ss) and degrees of freedom (df) are returned as attributes of the data frame.

Author(s)

J.H. Maindonald

See Also

lm, CVbinary

Examples

CVlm()
## Not run: 
CVlm(data=nihills, form.lm=formula(log(time)~log(climb)+log(dist)),
          plotit="Observed")
CVlm(data=nihills, form.lm=formula(log(time)~log(climb)+log(dist)),
     plotit="Residual")
out <- CVlm(data=nihills, form.lm=formula(log(time)~log(climb)+log(dist)),
               plotit="Observed")
out[c("ms","df")]

## End(Not run)

A Summary of the Cars93 Data set

Description

The Cars93.summary data frame has 6 rows and 4 columns created from information in the Cars93 data set in the Venables and Ripley MASS package. Each row corresponds to a different class of car (e.g. Compact, Large, etc.).

Usage

Cars93.summary

Format

This data frame contains the following columns:

Min.passengers

minimum passenger capacity for each class of car

Max.passengers

maximum passenger capacity for each class of car

No.of.cars

number of cars in each class

abbrev

a factor with levels C Compact, L Large, M Mid-Size, Sm Small, Sp Sporty, V Van

Source

Lock, R. H. (1993) 1993 New Car Data. Journal of Statistics Education 1(1)

References

MASS library

Examples

type <- Cars93.summary$abbrev
type <- Cars93.summary[,4]
type <- Cars93.summary[,"abbrev"]
type <- Cars93.summary[[4]] # Take the object that is stored
                            # in the fourth list element.
type
pause()

attach(Cars93.summary)
  # R can now access the columns of Cars93.summary directly
abbrev
detach("Cars93.summary")
pause()

#  To change the name of the \verb!abbrev! variable (the fourth column)
names(Cars93.summary)[4] <- "code"
pause()

#  To change all of the names, try
names(Cars93.summary) <- c("minpass","maxpass","number","code")

Function to generate lattice themes for graphs.

Description

This generates themes for use in "A Practical Guide to Data Analysis Using R".

Usage

DAAGtheme(fontsize = list(text = 10, points = 6), box = "gray40", color=TRUE,
          sides = list(tck = 0.6, pad1 = 0.75, pad2 = 0.75),...)

Arguments

Details

Setting the color of the bounding box and of the strip boxes to gray, which is the default, reduces the focus on them.

Value

A list which can be used as the par.settings argument to lattice graphics functions, or as the theme argument to trellis.par.set().

Note

The code provides an example of the creation of a functions that generates themes that are tuned to specific user requirements. In this connection, see also theEconomist.theme.

Author(s)

John Maindonald.

See Also

standard.theme, simpleTheme, theEconomist.theme, custom.theme

Examples

bwtheme <- DAAGtheme(pch=2:4)
lattice::xyplot(csoa ~ age | target, groups=sex, 
                data=DAAG::tinting, par.settings=bwtheme)

List, each of whose elements hold rows of a file, in character format

Description

This is the default database for use with the function datafile, which uses elements of this list to place files in the working directory.

Usage

data(DAAGxdb)

Format

Successive elements in this list hold character vectors from which the corresponding files can be generated. The names of the list elements are fuel, fuel.csv, oneBadRow, scan-demo, molclock1, molclock2, and travelbooks.

Details

The files fuel.txt and fuel.csv are used in Chapter 1 of DAAGUR, while the files oneBadRow.txt and scan-demo.txt are used in Chapter 14 of DAAGUR.

References

Maindonald, J.H. and Braun, W.J. 2007. Data Analysis and Graphics Using R: An Example-Based Approach. 2nd edn, Cambridge University Press (DAAGUR).

Examples

data(DAAGxdb)
names(DAAGxdb)

Ontario Lottery Data

Description

The data frame Lottario is a summary of 122 weekly draws of an Ontario lottery, beginning in November, 1978. Each draw consists of 7 numbered balls, drawn without replacement from an urn consisting of balls numbered from 1 through 39.

Usage

Lottario

Format

This data frame contains the following columns:

Number

the integers from 1 to 39, representing the numbered balls

Frequency

the number of occurrences of each numbered ball

Source

The Ontario Lottery Corporation

References

Bellhouse, D.R. (1982). Fair is fair: new rules for Canadian lotteries. Canadian Public Policy - Analyse de Politiques 8: 311-320.

Examples

 
order(Lottario$Frequency)[33:39]  # the 7 most frequently chosen numbers

The Nine Largest Lakes in Manitoba

Description

The Manitoba.lakes data frame has 9 rows and 2 columns. The areas and elevations of the nine largest lakes in Manitoba, Canada. The geography of Manitoba (a relatively flat province) can be divided crudely into three main areas: a very flat prairie in the south which is at a relatively high elevation, a middle region consisting of mainly of forest and Precambrian rock, and a northern region which drains more rapidly into Hudson Bay. All water in Manitoba, which does not evaporate, eventually drains into Hudson Bay.

Usage

Manitoba.lakes

Format

This data frame contains the following columns:

elevation

a numeric vector consisting of the elevations of the lakes (in meters)

area

a numeric vector consisting of the areas of the lakes (in square kilometers)

Source

The CANSIM data base at Statistics Canada.

Examples

plot(Manitoba.lakes)
plot(Manitoba.lakes[-1,])

Closing Numbers for S and P 500 Index - First 100 Days of 1990

Description

Closing numbers for S and P 500 Index, Jan. 1, 1990 through early 2000.

Usage

SP500W90

Source

Derived from SP500 in the MASS library.

Examples

ts.plot(SP500W90)

Closing Numbers for S and P 500 Index

Description

Closing numbers for S and P 500 Index, Jan. 1, 1990 through early 2000.

Usage

SP500close

Source

Derived from SP500 in the MASS library.

Examples

ts.plot(SP500close)

Australian athletes data set

Description

These data were collected in a study of how data on various characteristics of the blood varied with sport, body size, and sex of the athlete.

Usage

data(ais)

Format

A data frame with 202 observations on the following 13 variables.

rcc

red blood cell count, in 10^{12} l^{-1}

wcc

while blood cell count, in 10^{12} per liter

hc

hematocrit, percent

hg

hemaglobin concentration, in g per decaliter

ferr

plasma ferritins, ng dl^{-1}

bmi

Body mass index, kg cm^{-2} 10^2

ssf

sum of skin folds

pcBfat

percent Body fat

lbm

lean body mass, kg

ht

height, cm

wt

weight, kg

sex

a factor with levels f m

sport

a factor with levels B_Ball Field Gym Netball Row Swim T_400m T_Sprnt Tennis W_Polo

Details

Do blood hemoglobin concentrations of athletes in endurance-related events differ from those in power-related events?

Source

These data were the basis for the analyses that are reported in Telford and Cunningham (1991).

References

Telford, R.D. and Cunningham, R.B. 1991. Sex, sport and body-size dependency of hematology in highly trained athletes. Medicine and Science in Sports and Exercise 23: 788-794.

Function to align points from ordination with known locations

Description

Find the linear transformation which, applied to one set of points in the ($x$, $y$) plane, gives the best match in a least squares sense to a second set of points.

Usage

align2D(lat, long, x1, x2, wts=NULL)

Arguments

Details

Achieves the best match, in a least squares sense, between an ordination and known locations in two-dimensionaL space.

Value

Note

An ordination that is designed to reproduce distances between points is specified only to within an arbitrary rotation about the centroid. What linear transformation of the points ($x1$, $x2$) given by the ordination gives the best match to the known co-ordinates?

Author(s)

John H Maindonald

Examples

if(require(DAAG)&require(oz)){
aupts <- cmdscale(audists)
xy <- align2D(lat = aulatlong$latitude, long = aulatlong$longitude,
              x1 = aupts[, 1], x2 = aupts[, 2], wts = NULL)
oz()
fitcoords <- align2D(lat=aulatlong$latitude,
                      long=aulatlong$longitude,
                      x1=aupts[,1], x2 = aupts[,2],
                      wts=NULL)
x <-with(fitcoords,
         as.vector(rbind(lat, fitlat, rep(NA,length(lat)))))
y <-with(fitcoords,
         as.vector(rbind(long, fitlong, rep(NA,length(long)))))
points(aulatlong, col="red", pch=16, cex=1.5)
lines(x, y, col="gray40", lwd=3)
}

## The function is currently defined as
function(lat, long, x1, x2, wts=NULL){
    ## Get best fit in space of (latitude, longitude)
    if(is.null(wts))wts <- rep(1,length(x1))
    fitlat <- predict(lm(lat ~ x1+x2, weights=wts))
    fitlong <- predict(lm(long ~ x1+x2, weights=wts))
    list(fitlat = fitlat, fitlong=fitlong, lat=lat, long=long)
}

Measurements on a Selection of Books

Description

The allbacks data frame gives measurements on the volume and weight of 15 books, some of which are softback (pb) and some of which are hardback (hb). Area of the hardback covers is also included.

Usage

allbacks

Format

This data frame contains the following columns:

volume

book volumes in cubic centimeters

area

hard board cover areas in square centimeters

weight

book weights in grams

cover

a factor with levels hb hardback, pb paperback

Source

The bookshelf of J. H. Maindonald.

Examples

print("Multiple Regression - Example 6.1")
attach(allbacks)
volume.split <- split(volume, cover)
weight.split <- split(weight, cover)
plot(weight.split$hb ~ volume.split$hb, pch=16, xlim=range(volume), ylim=range(weight),
     ylab="Weight (g)", xlab="Volume (cc)")
points(weight.split$pb ~ volume.split$pb, pch=16, col=2)
pause()

allbacks.lm <- lm(weight ~ volume+area)
summary(allbacks.lm)
detach(allbacks)
pause()

anova(allbacks.lm)
pause()

model.matrix(allbacks.lm)
pause()

print("Example 6.1.1")
allbacks.lm0 <- lm(weight ~ -1+volume+area, data=allbacks)
summary(allbacks.lm0)
pause()

print("Example 6.1.2")
oldpar <- par(mfrow=c(2,2))
plot(allbacks.lm0)
par(oldpar)
allbacks.lm13 <- lm(weight ~ -1+volume+area, data=allbacks[-13,])
summary(allbacks.lm13)
pause()

print("Example 6.1.3")
round(coef(allbacks.lm0),2)  # Baseline for changes
round(lm.influence(allbacks.lm0)$coef,2)

Anesthetic Effectiveness

Description

Thirty patients were given an anesthetic agent maintained at a predetermined level (conc) for 15 minutes before making an incision. It was then noted whether the patient moved, i.e. jerked or twisted.

Usage

anesthetic

Format

This data frame contains the following columns:

move

a binary numeric vector coded for patient movement (0 = no movement, 1 = movement)

conc

anesthetic concentration

logconc

logarithm of concentration

nomove

the complement of move

Details

The interest is in estimating how the probability of jerking or twisting varies with increasing concentration of the anesthetic agent.

Source

unknown

Examples

print("Logistic Regression - Example 8.1.4")

z <- table(anesthetic$nomove, anesthetic$conc)
tot <- apply(z, 2, sum)         # totals at each concentration
prop <- z[2,  ]/(tot)           # proportions at each concentration
oprop <- sum(z[2,  ])/sum(tot)  # expected proportion moving if concentration had no effect
conc <- as.numeric(dimnames(z)[[2]])
plot(conc, prop, xlab = "Concentration", ylab = "Proportion", xlim = c(.5,2.5),
    ylim = c(0, 1), pch = 16)
chw <- par()$cxy[1]
text(conc - 0.75 * chw, prop, paste(tot), adj = 1)
abline(h = oprop, lty = 2)

pause()

anes.logit <- glm(nomove ~ conc, family = binomial(link = logit),
  data = anesthetic)
anova(anes.logit)
summary(anes.logit)

Averages by block of yields for the Antigua Corn data

Description

These data frames have yield averages by blocks (parcels). The ant111bdataset is a subset that has block averages of corn yields for treatment 111 only

Usage

  data(antigua)
  data(ant111b)
  

Format

A data frame with 324 observations on 7 variables.

id

a numeric vector

site

a factor with 8 levels.

block

a factor with levels I II III IV

plot

a numeric vector

trt

a factor consisting of 12 levels

ears

a numeric vector; note that -9999 is used as a missing value code.

harvwt

a numeric vector; the average yield

Source

Andrews DF; Herzberg AM, 1985. Data. A Collection of Problems from Many Fields for the Student and Research Worker. Springer-Verlag. (pp. 339-353)

Tasting experiment that compared four apple varieties

Description

Each of 20 tasters each assessed three out of the four varieties. The experiment was conducted according to a balanced incomplete block design.

Usage

data(appletaste)

Format

A data frame with 60 observations on the following 3 variables.

aftertaste

a numeric vector

Apple samples were rated for aftertaste, by making a mark on a continuous scale that ranged from 0 (extreme dislike) to 150 (like very much).

panelist

a factor with levels a b c d e f g h i j k l m n o p q r s t

product

a factor with levels 298 493 649 937

Examples

data(appletaste)
appletaste.aov <- aov(aftertaste ~ panelist + product, data=appletaste)
termplot(appletaste.aov)

Road distances between 10 Australian cities

Description

Distances between the Australian cities of Adelaide, Alice, Brisbane, Broome, Cairns, Canberra, Darwin, Melbourne, Perth and Sydney

Usage

audists

Format

The format is: Class 'dist', i.e., a distance matrix.

Source

Australian road map

Examples

data(audists)
## Not run: 
audists.cmd <- cmdscale(audists)
library(lattice)
xyplot(audists.cmd[,2] ~ audists.cmd[,1], 
       groups=row.names(audists.cmd),
       panel = function(x, y, subscripts, groups)  
                        ltext(x = x, y = y, label = groups[subscripts],
                        cex=1, fontfamily = "HersheySans"))

## End(Not run)

Latitudes and longitudes for ten Australian cities

Description

Latitudes and longitudes for Adelaide, Alice, Brisbane, Broome, Cairns, Canberra, Darwin, Melbourne, Perth and Sydney; i.e., for the cities to which the road distances in audists relate.

Usage

aulatlong

Format

A data frame with 10 observations on the following 2 variables.

latitude

Latitude, as a decimal number

longitude

Latitude, as a decimal number

Source

Map of Australia showing latitude and longitude information.

Examples

data(aulatlong)
## maybe str(aulatlong) ; plot(aulatlong) ...

Population figures for Australian States and Territories

Description

Population figures for Australian states and territories for 1917, 1927, ..., 1997.

Usage

austpop

Format

This data frame contains the following columns:

year

a numeric vector

NSW

New South Wales population counts

Vic

Victoria population counts

Qld

Queensland population counts

SA

South Australia population counts

WA

Western Australia population counts

Tas

Tasmania population counts

NT

Northern Territory population counts

ACT

Australian Capital Territory population counts

Aust

Population counts for the whole country

Source

Australian Bureau of Statistics

Examples

print("Looping - Example 1.7")

growth.rates <- numeric(8)
for (j in seq(2,9)) {
    growth.rates[j-1] <- (austpop[9, j]-austpop[1, j])/austpop[1, j] }
growth.rates <- data.frame(growth.rates)
row.names(growth.rates) <- names(austpop[c(-1,-10)])
  # Note the use of row.names() to name the rows of the data frame
growth.rates

pause()
print("Avoiding Loops - Example 1.7b")

sapply(austpop[,-c(1,10)], function(x){(x[9]-x[1])/x[1]})

pause()
print("Plot - Example 1.8a")
attach(austpop)
plot(year, ACT, type="l") # Join the points ("l" = "line")
detach(austpop)

pause()
print("Exerice 1.12.9")
attach(austpop)
oldpar <- par(mfrow=c(2,4))  
for (i in 2:9){
plot(austpop[,1], log(austpop[, i]), xlab="Year",
    ylab=names(austpop)[i], pch=16, ylim=c(0,10))}
par(oldpar) 
detach(austpop)

Best Subset Selection Applied to Noise

Description

Best subset selection applied to completely random noise. This function demonstrates how variable selection techniques in regression can often err in including explanatory variables that are indistinguishable from noise.

Usage

bestsetNoise(m = 100, n = 40, method = "exhaustive", nvmax = 3,
              X = NULL, y=NULL, intercept=TRUE,
              print.summary = TRUE, really.big = FALSE, ...)

bestset.noise(m = 100, n = 40, method = "exhaustive", nvmax = 3,
              X = NULL, y=NULL, intercept=TRUE,
              print.summary = TRUE, really.big = FALSE, ...)

bsnCV(m = 100, n = 40, method = "exhaustive", nvmax = 3,
              X = NULL, y=NULL, intercept=TRUE, nfolds = 2,
              print.summary = TRUE, really.big = FALSE)

bsnOpt(X = matrix(rnorm(25 * 10), ncol = 10), y = NULL, method = "exhaustive",
              nvmax = NULL, nbest = 1, intercept = TRUE, criterion = "cp",
              tcrit = NULL, print.summary = TRUE, really.big = FALSE,
         ...)

bsnVaryNvar(m = 100, nvar = nvmax:50, nvmax = 3, method = "exhaustive",
              intercept=TRUE,
              plotit = TRUE, xlab = "# of variables from which to select",
              ylab = "p-values for t-statistics", main = paste("Select 'best'",
                                                  nvmax, "variables"),
              details = FALSE, really.big = TRUE, smooth = TRUE, ...)

Arguments

Details

If X is not supplied, and in any case for bsnVaryNvar, a set of n predictor variables are simulated as independent standard normal, i.e. N(0,1), variates. Additionally a N(0,1) response variable is simulated. The function bsnOpt selects the ‘best’ model with nvmax or fewer explanatory variables, where the argument criterion specifies the criterion that will be used to choose between models with different numbers of explanatory columns. Other functions select the ‘best’ model with nvmax explanatory columns. In any case, the selection is made using the regsubsets() function from the leaps package. (The leaps package must be installed for this function to work.)

The function bsnCV splits the data (randomly) into nfolds (2 or more) parts. It puts each part aside in turn for use to fit the model (effectively, test data), with the remaining data used for selecting the variables that will be used for fitting. One model fit is returned for each of the nfolds parts.

The function bsnVaryVvar makes repeated calls to bestsetNoise

Value

bestsetNoise returns the lm model object for the "best" model with nvmax explanatory columns.

bsnCV returns as many models as there are folds.

bsnVaryVvar silently returns either (details=FALSE) a matrix that has p-values of the coefficients for the ‘best’ choice of model for each different number of candidate variables, or (details=TRUE) a list with elements:

Matrices have one row for each choice of nvar. The statistics returned are for the ‘best’ model with nvmax explanatory variables.

bsnOpt silently returns a list with elements:

Note

These functions are primarily designed to demonstrate the biases that can be expected, relative to theoretical estimates of standard errors of parameters and other fitted model statistics, when there is prior selection of the columns that are to be included in the model. With the exception of bsnVaryNvar, they can also be used with an X and y for actual data. In that case, the p-values should be compared with those obtained from repeated use of the function where y is random noise, as a check on the extent of selection effects.

Author(s)

J.H. Maindonald

See Also

lm

Examples

leaps.out <- try(require(leaps, quietly=TRUE))
leaps.out.log <- is.logical(leaps.out)
if ((leaps.out.log==TRUE)&(leaps.out==TRUE)){
bestsetNoise(20,6) # `best' 3-variable regression for 20 simulated observations
                   # on 7 unrelated variables (including the response)
bsnCV(20,6) # `best' 3-variable regressions (one for each fold) for 20
                   # simulated observations on 7 unrelated variables
                   # (including the response)
bsnVaryNvar(m = 50, nvar = 3:6, nvmax = 3, method = "exhaustive",
            plotit=FALSE, details=TRUE)
bsnOpt()
}

Biomass Data

Description

The biomass data frame has 135 rows and 8 columns. The rainforest data frame is a subset of this one.

Usage

biomass

Format

This data frame contains the following columns:

dbh

a numeric vector

wood

a numeric vector

bark

a numeric vector

fac26

a factor with 3 levels

root

a numeric vector

rootsk

a numeric vector

branch

a numeric vector

species

a factor with levels Acacia mabellae, C. fraseri, Acmena smithii, B. myrtifolia

Source

J. Ash, Australian National University

References

Ash, J. and Helman, C. (1990) Floristics and vegetation biomass of a forest catchment, Kioloa, south coastal N.S.W. Cunninghamia, 2: 167-182.

Australian and Related Historical Annual Climate Data, by Region

Description

Australian regional temperature data, Australian regional rainfall data, and Annual SOI, are given for the years 1900-2021. The regional rainfall and temperature data are area-weighted averages for the respective regions. The Southern Oscillation Index (SOI) is the difference in barometric pressure at sea level between Tahiti and Darwin.

Usage

data("bomregions2021")

Format

These data frames contains the following columns:

Year

Year

seAVt

Southeastern region average temperature (degrees C)

southAVt

Southern temperature

eastAVt

Eastern temperature

northAVt

Northern temperature

swAVt

Southwestern temperature

qldAVt

temperature

nswAVt

temperature

ntAVt

temperature

saAVt

temperature

tasAVt

temperature

vicAVt

temperature

waAVt

temperature

mdbAVt

Murray-Darling basin temperature

ausAVt

Australian average temperature, area-weighted mean

seRain

Southeast Australian annual rainfall (mm)

southRain

Southern rainfall

eastRain

Eastern rainfall

northRain

Northern rainfall

swRain

Southwest rainfall

qldRain

Queensland rainfall

nswRain

NSW rainfall

ntRain

Northern Territory rainfall

saRain

South Australian rainfall

tasRain

Tasmanian rainfall

vicRain

Victorian rainfall

waRain

West Australian rainfall

mdbRain

Murray-Darling basin rainfall

ausRain

Australian average rainfall, area weighted

SOI

Annual average Southern Oscillation Index

sunspot

Yearly mean sunspot number

co2mlo

Moana Loa CO2 concentrations, from 1959

co2law

Moana Loa CO2 concentrations, 1900 to 1978

CO2

CO2 concentrations, composite series

avDMI

Annual average Dipole Mode Index, for the Indian Ocean Dipole, from 1950

Source

Australian Bureau of Meteorology web pages:

Go to the url http://www.bom.gov.au/climate/change/, choose timeseries to display, then click "Download data"

For the SOI data, go to the url http://www.bom.gov.au/climate/enso/.

The CO2 series co2law, for Law Dome ice core data. is from https://data.ess-dive.lbl.gov/portals/CDIAC/.

The Moana Loa CO2 series co2mlo is from Dr. Pieter Tans, NOAA/ESRL (https://gml.noaa.gov/ccgg/trends/)

The series CO2 is a composite series, obtained by adding 0.46 to the Law data for 1900 to 1958, then following this with the Moana Loa data that is avaiable from 1959. The addition of 0.46 brings the average of the Law data into agreement with that for the Moana Loa data for the period 1959 to 1968.

The yearly mean sunspot number is a subset of one of several sunspot series that are available from WDC-SILSO, Royal Observatory of Belgium, Brussels. https://www.sidc.be/silso/datafiles/

The dipole mode index data are from https://ds.data.jma.go.jp/tcc/tcc/products/elnino/index/Readme_iod.txt. Note also https://stateoftheocean.osmc.noaa.gov/sur/ind/dmi.php, which has details of several other such series.

References

D.M. Etheridge, L.P. Steele, R.L. Langenfelds, R.J. Francey, J.-M. Barnola and V.I. Morgan, 1998, Historical CO2 records from the Law Dome DE08, DE08-2, and DSS ice cores, in Trends: A Compendium of Data on Global Change, on line at Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A.

Lavery, B., Joung, G. and Nicholls, N. 1997. An extended high-quality historical rainfall dataset for Australia. Australian Meteorological Magazine, 46, 27-38.

Nicholls, N., Lavery, B., Frederiksen, C.\ and Drosdowsky, W. 1996. Recent apparent changes in relationships between the El Nino – southern oscillation and Australian rainfall and temperature. Geophysical Research Letters 23: 3357-3360.

SIDC-team, World Data Center for the Sunspot Index, Royal Observatory of Belgium, Monthly Report on the International Sunspot Number, online catalogue of the sunspot index: https://www.sidc.be/silso/datafiles

Examples

plot(ts(bomregions2021[, c("mdbRain","SOI")], start=1900),
     panel=function(y,...)panel.smooth(bomregions2021$Year, y,...))
avrain <- bomregions2021[,"mdbRain"]
xbomsoi <- with(bomregions2021, data.frame(Year=Year, SOI=SOI,
                cuberootRain=avrain^0.33))
xbomsoi$trendSOI <- lowess(xbomsoi$SOI, f=0.1)$y
xbomsoi$trendRain <- lowess(xbomsoi$cuberootRain, f=0.1)$y
xbomsoi$detrendRain <-
  with(xbomsoi, cuberootRain - trendRain + mean(trendRain))
xbomsoi$detrendSOI <-
  with(xbomsoi, SOI - trendSOI + mean(trendSOI))
## Plot time series avrain and SOI: ts object xbomsoi
plot(ts(xbomsoi[, c("cuberootRain","SOI")], start=1900),
     panel=function(y,...)panel.smooth(xbomsoi$Year, y,...),
     xlab = "Year", main="", ylim=list(c(250, 800),c(-20,25)))
par(mfrow=c(1,2))
rainpos <- pretty(xbomsoi$cuberootRain^3, 6)
plot(cuberootRain ~ SOI, data = xbomsoi,
     ylab = "Rainfall (cube root scale)", yaxt="n")
axis(2, at = rainpos^0.33, labels=paste(rainpos))
mtext(side = 3, line = 0.8, "A", adj = -0.025)
with(xbomsoi, lines(lowess(cuberootRain ~ SOI, f=0.75)))
plot(detrendRain ~ detrendSOI, data = xbomsoi,
     xlab="Detrended SOI", ylab = "Detrended rainfall", yaxt="n")
axis(2, at = rainpos^0.33, labels=paste(rainpos))
with(xbomsoi, lines(lowess(detrendRain ~ detrendSOI, f=0.75)))
mtext(side = 3, line = 0.8, "B", adj = -0.025)
par(mfrow=c(1,1))

Southern Oscillation Index Data

Description

The Southern Oscillation Index (SOI) is the difference in barometric pressure at sea level between Tahiti and Darwin. Annual SOI and Australian rainfall data, for the years 1900-2005, are given. Australia's annual mean rainfall is an area-weighted average of the total annual precipitation at approximately 370 rainfall stations around the country.

Usage

bomsoi

Format

This data frame contains the following columns:

Year

a numeric vector

Jan

average January SOI values for each year

Feb

average February SOI values for each year

Mar

average March SOI values for each year

Apr

average April SOI values for each year

May

average May SOI values for each year

Jun

average June SOI values for each year

Jul

average July SOI values for each year

Aug

average August SOI values for each year

Sep

average September SOI values for each year

Oct

average October SOI values for each year

Nov

average November SOI values for each year

Dec

average December SOI values for each year

SOI

a numeric vector consisting of average annual SOI values

avrain

a numeric vector consisting of a weighted average annual rainfall at a large number of Australian sites

NTrain

Northern Territory rain

northRain

north rain

seRain

southeast rain

eastRain

east rain

southRain

south rain

swRain

southwest rain

Source

Australian Bureau of Meteorology web pages:

http://www.bom.gov.au/climate/change/rain02.txt and http://www.bom.gov.au/climate/current/soihtm1.shtml

References

Nicholls, N., Lavery, B., Frederiksen, C.\ and Drosdowsky, W. 1996. Recent apparent changes in relationships between the El Nino – southern oscillation and Australian rainfall and temperature. Geophysical Research Letters 23: 3357-3360.

Examples

 
plot(ts(bomsoi[, 15:14], start=1900),
     panel=function(y,...)panel.smooth(1900:2005, y,...))
pause()

# Check for skewness by comparing the normal probability plots for 
# different a, e.g.
par(mfrow = c(2,3))
for (a in c(50, 100, 150, 200, 250, 300))
qqnorm(log(bomsoi[, "avrain"] - a))
  # a = 250 leads to a nearly linear plot

pause()

par(mfrow = c(1,1))
plot(bomsoi$SOI, log(bomsoi$avrain - 250), xlab = "SOI",
     ylab = "log(avrain = 250)")
lines(lowess(bomsoi$SOI)$y, lowess(log(bomsoi$avrain - 250))$y, lwd=2)
  # NB: separate lowess fits against time
lines(lowess(bomsoi$SOI, log(bomsoi$avrain - 250)))
pause()

xbomsoi <-
  with(bomsoi, data.frame(SOI=SOI, cuberootRain=avrain^0.33))
xbomsoi$trendSOI <- lowess(xbomsoi$SOI)$y
xbomsoi$trendRain <- lowess(xbomsoi$cuberootRain)$y
rainpos <- pretty(bomsoi$avrain, 5)
with(xbomsoi,
     {plot(cuberootRain ~ SOI, xlab = "SOI",
           ylab = "Rainfall (cube root scale)", yaxt="n")
     axis(2, at = rainpos^0.33, labels=paste(rainpos))
## Relative changes in the two trend curves
     lines(lowess(cuberootRain ~ SOI))
     lines(lowess(trendRain ~ trendSOI), lwd=2)
  })
pause()

xbomsoi$detrendRain <-
  with(xbomsoi, cuberootRain - trendRain + mean(trendRain))
xbomsoi$detrendSOI <-
  with(xbomsoi, SOI - trendSOI + mean(trendSOI))
oldpar <- par(mfrow=c(1,2), pty="s")
plot(cuberootRain ~ SOI, data = xbomsoi,
     ylab = "Rainfall (cube root scale)", yaxt="n")
axis(2, at = rainpos^0.33, labels=paste(rainpos))
with(xbomsoi, lines(lowess(cuberootRain ~ SOI)))
plot(detrendRain ~ detrendSOI, data = xbomsoi,
  xlab="Detrended SOI", ylab = "Detrended rainfall", yaxt="n")
axis(2, at = rainpos^0.33, labels=paste(rainpos))
with(xbomsoi, lines(lowess(detrendRain ~ detrendSOI)))
pause()

par(oldpar)
attach(xbomsoi)
xbomsoi.ma0 <- arima(detrendRain, xreg=detrendSOI, order=c(0,0,0))
# ordinary regression model

xbomsoi.ma12 <- arima(detrendRain, xreg=detrendSOI,
                      order=c(0,0,12))
# regression with MA(12) errors -- all 12 MA parameters are estimated
xbomsoi.ma12
pause()

xbomsoi.ma12s <- arima(detrendRain, xreg=detrendSOI,
                      seasonal=list(order=c(0,0,1), period=12))
# regression with seasonal MA(1) (lag 12) errors -- only 1 MA parameter
# is estimated
xbomsoi.ma12s
pause()

xbomsoi.maSel <- arima(x = detrendRain, order = c(0, 0, 12),
                        xreg = detrendSOI, fixed = c(0, 0, 0,
                        NA, rep(0, 4), NA, 0, NA, NA, NA, NA),
                        transform.pars=FALSE)
# error term is MA(12) with fixed 0's at lags 1, 2, 3, 5, 6, 7, 8, 10
# NA's are used to designate coefficients that still need to be estimated
# transform.pars is set to FALSE, so that MA coefficients are not
# transformed (see help(arima))

detach(xbomsoi)
pause()

Box.test(resid(lm(detrendRain ~ detrendSOI, data = xbomsoi)),
          type="Ljung-Box", lag=20)

pause()

attach(xbomsoi)
 xbomsoi2.maSel <- arima(x = detrendRain, order = c(0, 0, 12),
                         xreg = poly(detrendSOI,2), fixed = c(0,
                         0, 0, NA, rep(0, 4), NA, 0, rep(NA,5)),
                         transform.pars=FALSE)
 xbomsoi2.maSel
qqnorm(resid(xbomsoi.maSel, type="normalized"))
detach(xbomsoi)

Boston Housing Data – Corrected

Description

The corrected Boston housing data (from http://lib.stat.cmu.edu/datasets/).

Usage

bostonc

Format

A single vector containing the contents of "boston_corrected.txt".

Source

Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978. corrected by Kelley Pace (kpace@unix1.sncc.lsu.edu)

Separate plotting positions for labels, to avoid overlap

Description

Return univariate plotting positions in which neighboring points are separated, if and as necessary, so that they are the specified minimum distance apart.

Usage

bounce(y, d, log = FALSE)

Arguments

Details

The centroid(s) of groups of points that are moved relative to each other remain the same.

Value

A vector of values such that, when plotted along a line, neighboring points are the required minimum distance apart.

Note

If values are plotted on a logarithmic scale, d is the required distance apart on that scale. If a base other than 10 is required, set log equal to that base. (Note that base 10 is the default for plot with log=TRUE.)

Author(s)

John Maindonald

See Also

See also onewayPlot

Examples

bounce(c(4, 1.8, 2, 6), d=.4)
bounce(c(4, 1.8, 2, 6), d=.1, log=TRUE)

Converts initial character of a string to upper case

Description

This function is useful for use before plotting, if one wants capitalized axis labels or factor levels.

Usage

capstring(names)

Arguments

Value

A character vector with upper case initial values.

Author(s)

W.J. Braun

Examples

capstring(names(tinting)[c(3,4)])

library(lattice)
levels(tinting$agegp) <- capstring(levels(tinting$agegp))
xyplot(csoa ~ it | sex * agegp, data=tinting) 

US Car Price Data

Description

U.S. data extracted from Cars93, a data frame in the MASS package.

Usage

carprice

Format

This data frame contains the following columns:

Type

Type of car, e.g. Sporty, Van, Compact

Min.Price

Price for a basic model

Price

Price for a mid-range model

Max.Price

Price for a ‘premium’ model

Range.Price

Difference between Max.Price and Min.Price

RoughRange

Rough.Range plus some N(0,.0001) noise

gpm100

The number of gallons required to travel 100 miles

MPG.city

Average number of miles per gallon for city driving

MPG.highway

Average number of miles per gallon for highway driving

Source

MASS package

References

Venables, W.N.\ and Ripley, B.D., 4th edn 2002. Modern Applied Statistics with S. Springer, New York.

See also ‘R’ Complements to Modern Applied Statistics with S-Plus, available from http://www.stats.ox.ac.uk/pub/MASS3/

Examples

 
print("Multicollinearity - Example 6.8")
pairs(carprice[,-c(1,8,9)])

carprice1.lm <- lm(gpm100 ~ Type+Min.Price+Price+Max.Price+Range.Price,
    data=carprice)
round(summary(carprice1.lm)$coef,3)
pause()

alias(carprice1.lm)
pause()

carprice2.lm <- lm(gpm100 ~ Type+Min.Price+Price+Max.Price+RoughRange, data=carprice)
round(summary(carprice2.lm)$coef, 2)
pause()

carprice.lm <- lm(gpm100 ~ Type + Price, data = carprice)
round(summary(carprice.lm)$coef,4)  
pause()

summary(carprice1.lm)$sigma   # residual standard error when fitting all 3 price variables
pause()

summary(carprice.lm)$sigma    # residual standard error when only price is used
pause()

vif(lm(gpm100 ~ Price, data=carprice)) # Baseline Price
pause()

vif(carprice1.lm)    # includes Min.Price, Price & Max.Price
pause()

vif(carprice2.lm)    # includes Min.Price, Price, Max.Price & RoughRange
pause()

vif(carprice.lm)     # Price alone

Percentage of Sugar in Breakfast Cereal

Description

Measurements of sugar content in frosted flakes breakfast cereal.

Usage

cerealsugar

Format

A vector of 100 measurements.

Cape Fur Seal Data

Description

The cfseal data frame has 30 rows and 11 columns consisting of weight measurements for various organs taken from 30 Cape Fur Seals that died as an unintended consequence of commercial fishing.

Usage

cfseal

Format

This data frame contains the following columns:

age

a numeric vector

weight

a numeric vector

heart

a numeric vector

lung

a numeric vector

liver

a numeric vector

spleen

a numeric vector

stomach

a numeric vector

leftkid

a numeric vector

rightkid

a numeric vector

kidney

a numeric vector

intestines

a numeric vector

Source

Stewardson, C.L., Hemsley, S., Meyer, M.A., Canfield, P.J. and Maindonald, J.H. 1999. Gross and microscopic visceral anatomy of the male Cape fur seal, Arctocephalus pusillus pusillus (Pinnepedia: Otariidae), with reference to organ size and growth. Journal of Anatomy (Cambridge) 195: 235-255. (WWF project ZA-348)

Examples

print("Allometric Growth - Example 5.7")

cfseal.lm <- lm(log(heart) ~ log(weight), data=cfseal); summary(cfseal.lm)
plot(log(heart) ~ log(weight), data = cfseal, pch=16, xlab = "Heart Weight (g, log scale)", 
ylab = "Body weight (kg, log scale)", axes=FALSE)
heartaxis <- 100*(2^seq(0,3))
bodyaxis <- c(20,40,60,100,180)
axis(1, at = log(bodyaxis), lab = bodyaxis)
axis(2, at = log(heartaxis), lab = heartaxis)
box()
abline(cfseal.lm)

Populations of Major Canadian Cities (1992-96)

Description

Population estimates for several Canadian cities.

Usage

cities

Format

This data frame contains the following columns:

CITY

a factor, consisting of the city names

REGION

a factor with 5 levels (ATL=Atlantic, ON=Ontario, QC=Quebec, PR=Prairies, WEST=Alberta and British Columbia) representing the location of the cities

POP1992

a numeric vector giving population in 1000's for 1992

POP1993

a numeric vector giving population in 1000's for 1993

POP1994

a numeric vector giving population in 1000's for 1994

POP1995

a numeric vector giving population in 1000's for 1995

POP1996

a numeric vector giving population in 1000's for 1996

Source

Statistics Canada

Examples

cities$have <- factor((cities$REGION=="ON")|(cities$REGION=="WEST"))
plot(POP1996~POP1992, data=cities, col=as.integer(cities$have))

Dose-mortality data, for fumigation of codling moth with methyl bromide

Description

Data are from trials that studied the mortality response of codling moth to fumigation with methyl bromide.

Usage

data(codling)

Format

A data frame with 99 observations on the following 10 variables.

dose

Injected dose of methyl bromide, in gm per cubic meter

tot

Number of insects in chamber

dead

Number of insects dying

pobs

Proportion dying

cm

Control mortality, i.e., at dose 0

ct

Concentration-time sum

Cultivar

a factor with levels BRAEBURN FUJI GRANNY Gala ROYAL Red Delicious Splendour

gp

a factor which has a different level for each different combination of Cultivar, year and rep (replicate).

year

a factor with levels 1988 1989

numcm

a numeric vector: total number of control insects

Details

The research that generated these data was in part funded by New Zealand pipfruit growers. The published analysis was funded by New Zealand pipfruit growers. See also sorption.

Source

Maindonald, J.H.; Waddell, B.C.; Petry, R.J. 2001. Apple cultivar effects on codling moth (Lepidoptera: Tortricidae) egg mortality following fumigation with methyl bromide. Postharvest Biology and Technology 22: 99-110.

Error rate comparisons for tree-based classification

Description

Compare error rates, between different functions and different selection rules, for an approximately equal random division of the data into a training and test set.

Usage

compareTreecalcs(x = yesno ~ ., data = DAAG::spam7, cp = 0.00025, fun = c("rpart",
"randomForest"))

Arguments

Details

Data are randomly divided into two subsets, I and II. The function(s) are used in the standard way for calculations on subset I, and error rates returined that come from the calculations carried out by the function(s). Predictions are made for subset II, allowing the calculation of a completely independent set of error rates.

Value

If rpart is specified in fun, the following:

If rpart is specified in fun, the following:

Author(s)

John Maindonald

Component + Residual Plot

Description

Component + Residual plot for a term in a lm model.

Usage

component.residual(lm.obj, which = 1, xlab = "Component",
    ylab = "C+R")

Arguments

Value

A scatterplot with a smooth curve overlaid.

Author(s)

J.H. Maindonald

See Also

lm

Examples

mice12.lm <- lm(brainwt ~ bodywt + lsize, data=litters)
oldpar <- par(mfrow = c(1,2))
component.residual(mice12.lm, 1, xlab = "Body weight", ylab= "t(Body weight) + e")
component.residual(mice12.lm, 2, xlab = "Litter size", ylab= "t(Litter size) + e")
par(oldpar)

Given actual and predicted group assignments, give the confusion matrix

Description

Given actual and predicted group assignments, give the confusion matrix

Usage

confusion(actual, predicted, gpnames = NULL, rowcol=c("actual", "predicted"),
printit = c("overall","confusion"), prior = NULL, digits=3)

Arguments

Details

Predicted group assignments should be estimated from cross-validation or from bootstrap out-of-bag data. Better still, work with assignments for test data that are completely separate from the data used to dervive the model.

Value

A list with elements overall (overall accuracy), confusion (confusion matrix) and prior (prior used for calculation of overall accuracy)

Author(s)

John H Maindonald

References

Maindonald and Braun: 'Data Analysis and Graphics Using R', 3rd edition 2010, Section 12.2.2

Examples

library(MASS)
library(DAAG)
cl <- lda(species ~ length+breadth, data=cuckoos, CV=TRUE)$class
confusion(cl, cuckoos$species)

## The function is currently defined as
function (actual, predicted, gpnames = NULL,
            rowcol = c("actual", "predicted"),
            printit = c("overall","confusion"),
            prior = NULL, digits = 3) 
{
  if (is.null(gpnames)) 
    gpnames <- levels(actual)
  if (is.logical(printit)){
    if(printit)printit <- c("overall","confusion")
    else printit <- ""
  }
  tab <- table(actual, predicted)
  acctab <- t(apply(tab, 1, function(x) x/sum(x)))
  dimnames(acctab) <- list(Actual = gpnames, `Predicted (cv)` = gpnames)
  if (is.null(prior)) {
    relnum <- table(actual)
    prior <- relnum/sum(relnum)
    acc <- sum(tab[row(tab) == col(tab)])/sum(tab)
  }
  else {
    acc <- sum(prior * diag(acctab))
  }
  names(prior) <- gpnames
  if ("overall"%in%printit) {
    cat("Overall accuracy =", round(acc, digits), "\n")
    if(is.null(prior)){
      cat("This assumes the following prior frequencies:", 
          "\n")
      print(round(prior, digits))
    }
  }
  if ("confusion"%in%printit) {
    cat("\nConfusion matrix", "\n")
    print(round(acctab, digits))
  }
  invisible(list(overall=acc, confusion=acctab, prior=prior))
}

P-values from biological expression array data

Description

P-values were calculated for each of 3072 genes, for data that compared expression values between post-settlement coral larvae and pre-settlement coral larvae.

Usage

data("coralPval")

Format

The format is: num [1:3072, 1] 8.60e-01 3.35e-08 3.96e-01 2.79e-01 6.36e-01 ...

Details

t-statistics, and hence p-values, were derived from five replicate two-colour micro-array slides. Details are in a vignette that accompanies the DAAGbio package.

Source

See the ?DAAGbio::coralRG

References

Grasso, L. C.; Maindonald, J.; Rudd, S.; Hayward, D. C.; Saint, R.; Miller, D. J.; and Ball, E. E., 2008. Microarray analysis identifies candidate genes for key roles in coral development. BMC Genomics, 9:540.

Examples

## From p-values, calculate Benjamini-Hochberg false discrimination rates
fdr <- p.adjust(DAAG::coralPval, method='BH')
## Number of genes identified as differentially expressed for FDR = 0.01
sum(fdr<=0.01)

Occupation and wage profiles of British cotton workers

Description

Numbers are given in different categories of worker, in each of two investigations. The first source of information is the Board of Trade Census that was conducted on 1886. The second is a relatively informal survey conducted by US Bureau of Labor representatives in 1889, for use in official reports.

Usage

data(cottonworkers)

Format

A data frame with 14 observations on the following 3 variables.

census1886

Numbers of workers in each of 14 different categories, according to the Board of Trade wage census that was conducted in 1886

survey1889

Numbers of workers in each of 14 different categories, according to data collected in 1889 by the US Bureau of Labor, for use in a report to the US Congress and House of Representatives

avwage

Average wage, in pence, as estimated in the US Bureau of Labor survey

Details

The data in survey1889 were collected in a relatively informal manner, by approaching individuals on the street. Biases might therefore be expected.

Source

United States congress, House of Representatives, Sixth Annual Report of the Commissioner of Labor, 1890, Part III, Cost of Living (Washington D.C. 1891); idem., Seventh Annual Report of the Commissioner of Labor, 1891, Part III, Cost of Living (Washington D.C. 1892)

Return of wages in the principal textile trades of the United Kingdom, with report therein. (P.P. 1889, LXX). United Kingdom Official Publication.

References

Boot, H. M. and Maindonald, J. H. 2007. New estimates of age- and sex- specific earnings and the male-female earnings gap in the British cotton industry, 1833-1906. Economic History Review. Published online 28-Aug-2007 doi: 10.1111/j.1468-0289.2007.00398.x

Examples

data(cottonworkers)
str(cottonworkers)
plot(survey1889 ~ census1886, data=cottonworkers)
plot(I(avwage*survey1889) ~ I(avwage*census1886), data=cottonworkers)

Lifespans of UK 1st class cricketers born 1840-1960

Description

Year and birth, lifespan, etc, of British first class cricketers, born 1840-1960, whose handedness could be determined from information in the Who's who of cricketers. The status (alive=0, dead =1), and lifetime or lifespan, is for 1992.

Usage

data(cricketer)

Format

A data frame with 5960 observations on the following 8 variables.

left

a factor with levels right left

year

numeric, year of birth

life

numeric, lifetime or lifespan to 1992

dead

numeric (0 = alive (censored), 1 = dead, in 1992)

acd

numeric (0 = not accidental or not dead, 1 = accidental death)

kia

numeric (0 = not killed in action, 1 = killed in action)

inbed

numeric (0 = did not die in bed, 1 = died in bed)

cause

a factor with levels alive acd (accidental death) inbed (died in bed)

Details

Note that those 'killed in action' (mostly during World Wars I and II) form a subset of those who died by accident.

Source

John Aggleton, Martin Bland. Data were collated as described in Aggleton et al.

References

Aggleton JP, Bland JM, Kentridge RW, Neave NJ 1994. Handedness and longevity: an archival study of cricketers. British Medical Journal 309, 1681-1684.

Bailey P, Thorne P, Wynne-Thomas P. 1993. Who's Who of Cricketers. 2nd ed, London, Hamlyn.

Bland M and Altman D. 2005. Do the left-handed die young? Significance 2, 166-170.

See Also

earlycrcktr.

Examples

data(cricketer)
numLH <- xtabs(~ left+year, data=cricketer)
propLH <- prop.table(numLH, margin=2)[2,]
yr <- as.numeric(colnames(numLH))
plot(propLH ~ yr)
cricketer$lh <- unclass(cricketer$left)-1
left2.hat <- fitted(lm(lh ~ poly(year,2), data=cricketer))
ord <- order(cricketer$year)
lines(left2.hat[ord] ~ cricketer$year[ord])
library(splines)
ns3.hat <- fitted(lm(lh ~ ns(year,3), data=cricketer))
lines(ns3.hat[ord] ~ cricketer$year[ord], col="red")
require(survival)
summary(coxph(Surv(life, kia) ~ bs(year,3) +left, data=cricketer))
cricketer$notacdDead <- with(cricketer, {dead[acd==1]<-0; dead})
summary(coxph(Surv(life, notacdDead) ~ ns(year,2) +left, data=cricketer))

Comparison of cuckoo eggs with host eggs

Description

These data compare mean length, mean breadth, and egg color, between cuckoos and their hosts.

Usage

cuckoohosts

Format

A data frame with 10 observations on the following 12 variables.

clength

mean length of cuckoo eggs in given host's nest

cl.sd

standard deviation of cuckoo egg lengths

cbreadth

mean breadth of cuckoo eggs in given host's nest

cb.sd

standard deviation of cuckoo egg breadths

cnum

number of cuckoo eggs

hlength

length of host eggs

hl.sd

standard deviation of host egg lengths

hbreadth

breadth of host eggs

hb.sd

standard deviation of host egg breadths

hnum

number of host eggs

match

number of eggs where color matched

nomatch

number where color did not match

Details

Although from the same study that generated data in the data frame cuckoos, the data do not match precisely. The cuckoo egg lengths and breadths are from the tables on page 168, the host egg lengths and breadths from Appendix IV on page 176, and the color match counts from the table on page 171.

Source

Latter, O.H., 1902. The egg of cuculus canorus. an inquiry into the dimensions of the cuckoo's egg and the relation of the variations to the size of the eggs of the foster-parent, with notes on coloration, &c. Biometrika, 1:164–176.

Examples

cuckoohosts
str(cuckoohosts)
plot(cuckoohosts)
with(cuckoohosts,
     plot(c(clength,hlength),c(cbreadth,hbreadth),col=rep(1:2,c(6,6))))

Cuckoo Eggs Data

Description

Length and breadth measurements of 120 eggs lain in the nests of six different species of host bird.

Usage

cuckoos

Format

This data frame contains the following columns:

length

the egg lengths in millimeters

breadth

the egg breadths in millimeters

species

a factor with levels hedge.sparrow, meadow.pipit, pied.wagtail, robin, tree.pipit, wren

id

a numeric vector

Source

Latter, O.H. (1902). The eggs of Cuculus canorus. An Inquiry into the dimensions of the cuckoo's egg and the relation of the variations to the size of the eggs of the foster-parent, with notes on coloration, &c. Biometrika i, 164. Tippett (1931) gives summary details of the data.

References

Tippett, L.H.C. 1931: "The Methods of Statistics". Williams & Norgate, London.

Examples

 
print("Strip and Boxplots - Example 2.1.2")

attach(cuckoos)
oldpar <- par(las = 2) # labels at right angle to axis.
stripchart(length ~ species) 
boxplot(split(cuckoos$length, cuckoos$species),
         xlab="Length of egg", horizontal=TRUE)
detach(cuckoos)
par(oldpar)
pause()

print("Summaries - Example 2.2.2")
sapply(split(cuckoos$length, cuckoos$species), sd)
pause()

print("Example 4.1.4")
wren <- split(cuckoos$length, cuckoos$species)$wren
median(wren)
n <- length(wren)
sqrt(pi/2)*sd(wren)/sqrt(n)  # this s.e. computation assumes normality

Write an ASCII data file to the working directory.

Description

Invoking this function writes one or more nominated files to the working directory. In particular, it may be used to write the files 'fuel.txt' and 'fuel.csv' that are used in Chapter 1 of DAAGUR, and the files 'oneBadRow.txt' and 'scan-demo.txt' that are used in Chapter 14 of DAAGUR.

Usage

datafile(file = c("fuel", "travelbooks"), datastore =
                 DAAG::DAAGxdb, altstore = DAAG::zzDAAGxdb, showNames =
                 FALSE)

Arguments

Value

An ASCII file is output to the current working directory. The names of all available datasets are returned invisibly.

Author(s)

J.H. Maindonald

Examples

datafile(file="", showNames=TRUE)

Dengue prevalence, by administrative region

Description

Data record, for each of 2000 administrative regions, whether or not dengue was recorded at any time between 1961 and 1990.

Usage

data(dengue)

Format

A data frame with 2000 observations on the following 13 variables.

humid

Average vapour density: 1961-1990

humid90

90th percentile of humid

temp

Average temperature: 1961-1990

temp90

90th percentile of temp

h10pix

maximum of humid, within a 10 pixel radius

h10pix90

maximum of humid90, within a 10 pixel radius

trees

Percent tree cover, from satellite data

trees90

90th percentile of trees

NoYes

Was dengue observed? (1=yes)

Xmin

minimum longitude

Xmax

maximum longitude

Ymin

minimum latitude

Ymax

maximum latitude

Details

This is derived from a data set in which the climate and tree cover information were given for each half degree of latitude by half degreee of longitude pixel. The variable NoYes was given by administrative region. The climate data and tree cover data given here are 50th or 90th percentiles, where percetiles were calculates across pixels for an administrative region.

Source

Simon Hales, Environmental Research New Zealand Ltd.

References

Hales, S., de Wet, N., Maindonald, J. and Woodward, A. 2002. Potential effect of population and climate change global distribution of dengue fever: an empirical model. The Lancet 2002; 360: 830-34.

Examples

str(dengue)
glm(NoYes ~ humid, data=dengue, family=binomial)
glm(NoYes ~ humid90, data=dengue, family=binomial)

Dewpoint Data

Description

The dewpoint data frame has 72 rows and 3 columns. Monthly data were obtained for a number of sites (in Australia) and a number of months.

Usage

dewpoint

Format

This data frame contains the following columns:

maxtemp

monthly minimum temperatures

mintemp

monthly maximum temperatures

dewpt

monthly average dewpoint for each combination of minimum and maximum temperature readings (formerly dewpoint)

Source

Dr Edward Linacre, visiting fellow in the Australian National University Department of Geography.

Examples

print("Additive Model - Example 7.5")
require(splines)
attach(dewpoint)   
ds.lm <- lm(dewpt ~ bs(maxtemp,5) + bs(mintemp,5), data=dewpoint)
ds.fit <-predict(ds.lm, type="terms", se=TRUE)
oldpar <- par(mfrow=c(1,2))
plot(maxtemp, ds.fit$fit[,1], xlab="Maximum temperature",
     ylab="Change from dewpoint mean",type="n")
lines(maxtemp,ds.fit$fit[,1])
lines(maxtemp,ds.fit$fit[,1]-2*ds.fit$se[,1],lty=2)
lines(maxtemp,ds.fit$fit[,1]+2*ds.fit$se[,1],lty=2)
plot(mintemp,ds.fit$fit[,2],xlab="Minimum temperature",
     ylab="Change from dewpoint mean",type="n")
ord<-order(mintemp)
lines(mintemp[ord],ds.fit$fit[ord,2])
lines(mintemp[ord],ds.fit$fit[ord,2]-2*ds.fit$se[ord,2],lty=2)
lines(mintemp[ord],ds.fit$fit[ord,2]+2*ds.fit$se[ord,2],lty=2)
detach(dewpoint)
par(oldpar)

Periods Between Rain Events

Description

Data collected at Winnipeg International Airport (Canada) on periods (in days) between rain events.

Usage

droughts

Format

This data frame contains the following columns:

length

the length of time from the completion of the last rain event to the beginning of the next rain event.

year

the calendar year.

Examples

  boxplot(length ~ year, data=droughts)
  boxplot(log(length) ~ year, data=droughts)
  hist(droughts$length, main="Winnipeg Droughts", xlab="length (in days)")
  hist(log(droughts$length), main="Winnipeg Droughts", xlab="length (in days, log scale)")

EPICA Dome C Ice Core 800KYr Carbon Dioxide Data

Description

Carbon dioxide record from the EPICA (European Project for Ice Coring in Antarctica) Dome C ice core covering 0 to 800 kyr BP.

Usage

data(edcCO2)

Format

A data frame with 1096 observations on the following 2 variables.

age

Age in years before present (BP)

co2

CO2 level (ppmv)

Details

Data are a composite series.

Source

Go to the url https://www.ncei.noaa.gov/products/paleoclimatology/ice-core/

References

Luthi, D., M. et al. 2008. High-resolution carbon dioxide concentration record 650,000-800,000 years before present. Nature, Vol. 453, pp. 379-382, 15 May 2008. doi:10.1038/nature06949

Indermuhle, A., E. et al, 1999, Atmospheric CO2 concentration from 60 to 20 kyr BP from the Taylor Dome ice core, Antarctica. Geophysical Research Letters, 27, 735-738.

Monnin, E., A. et al. 2001. Atmospheric CO2 concentrations over the last glacial termination. Science, Vol. 291, pp. 112-114.

Petit, J.R. et al. 1999. Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica. Nature 399: 429-436.

Siegenthaler, U. et al. 2005. Stable Carbon Cycle-Climate Relationship During the Late Pleistocene. Science, v. 310 , pp. 1313-1317, 25 November 2005.

Examples

data(edcCO2)

EPICA Dome C Ice Core 800KYr Temperature Estimates

Description

Temperature record, using Deuterium as a proxy, from the EPICA (European Project for Ice Coring in Antarctica) Dome C ice core covering 0 to 800 kyr BP.

Usage

data(edcT)

Format

A data frame with 5788 observations on the following 5 variables.

Bag

Bag number

ztop

Top depth (m)

Age

Years before 1950

Deuterium

Deuterium dD data

dT

Temperature difference from the average of the last 1000 years ~ -54.5degC

Details

Temperature was estimated from the deuterium data, after making various corrections.

Source

Go to the url https://www.ncei.noaa.gov/products/paleoclimatology/ice-core/

References

Jouzel, J., et al. 2007. EPICA Dome C Ice Core 800KYr Deuterium Data and Temperature Estimates. IGBP PAGES/World Data Center for Paleoclimatology Data Contribution Series # 2007-091. NOAA/NCDC Paleoclimatology Program, Boulder CO, USA.

Jouzel, J., et al. 2007. Orbital and Millennial Antarctic Climate Variability over the Past 800,000 Years. Science, Vol. 317, No. 5839, pp.793-797, 10 August 2007.

Examples

 data(edcT) 

Elastic Band Data Replicated

Description

Both datasets give, for each amount by which an elastic band is stretched over the end of a ruler, the distance that the band traveled when released. The elastic1 data frame has 7 rows. The elastic2 data frame, whose data span a wider range of stretches and distances, has 9 rows.

Usage

  data(elastic1)
  data(elastic2)
  

Format

These data frames contains the following columns:

stretch

the amount by which the elastic band was stretched

distance

the distance traveled

Source

J. H. Maindonald

Examples

plot(elastic1)
sapply(elastic1, mean)
pause()
sapply(elastic1, function(x)mean(x))
pause()
sapply(elastic1, function(x)sum(log(x)))
pause()
yrange <- range(c(elastic1$distance, elastic2$distance))
xrange <- range(c(elastic1$stretch, elastic2$stretch))
plot(distance ~ stretch, data = elastic1, pch = 16, ylim = yrange, xlim = 
xrange)
points(distance ~ stretch, data = elastic2, pch = 15, col = 2)
legend(xrange[1], yrange[2], legend = c("Data set 1", "Data set 2"), pch = 
c(16, 15), col = c(1, 2))
elastic1.lm <- lm(distance ~ stretch, data = elastic1)
elastic2.lm <- lm(distance ~ stretch, data = elastic2)
abline(elastic1.lm)
abline(elastic2.lm, col = 2)
summary(elastic1.lm)
summary(elastic2.lm)
pause()
predict(elastic1.lm, se.fit=TRUE)
predict(elastic2.lm, se.fit=TRUE)

Elastic Band Data

Description

The elasticband data frame has 7 rows and 2 columns giving, for each amount by which an elastic band is stretched over the end of a ruler, the distance that the band traveled when released.

Usage

elasticband

Format

This data frame contains the following columns:

stretch

the amount by which the elastic band was stretched

distance

the distance traveled

Source

J. H. Maindonald

Examples

print("Example 1.8.1")

attach(elasticband)     # R now knows where to find stretch and distance
plot(stretch, distance) # Alternative: plot(distance ~ stretch)
detach(elasticband)

print("Lists - Example 12.7")

elastic.lm <- lm(distance ~ stretch, data=elasticband)
 names(elastic.lm)
 elastic.lm$coefficients
elastic.lm[["coefficients"]]
pause()

elastic.lm[[1]]
pause()

elastic.lm[1]
pause()

options(digits=3)
elastic.lm$residuals 
pause()

elastic.lm$call
pause()

 mode(elastic.lm$call)

Simulation of classical errors in x model, with multiple explanatory variables.

Description

Simulates $y-$ and $x-$values for a classical “errors in $x$” linear regression model. One or more $x-$values are subject to random measurement error, independently of the corresponding covariate values that are measured without error.

Usage

errorsINseveral(n = 1000, a0 = 2.5, beta = c(1.5, 0), mu = 12.5, SDyerr = 0.5,
default.Vpar = list(SDx = 2, rho = -0.5, timesSDx = 1.5),
V = with(default.Vpar, matrix(c(1, rho, rho, 1), ncol = 2) * SDx^2),
xerrV = with(default.Vpar, matrix(c(1, 0, 0, 0), ncol = 2) * (SDx * timesSDx)^2),
parset = NULL, print.summary = TRUE, plotit = TRUE)

Arguments

Details

With default arguments, simulates a model in which two covariates are in contention, the first measured without error, and the second with coefficient 0 in the model that includes both covariates measured without error.

Value

Author(s)

John Maindonald

References

Data Analysis and Graphics Using R, 3rd edn, Section 6.8.1

See Also

errorsINx

Examples

library(lattice)
function(n=1000, a0=2.5, beta=c(1.5,0), mu=12.5, SDyerr=0.5, 
           default.Vpar=list(SDx=2, rho=-0.5, timesSDx=1.5),
           V=with(default.Vpar, matrix(c(1,rho,rho,1), ncol=2)*SDx^2),
           xerrV=with(default.Vpar, matrix(c(1,0,0,0), ncol=2)*(SDx*timesSDx)^2),
           parset=NULL, print.summary=TRUE, plotit=TRUE){
    m <- dim(V)[1]
    if(length(mu)==1)mu <- rep(mu,m)
    ow <- options(warn=-1)
    xxmat <- sweep(matrix(rnorm(m*n, 0, 1), ncol=m) %*% chol(V), 2, mu, "+")
    errxx <- matrix(rnorm(m*n, 0, 1), ncol=m) %*% chol(xerrV, pivot=TRUE)
    options(ow)
    dimnames(xxmat)[[2]] <- paste("z", 1:m, sep="")
    xxWITHerr <- xxmat+errxx
    xxWITHerr <- data.frame(xxWITHerr)
    names(xxWITHerr) <- paste("xWITHerr", 1:m, sep="")
    xxWITHerr[, "y"] <- a0 + xxmat %*% matrix(beta,ncol=1) + rnorm(n, sd=SDyerr)
    err.lm <- lm(y ~ ., data=xxWITHerr)
    xx <- data.frame(xxmat)
    names(xx) <- paste("z", 1:m, sep="")
    xx$y <- xxWITHerr$y
    xx.lm <- lm(y ~ ., data=xx)
    B <- coef(err.lm)
    b <- coef(xx.lm)
    SE <- summary(err.lm)$coef[,2]
    se <- summary(xx.lm)$coef[,2]
    if(print.summary){
      beta0 <- c(mean(xx$y)-sum(beta*apply(xx[,1:m],2,mean)), beta)
      tab <- rbind(beta0, b, B)
      dimnames(tab) <- list(c("Values for simulation",
                              "Estimates: no error in x1",
                              "LS Estimates: error in x1"),
                            c("Intercept", paste("b", 1:m, sep="")))
      tabSE <- rbind(rep(NA,m+1),se,SE)
      rownames(tabSE) <- rownames(tab)
      colnames(tabSE) <- c("SE(Int)", paste("SE(", colnames(tab)[-1],")", sep=""))
      tab <- cbind(tab,tabSE)
      print(round(tab,3))
    }
    if(m==2 & print.summary){
      tau <- default.Vpar$timesSDx
      s1 <- sqrt(V[1,1])
      s2 <- sqrt(V[2,2])
      rho <- default.Vpar$rho
      s12 <- s1*sqrt(1-rho^2)
      lambda <- (1-rho^2)/(1-rho^2+tau^2)
      gam12 <- rho*sqrt(V[1,1]/V[2,2])
      expB2 <- beta[2]+beta[1]*(1-lambda)*gam12
      print(c("Theoretical attenuation of b1" = lambda, "Theoretical b2" = expB2))
    }
    if(is.null(parset))parset <- simpleTheme(col=c("gray40","gray40"),
                                             col.line=c("black","black"))
    if(plotit){
      library(lattice)
      zhat <- fitted(xx.lm)
      xhat <- fitted(err.lm)
      plt <- xyplot(xhat ~ zhat, aspect=1, scales=list(tck=0.5),
                    panel=function(x,y,...){
                      panel.xyplot(x,y,type="p",...)
                      panel.abline(lm(y ~ x), lty=2)
                      panel.abline(0,1)
                    },
                    xlab="Fitted values; regress on exact z",
                    ylab="Fitted values; regress on x = xWITHerr",
                    key=list(space="top", columns=2,
                      text=list(lab=c("Line y=x", "Regression fit to points")),
                      lines=list(lty=1:2)),
                    par.settings=parset
                    )
      print(plt)}
    invisible(list(ERRfree=xx, addedERR=xxWITHerr))
  }

Simulate data for straight line regression, with "errors in x".

Description

Simulates $y-$ and $x-$values for the straight line regression model, but with $x-$values subject to random measurement error, following the classical “errors in x” model. Optionally, the x-values can be split into two groups, with one group shifted relative to the other

Usage

errorsINx(mu = 12.5, n = 200, a = 15, b = 1.5, SDx=2, SDyerr = 1.5,
           timesSDx=(1:5)/2.5, gpfactor=if(missing(gpdiff))FALSE else TRUE,
           gpdiff=if(gpfactor) 1.5 else 0, layout=NULL,
           parset = simpleTheme(alpha = 0.75, col = c("black","gray45"),
             col.line = c("black","gray45"), lwd=c(1,1.5), pch=c(1,2),
           lty=c(1,2)), print.summary=TRUE, plotit=TRUE, xrelation="same")

Arguments

Details

The argument timesSDx can be a numeric vector. One set of $x$-values that are contaminated with measurement error is simulated for each element of timesSDx.

Value

Author(s)

John Maindonald

References

Data Analysis and Graphics Using R, 3rd edn, Section 6.7

Examples

library(lattice)
errorsINx()
errorsINx(gpdiff=2, timesSDx=1.25, SDyerr=2.5, n=80)

Create and analyze multiway frequency or weighted frequency table

Description

This function creates a multi-way table of counts for the response given a set of classifying factors. Output facilitates a check on how the factor specified as margin may, after accounting for other classifying factors, affect the response.

Usage

excessRisk(form = weight ~ seatbelt + airbag, response = "dead", margin = "airbag",
data = DAAG::nassCDS, decpl = 4, printResults = TRUE)

Arguments

Details

The best way to understand what this function does may be to run it with the default parameters, and/or with examples that appear below.

Value

The function returns a data frame, with one row for each combination of levels of factors on the right of the formula, but excluding the factor specified as margin. The final three columns show the count for level 1 as a fraction of the margin by total, the count for level 2 as a fraction of the margin by total, and the excess count for level 2 of response in the row, under the assumption that, in that row, there is no association between response and margin. This is the observed response (for the default arguments, number of dead) for level 2 (airbag deployed), less the number that would have been expected if the proportion had been that for level 1. (Negative values favor airbags.)

Author(s)

John Maindonald

References

See help(nassCDS)

See Also

xtabs

Examples

excessRisk()
excessRisk(weight ~ airbag+seatbelt+dvcat)
UCB <- as.data.frame.table(UCBAdmissions)
excessRisk(Freq~Gender, response="Admit", margin="Gender",data=UCB)
excessRisk(Freq~Gender+Dept, response="Admit", margin="Gender",data=UCB)

Fossil Fuel Data

Description

Estimates of total worldwide carbon emissions from fossil fuel use.

Usage

fossilfuel

Format

This data frame contains the following columns:

year

a numeric vector giving the year the measurement was taken.

carbon

a numeric vector giving the total worldwide carbon emissions from fossil fuel use, in millions of tonnes.

Details

Data for the years 1751 through to 2014 is available from Data for the years 2014 https://data.ess-dive.lbl.gov/portals/CDIAC

Source

Boden T A; Marland G; Andres R J (1999): Global, Regional, and National Fossil-Fuel CO2 Emissions (1751 - 2014) (V. 2017). Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States), ESS-DIVE repository. Dataset. doi:10.3334/CDIAC/00001_V2017

Examples

plot(fossilfuel)

Frogs Data

Description

The frogs data frame has 212 rows and 11 columns. The data are on the distribution of the Southern Corroboree frog, which occurs in the Snowy Mountains area of New South Wales, Australia.

Usage

frogs

Format

This data frame contains the following columns:

pres.abs

0 = frogs were absent, 1 = frogs were present

northing

reference point

easting

reference point

altitude

altitude , in meters

distance

distance in meters to nearest extant population

NoOfPools

number of potential breeding pools

NoOfSites

(number of potential breeding sites within a 2 km radius

avrain

mean rainfall for Spring period

meanmin

mean minimum Spring temperature

meanmax

mean maximum Spring temperature

Source

Hunter, D. (2000) The conservation and demography of the southern corroboree frog (Pseudophryne corroboree). M.Sc. thesis, University of Canberra, Canberra.

Examples

print("Multiple Logistic Regression - Example 8.2")

plot(northing ~ easting, data=frogs, pch=c(1,16)[frogs$pres.abs+1],
  xlab="Meters east of reference point", ylab="Meters north")
pairs(frogs[,4:10])
attach(frogs)
pairs(cbind(altitude,log(distance),log(NoOfPools),NoOfSites),
  panel=panel.smooth, labels=c("altitude","log(distance)",
  "log(NoOfPools)","NoOfSites"))
detach(frogs)

frogs.glm0 <- glm(formula = pres.abs ~ altitude + log(distance) +
  log(NoOfPools) + NoOfSites + avrain + meanmin + meanmax,
  family = binomial, data = frogs)
summary(frogs.glm0)

frogs.glm <- glm(formula = pres.abs ~ log(distance) + log(NoOfPools) + 
meanmin +
  meanmax, family = binomial, data = frogs)
oldpar <- par(mfrow=c(2,2))
termplot(frogs.glm, data=frogs)

termplot(frogs.glm, data=frogs, partial.resid=TRUE)

cv.binary(frogs.glm0)   # All explanatory variables
pause()

cv.binary(frogs.glm)    # Reduced set of explanatory variables

for (j in 1:4){
 rand <- sample(1:10, 212, replace=TRUE)
 all.acc <- cv.binary(frogs.glm0, rand=rand, print.details=FALSE)$acc.cv
 reduced.acc <- cv.binary(frogs.glm, rand=rand, print.details=FALSE)$acc.cv
 cat("\nAll:", round(all.acc,3), "  Reduced:", round(reduced.acc,3))
}

Frosted Flakes data

Description

The frosted flakes data frame has 100 rows and 2 columns giving the sugar concentration (in percent) for 25 g samples of a cereal as measured by 2 methods – high performance liquid chromatography (a slow accurate lab method) and a quick method using the infra-analyzer 400.

Usage

frostedflakes

Format

This data frame contains the following columns:

Lab

careful laboratory analysis measurements using high performance liquid chromatography

IA400

measurements based on the infra-analyzer 400

Source

W. J. Braun

Electrical Resistance of Kiwi Fruit

Description

Data are from a study that examined how the electrical resistance of a slab of kiwifruit changed with the apparent juice content.

Usage

fruitohms

Format

This data frame contains the following columns:

juice

apparent juice content (percent)

ohms

electrical resistance (in ohms)

Source

Harker, F. R. and Maindonald J.H. 1994. Ripening of nectarine fruit. Plant Physiology 106: 165 - 171.

Examples

plot(ohms ~ juice, xlab="Apparent juice content (%)",ylab="Resistance (ohms)", data=fruitohms)
lines(lowess(fruitohms$juice, fruitohms$ohms), lwd=2)
pause()

require(splines)
attach(fruitohms)
plot(ohms ~ juice, cex=0.8, xlab="Apparent juice content (%)",
     ylab="Resistance (ohms)", type="n")
fruit.lmb4 <- lm(ohms ~ bs(juice,4))
ord <- order(juice)
lines(juice[ord], fitted(fruit.lmb4)[ord], lwd=2)
ci <- predict(fruit.lmb4, interval="confidence")
lines(juice[ord], ci[ord,"lwr"])
lines(juice[ord], ci[ord,"upr"])

Effect of pentazocine on post-operative pain (average VAS scores)

Description

The table shows, separately for males and females, the effect of pentazocine on post-operative pain profiles (average VAS scores), with (mbac and fbac) and without (mpl and fpl) preoperatively administered baclofen. Pain scores are recorded every 20 minutes, from 10 minutes to 170 minutes.

Usage

gaba

Format

A data frame with 9 observations on the following 7 variables.

min

a numeric vector

mbac

a numeric vector

mpl

a numeric vector

fbac

a numeric vector

fpl

a numeric vector

avbac

a numeric vector

avplac

a numeric vector

Details

15 females were given baclofen, as against 3 males. 7 females received the placebo, as against 16 males. Averages for the two treatments (baclofen/placebo), taken over all trial participants and ignoring sex, are misleading.

Source

Gordon, N. C. et al.(1995): 'Enhancement of Morphine Analgesia by the GABA_B against Baclofen'. Neuroscience 69: 345-349.

Examples

data(gaba)
mr <- range(gaba$min)
tran <- range(gaba[, c("mbac","mpl","fbac","fpl")])
## Means by treatment and sex
par(mfrow=c(1,2))
plot(mr, tran, xlab = "Time post pentazocine (min)",
     ylab = "Reduction in VAS pain rating", 
     type = "n", xlim = c(0, 170), ylim = tran)
points(gaba$min, gaba$fbac, pch = 1, col = 8, lwd = 2, lty = 2, 
       type = "b")
points(gaba$min, gaba$fpl, pch = 0, col = 8, lwd = 2, lty = 2, 
       type = "b")
points(gaba$min, gaba$mbac, pch = 16, col = 8, lty = 2, type = "b")
points(gaba$min, gaba$mpl, pch = 15, col = 8, lty = 2, type = "b")
box()
## Now plot means, by treatment, averaged over all participants
plot(mr, tran, xlab = "Time post pentazocine (min)",
     ylab = "Reduction in VAS pain rating", 
     type = "n", xlim = c(0, 170), ylim = tran)
bac <- (15 * gaba$fbac + 3 * gaba$mbac)/18
plac <- (7 * gaba$fpl + 9 * gaba$mpl)/16
points(gaba$min, plac, pch = 15, lty = 1, col=1, type = "b")
points(gaba$min, bac, pch = 16, lty = 1, col=1, type = "b")
box()
par(mfrow=c(1,1))

Seismic Timing Data

Description

The geophones data frame has 56 rows and 2 columns. Thickness of a layer of Alberta substratum as measured by a line of geophones.

Usage

geophones

Format

This data frame contains the following columns:

distance

location of geophone.

thickness

time for signal to pass through substratum.

Examples

plot(geophones)
lines(lowess(geophones, f=.25))

Yearly averages of Great Lake heights: 1918 - 2009

Description

Heights, stored as a multivariate time series, are for the lakes Erie, Michigan/Huron, Ontario and St Clair

Usage

data(greatLakes)

Format

The format is: mts [1:92, 1:4] 174 174 174 174 174 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:4] "Erie" "michHuron" "Ontario" "StClair" - attr(*, "tsp")= num [1:3] 1918 2009 1 - attr(*, "class")= chr [1:2] "mts" "ts"

Details

For more details, go to the website that is the source of the data.

Source

https://www.lre.usace.army.mil/Missions/Great-Lakes-Information/Great-Lakes-Information-2/Water-Level-Data/

Examples

data(greatLakes)
plot(greatLakes)
## maybe str(greatLakes)

Alcohol consumption in Australia and New Zealand

Description

Data are annual apparent alcohol consumption in Australia and New Zealand, in liters of pure alcohol content per annum, separately for beer, wine, and spirits (including spirit-based products).

Usage

data(grog)

Format

A data frame with 18 observations on the following 5 variables.

Beer

liters per annum

Wine

liters per annum

Spirit

liters per annum

Country

a factor with levels Australia NewZealand

Year

Year ending in June of the given year

Details

Data are total available pure alcohol content, for the three categories, divided by numbers of persons aged 15 years or more. The source data for New Zealand included quarterly figures from December 1997, and annual data to December for all years. The annual New Zealand figure to June 1998 required an estimate for September 1997 that was obtained by extrapolating back the third quarter trend line from later years.

Source

Australian data are from https://www.abs.gov.au. For the most recent data, go to https://www.abs.gov.au/statistics/health/health-conditions-and-risks/apparent-consumption-alcohol-australia For New Zealand data, go to https://infoshare.stats.govt.nz/ Click on 'Industry sectors' and then on 'Alcohol Available for Consumption - ALC'.

Examples

data(grog)
library(lattice)
xyplot(Beer+Wine+Spirit ~ Year | Country, data=grog)
xyplot(Beer+Wine+Spirit ~ Year, groups=Country, data=grog, outer=TRUE)

Graphical Output for Hardcopy

Description

This function streamlines graphical output to the screen, pdf or ps files. File names for hard copy devices can be generated automatically from function names of the form g3.2 or fig3.2 (the choice of alphabetic characters prior to 3.2 is immaterial).

Usage

hardcopy(width = 3.75, height = 3.75, color = FALSE, trellis = FALSE,
                 device = c("", "pdf", "ps"), path = getwd(), file =
                 NULL, format = c("nn-nn", "name"), split = "\\.",
                 pointsize = c(8, 4), fonts=NULL, horiz = FALSE, ...)

Arguments

Details

If a file name (file, without extension) is not supplied, the format argument determines how the name is constructed. With format="name", the function name is used. With format="nn-nn" and dotsplit unchanged from the default, a function name of the form g3.1 leads to the name 03-01. Here g can be replaced by any other non-numeric characters; the result is the same. The relevant extension is in any case added.

Value

Graphical output to screen, pdf or ps file.

Author(s)

J.H. Maindonald

See Also

postscript

Minor Head Injury (Simulated) Data

Description

The headInjury data frame has 3121 rows and 11 columns. The data were simulated according to a simple logistic regression model to match roughly the clinical characteristics of a sample of individuals who suffered minor head injuries.

Usage

headInjury

Format

This data frame contains the following columns:

age.65

age factor (0 = under 65, 1 = over 65).

amnesia.before

amnesia before impact (less than 30 minutes = 0, more than 30 minutes =1).

basal.skull.fracture

(0 = no fracture, 1 = fracture).

GCS.decrease

Glasgow Coma Scale decrease (0 = no deterioration, 1 = deterioration).

GCS.13

initial Glasgow Coma Scale (0 = not ‘13’, 1 = ‘13’).

GCS.15.2hours

Glasgow Coma Scale after 2 hours (0 = not ‘15’, 1 = '15').

high.risk

assessed by clinician as high risk for neurological intervention (0 = not high risk, 1 = high risk).

loss.of.consciousness

(0 = conscious, 1 = loss of consciousness).

open.skull.fracture

(0 = no fracture, 1 = fracture)

vomiting

(0 = no vomiting, 1 = vomiting)

clinically.important.brain.injury

any acute brain finding revealed on CT (0 = not present, 1 = present).

References

Stiell, I.G., Wells, G.A., Vandemheen, K., Clement, C., Lesiuk, H., Laupacis, A., McKnight, R.D., Verbee, R., Brison, R., Cass, D., Eisenhauer, M., Greenberg, G.H., and Worthington, J. (2001) The Canadian CT Head Rule for Patients with Minor Head Injury, The Lancet. 357: 1391-1396.

Scottish Hill Races Data

Description

The record times in 1984 (hills) for 35 Scottish hill races, or in 2000 (hills2000) for 56 hill races. The hills2000 dataset is the subset of races2000 for which type is hill.

Usage

  data(hills)
  data(hills2000)
  

Format

dist

distance, in miles (on the map)

climb

total height gained during the route, in feet

time

record time in hours

timef

record time in hours for females, in the hills2000 dataset.

Source

A.C. Atkinson (1986) Comment: Aspects of diagnostic regression analysis. Statistical Science 1, 397-402.

Also, in MASS library, with time in minutes.

The Scottish Running Resource, http://www.hillrunning.co.uk

References

A.C. Atkinson (1988) Transformations unmasked. Technometrics 30, 311-318. [ "corrects" the time for Knock Hill, in the hills dataset, from 78.65 to 18.65. It is unclear if this based on the original records.]

Examples

print("Transformation - Example 6.4.3")
pairs(hills, labels=c("dist\n\n(miles)", "climb\n\n(feet)", 
"time\n\n(hours)"))
pause()

pairs(log(hills), labels=c("dist\n\n(log(miles))", "climb\n\n(log(feet))",
  "time\n\n(log(hours))"))
pause()

hills0.loglm <- lm(log(time) ~ log(dist) + log(climb), data = hills)  
oldpar <- par(mfrow=c(2,2))
plot(hills0.loglm)
pause()


hills.loglm <- lm(log(time) ~ log(dist) + log(climb), data = hills[-18,])
summary(hills.loglm) 
plot(hills.loglm)
pause()

hills2.loglm <- lm(log(time) ~ log(dist)+log(climb)+log(dist):log(climb), 
data=hills[-18,])
anova(hills.loglm, hills2.loglm)
pause()

step(hills2.loglm)
pause()

summary(hills.loglm, corr=TRUE)$coef
pause()

summary(hills2.loglm, corr=TRUE)$coef
par(oldpar)
pause()

print("Nonlinear - Example 6.9.4")
hills.nls0 <- nls(time ~ (dist^alpha)*(climb^beta), start =
   c(alpha = .909, beta = .260), data = hills[-18,])
summary(hills.nls0)
plot(residuals(hills.nls0) ~ predict(hills.nls0)) # residual plot
pause()

hills$climb.mi <- hills$climb/5280
hills.nls <- nls(time ~ alpha + beta*dist + gamma*(climb.mi^delta),
  start=c(alpha = 1, beta = 1, gamma = 1, delta = 1), data=hills[-18,])
summary(hills.nls)
plot(residuals(hills.nls) ~ predict(hills.nls)) # residual plot

Hawaian island chain hotspot Potassium-Argon ages

Description

K-Ar Ages (millions of years) and distances (km) from Kilauea along the trend of the chain of Hawaian volcanic islands and other seamounts that are believed to have been created by a moving "hot spot". The age of Kilauea is given as 0-0.4 Ma.

Usage

data(hotspots)

Format

A data frame with 36 observations on the following 6 variables.

ID

Volcano identifier

name

Name

distance

Distance in kilometers

age

K-Ar age in millions of years

error

Standard error of estimate?

source

Data source; see information on web site below.

Details

For details of the way that errors werre calculated, refer to the original papers. See also the comments under hotspots2006. In general, errors do not account for geological uncertainty.

Source

http://www.soest.hawaii.edu/GG/HCV/haw_formation.html

Examples

data(hotspots)
plot(age ~ distance, data=hotspots)
abline(lm(age ~ distance, data=hotspots))

Hawaian island chain hotspot Argon-Argon ages

Description

Ar-Ar Ages (millions of years) and distances (km) from Kilauea along the trend of the chain of Hawaian volcanic islands and other seamounts that are believed to have been created by a moving "hot spot".

Usage

data(hotspots2006)

Format

A data frame with 10 observations on the following 6 variables.

age

Ar-Ar age

CI95lim

Measurement error; 95% CI

geoErr

Geological Uncertainty

totplus

Total uncertainty (+)

totminus

Total uncertainty (-)

distance

Distance in kilometers

Details

Note that measurement error is small relative to geological uncertainty. Geological uncertainty arises because lavas are likely to have erupted, over a period of up to 2 million years, somewhat after passage over the hot spot's centre. Dredging or drilling will in general have accessed larvas from the younger half of this interval. Hence the asymmetry in the geological uncertainty.

Source

Warren D. Sharp and David A. Clague, 50-Ma initiation of Hawaiian-Emperor bend records major change in Pacific Plate motion. Science 313: 1281-1284 (2006).

Examples

data(hotspots2006)

Aranda House Prices

Description

The houseprices data frame consists of the floor area, price, and the number of bedrooms for a sample of houses sold in Aranda in 1999. Aranda is a suburb of Canberra, Australia.

Usage

houseprices

Format

This data frame contains the following columns:

area

a numeric vector giving the floor area

bedrooms

a numeric vector giving the number of bedrooms

sale.price

a numeric vector giving the sale price in thousands of Australian dollars

Source

J.H. Maindonald

Examples

plot(sale.price~area, data=houseprices)
pause()

coplot(sale.price~area|bedrooms, data=houseprices)
pause()

print("Cross-Validation - Example 5.5.2")

houseprices.lm <- lm(sale.price ~ area, data=houseprices)
summary(houseprices.lm)$sigma^2
pause()

CVlm()
pause()

print("Bootstrapping - Example 5.5.3")
houseprices.fn <- function (houseprices, index){
house.resample <- houseprices[index,]
house.lm <- lm(sale.price ~ area, data=house.resample)
coef(house.lm)[2]    # slope estimate for resampled data
}
require(boot)       # ensure that the boot package is loaded
houseprices.boot <- boot(houseprices, R=999, statistic=houseprices.fn)

houseprices1.fn <- function (houseprices, index){
house.resample <- houseprices[index,]
house.lm <- lm(sale.price ~ area, data=house.resample)
predict(house.lm, newdata=data.frame(area=1200))
}

houseprices1.boot <- boot(houseprices, R=999, statistic=houseprices1.fn)
boot.ci(houseprices1.boot, type="perc") # "basic" is an alternative to "perc"
houseprices2.fn <- function (houseprices, index){
house.resample <- houseprices[index,]
house.lm <- lm(sale.price ~ area, data=house.resample)
houseprices$sale.price-predict(house.lm, houseprices)  # resampled prediction errors
}

n <- length(houseprices$area)
R <- 200   
houseprices2.boot <- boot(houseprices, R=R, statistic=houseprices2.fn)
house.fac <- factor(rep(1:n, rep(R, n)))
plot(house.fac, as.vector(houseprices2.boot$t), ylab="Prediction Errors", 
xlab="House")
pause()

plot(apply(houseprices2.boot$t,2, sd)/predict.lm(houseprices.lm, se.fit=TRUE)$se.fit,
     ylab="Ratio of Bootstrap SE's to Model-Based SE's", xlab="House", pch=16)
abline(1,0)

Oxygen uptake versus mechanical power, for humans

Description

Data are from Daedalus project; see the reference below.

Usage

data(humanpower1)

Format

A data frame with 28 observations on the following 3 variables.

wattsPerKg

a numeric vector: watts per kilogram of body weight

o2

a numeric vector: ml/min/kg

id

a factor with levels 1 - 5 (humanpower1) or 1 - 4 (humanpower2), identifying the different athletes

Details

Data in humanpower1 are from investigations (Bussolari 1987) designed to assess the feasibility of a proposed 119 kilometer human powered flight from the island of Crete – in the initial phase of the Daedalus project. Data are for five athletes – a female hockey player, a male amateur tri-athlete, a female amateur triathlete, a male wrestler and a male cyclist – who were selected from volunteers who were recruited through the news media, Data in humanpower2) are for four out of the 25 applicants who were selected for further testing, in the lead-up to the eventual selection of a pilot for the Daedalus project (Nadel and Bussolari 1988).

Source

Bussolari, S.R.(1987). Human factors of long-distance human-powered aircraft flights. Human Power 5: 8-12.

Nadel and Bussolari, S.R.(1988). The Daedalus project: physiological problems and solutions. American Scientist 76: 351-360.

References

Nadel and Bussolari, S.R.(1989). The physiological limits of long-duration human-power production – lessons learned from the Daedalus project. Human Power 7: 7-10.

Examples

str(humanpower1)
plot(humanpower1)
lm(o2 ~ id + wattsPerKg:id, data=humanpower1)
lm(o2 ~ id + wattsPerKg:id, data=humanpower2)

Named US Atlantic Hurricanes

Description

Details are given of atmospheric pressure at landfall, estimated damage in millions of dollars, and deaths, for named hurricanes that made landfall in the US mainland from 1950 through to 2012.

Usage

data("hurricNamed")

Format

A data frame with 94 observations on the following 11 variables.

Name

Hurricane name

Year

Numeric

LF.WindsMPH

Maximum sustained windspeed (>= 1 minute) to occur along the US coast. Prior to 1980, this is estimated from the maximum windspeed associated with the Saffir-Simpson index at landfall. If 2 or more landfalls, the maximum is taken

LF.PressureMB

Atmospheric pressure at landfall in millibars. If 2 or more landfalls, the minimum is taken

LF.times

Date of first landfall

BaseDam2014

Property damage (millions of 2014 US dollars)

BaseDamage

Property damage (in millions of dollars for that year)

NDAM2014

Damage, had hurricane appeared in 2014

AffectedStates

Affected states (2-digit abbreviations), pasted together

firstLF

Date of first landfall

deaths

Number of continental US direct and indirect deaths

mf

Gender of name; a factor with levels f m

Details

An earlier version of these data was the subject of a controversial paper that claimed to have found that hurricanes with female names, presumably because taken less seriously, did more human damage after adjusting for the severity of the storm than those with male names.

Source

https://www.icat.com/storms/catastrophe-resources Deaths except for Audrey and Katrina, are in the Excel file that is available from the url https://www.pnas.org/doi/10.1073/pnas.1402786111

NOAA Monthly Weather Reports (MWRs) supplied the numbers of deaths for all except Donna, Celia, Audrey and Katrina. The figure for Celia is from https://www.nhc.noaa.gov/pdf/NWS-TPC-5.pdf. For the other three hurricanes, it is from the Atlantic hurricane list in Wikipedia (see the references.)

References

https://www.aoml.noaa.gov/hrd/hurdat/mwr_pdf/ https://en.wikipedia.org/wiki/List_of_Atlantic_hurricanes https://www.nhtsa.gov/file-downloads

Jung, Kiju, et al. "Female hurricanes are deadlier than male hurricanes." Proceedings of the National Academy of Sciences 111.24 (2014): 8782-8787.

Examples

data(hurricNamed)
str(hurricNamed)
plot(log(deaths+0.5) ~ log(NDAM2014), data=hurricNamed)
with(hurricNamed, lines(lowess(log(deaths+0.5) ~ log(NDAM2014))))
plot(log(deaths+0.5) ~ I(NDAM2014^0.14), data=hurricNamed)
with(hurricNamed, lines(lowess(log(deaths+0.1) ~ I(NDAM2014^0.14))))

Blood pressure versus Salt; inter-population data

Description

Median blood pressure, as a fuction of salt intake, for each of 52 human populations.

Usage

intersalt

Format

A data frame with 52 observations on the following 4 variables.

b

a numeric vector

bp

mean diastolic blood pressure (mm Hg)

na

mean sodium excretion (mmol/24h)

country

a character vector

Details

For each population took a sample of 25 males and 25 females from each decade in the age range 20 - 50, i.e. 200 individuals in all.

Source

Intersalt Cooperative Research Group. 1988. Intersalt: an international study of electrolyte excretion and blood pressure: results for 24 hour urinary sodium and potassium excretion. British Medical Journal 297: 319-328.

References

Maindonald, J.H. The Design of Research Studies ? A Statistical Perspective, viii + 109pp. Graduate School Occasional Paper 00/2, Australian National University 2000.

Examples

data(intersalt)
plot(bp ~ na, data=intersalt, xlab="Median sodium excretion (mmol/24h)",
     ylab="Median diatoluc blood pressure (mm Hg)")

Iron Content Measurements

Description

The ironslag data frame has 53 rows and 2 columns. Two methods for measuring the iron content in samples of slag were compared, a chemical and a magnetic method. The chemical method requires greater effort than the magnetic method.

Usage

ironslag

Format

This data frame contains the following columns:

chemical

a numeric vector containing the measurements coming from the chemical method

magnetic

a numeric vector containing the measurments coming from the magnetic method

Source

Hand, D.J., Daly, F., McConway, K., Lunn, D., and Ostrowski, E. eds (1993) A Handbook of Small Data Sets. London: Chapman & Hall.

Examples

iron.lm <- lm(chemical ~ magnetic, data = ironslag)
oldpar <- par(mfrow = c(2,2))
plot(iron.lm)
par(oldpar)

Canadian Labour Force Summary Data (1995-96)

Description

The number of workers in the Canadian labour force broken down by region (BC, Alberta, Prairies, Ontario, Quebec, Atlantic) for the 24-month period from January, 1995 to December, 1996 (a time when Canada was emerging from a deep economic recession).

Usage

jobs

Format

This data frame contains the following columns:

BC

monthly labour force counts in British Columbia

Alberta

monthly labour force counts in Alberta

Prairies

monthly labour force counts in Saskatchewan and Manitoba

Ontario

monthly labour force counts in Ontario

Quebec

monthly labour force counts in Quebec

Atlantic

monthly labour force counts in Newfoundland, Nova Scotia, Prince Edward Island and New Brunswick

Date

year (in decimal form)

Details

These data have been seasonally adjusted.

Source

Statistics Canada

Examples

print("Multiple Variables and Times - Example 2.1.4")
sapply(jobs, range)
pause()

matplot(jobs[,7], jobs[,-7], type="l", xlim=c(95,97.1))
 # Notice that we have been able to use a data frame as the second argument to matplot().
 # For more information on matplot(), type help(matplot)
text(rep(jobs[24,7], 6), jobs[24,1:6], names(jobs)[1:6], adj=0)
pause()

sapply(log(jobs[,-7]), range)
apply(sapply(log(jobs[,-7]), range), 2, diff)
pause()

oldpar <- par(mfrow=c(2,3))
range.log <- sapply(log(jobs[,-7], 2), range)
maxdiff <- max(apply(range.log, 2, diff))
range.log[2,] <- range.log[1,] + maxdiff
titles <- c("BC Jobs","Alberta Jobs","Prairie Jobs",
   "Ontario Jobs", "Quebec Jobs", "Atlantic Jobs")
for (i in 1:6){
plot(jobs$Date, log(jobs[,i], 2), type = "l", ylim = range.log[,i],
    xlab = "Time", ylab = "Number of jobs", main = titles[i])
}
par(oldpar)

Kiwi Shading Data

Description

The kiwishade data frame has 48 rows and 4 columns. The data are from a designed experiment that compared different kiwifruit shading treatments. There are four vines in each plot, and four plots (one for each of four treatments: none, Aug2Dec, Dec2Feb, and Feb2May) in each of three blocks (locations: west, north, east). Each plot has the same number of vines, each block has the same number of plots, with each treatment occurring the same number of times.

Usage

kiwishade

Format

This data frame contains the following columns:

yield

Total yield (in kg)

plot

a factor with levels east.Aug2Dec, east.Dec2Feb, east.Feb2May, east.none, north.Aug2Dec, north.Dec2Feb, north.Feb2May, north.none, west.Aug2Dec, west.Dec2Feb, west.Feb2May, west.none

block

a factor indicating the location of the plot with levels east, north, west

shade

a factor representing the period for which the experimenter placed shading over the vines; with levels: none no shading, Aug2Dec August - December, Dec2Feb December - February, Feb2May February - May

Details

The northernmost plots were grouped together because they were similarly affected by shading from the sun in the north. For the remaining two blocks shelter effects, whether from the west or from the east, were thought more important.

Source

Snelgar, W.P., Manson. P.J., Martin, P.J. 1992. Influence of time of shading on flowering and yield of kiwifruit vines. Journal of Horticultural Science 67: 481-487.

References

Maindonald J H 1992. Statistical design, analysis and presentation issues. New Zealand Journal of Agricultural Research 35: 121-141.

Examples

print("Data Summary - Example 2.2.1")
attach(kiwishade)
kiwimeans <- aggregate(yield, by=list(block, shade), mean)
names(kiwimeans) <- c("block","shade","meanyield")

kiwimeans[1:4,]
pause()

print("Multilevel Design - Example 9.3")
kiwishade.aov <- aov(yield ~ shade+Error(block/shade),data=kiwishade)
summary(kiwishade.aov)
pause()


sapply(split(yield, shade), mean)

pause()

kiwi.table <- t(sapply(split(yield, plot), as.vector))
kiwi.means <- sapply(split(yield, plot), mean)
kiwi.means.table <- matrix(rep(kiwi.means,4), nrow=12, ncol=4)
kiwi.summary <- data.frame(kiwi.means, kiwi.table-kiwi.means.table)
names(kiwi.summary)<- c("Mean", "Vine 1", "Vine 2", "Vine 3", "Vine 4")
kiwi.summary
mean(kiwi.means) # the grand mean (only for balanced design)

if(require(lme4, quietly=TRUE)) {
kiwishade.lmer <- lmer(yield ~ shade + (1|block) + (1|block:plot),
                       data=kiwishade)
## block:shade is an alternative to block:plot

kiwishade.lmer


##                  Residuals and estimated effects
library(lattice)
xyplot(residuals(kiwishade.lmer) ~ fitted(kiwishade.lmer)|block,
                data=kiwishade, groups=shade,
                layout=c(3,1), par.strip.text=list(cex=1.0),
                xlab="Fitted values (Treatment + block + plot effects)",
                ylab="Residuals", pch=1:4, grid=TRUE,
                scales=list(x=list(alternating=FALSE), tck=0.5),
                key=list(space="top", points=list(pch=1:4),
                         text=list(labels=levels(kiwishade$shade)),columns=4))
ploteff <- ranef(kiwishade.lmer, drop=TRUE)[[1]]
qqmath(ploteff, xlab="Normal quantiles", ylab="Plot effect estimates",
       scales=list(tck=0.5))
}

Full Leaf Shape Data Set

Description

Leaf length, width and petiole measurements taken at various sites worldwide. The leafshape17 data frame is the subset that has data for North Queensland sites.

Usage

data(leafshape)
data(leafshape17)

Format

This data frame contains the following columns:

bladelen

leaf length (in mm)

petiole

a numeric vector

bladewid

leaf width (in mm)

latitude

latitude

logwid

natural logarithm of width

logpet

logarithm of petiole

loglen

logarithm of length

arch

leaf architecture (0 = plagiotropic, 1 = orthotropic

location

a factor with levels Sabah, Panama, Costa Rica, N Queensland, S Queensland, Tasmania

Source

King, D.A. and Maindonald, J.H. 1999. Tree architecture in relation to leaf dimensions and tree stature in temperate and tropical rain forests. Journal of Ecology 87: 1012-1024.

Examples

library(MASS)
leaf17.lda <- lda(arch ~ logwid+loglen, data=leafshape17)
leaf17.hat <- predict(leaf17.lda)
leaf17.lda
 table(leafshape17$arch, leaf17.hat$class)
pause()

tab <- table(leafshape17$arch, leaf17.hat$class)
 sum(tab[row(tab)==col(tab)])/sum(tab)
leaf17cv.lda <- lda(arch ~ logwid+loglen, data=leafshape17, CV=TRUE)
tab <- table(leafshape17$arch, leaf17cv.lda$class)
pause()

leaf17.glm <- glm(arch ~ logwid + loglen, family=binomial, data=leafshape17)
 options(digits=3)
summary(leaf17.glm)$coef
pause()

leaf17.one <- cv.binary(leaf17.glm)
table(leafshape17$arch, round(leaf17.one$internal))     # Resubstitution
pause()

table(leafshape17$arch, round(leaf17.one$cv))           # Cross-validation

Leaf and Air Temperature Data

Description

Data are measurements of vapour pressure and of the difference between leaf and air temperature.

Usage

leaftemp

Format

This data frame contains the following columns:

CO2level

Carbon Dioxide level low, medium, high

vapPress

Vapour pressure

tempDiff

Difference between leaf and air temperature

BtempDiff

a numeric vector

Source

Katharina Siebke and Susan von Cammerer, Australian National University.

Examples

print("Fitting Multiple Lines - Example 7.3")

leaf.lm1 <- lm(tempDiff ~ 1 , data = leaftemp)
leaf.lm2 <- lm(tempDiff ~ vapPress, data = leaftemp)
leaf.lm3 <- lm(tempDiff ~ CO2level + vapPress, data = leaftemp)
leaf.lm4 <- lm(tempDiff ~ CO2level + vapPress + vapPress:CO2level,
  data = leaftemp)

anova(leaf.lm1, leaf.lm2, leaf.lm3, leaf.lm4)

summary(leaf.lm2)
plot(leaf.lm2)

Full Leaf and Air Temperature Data Set

Description

The leaftemp.all data frame has 62 rows and 9 columns.

Usage

leaftemp.all

Format

This data frame contains the following columns:

glasshouse

a factor with levels A, B, C

CO2level

a factor with Carbon Dioxide Levels: high, low, medium

day

a factor

light

a numeric vector

CO2

a numeric vector

tempDiff

Difference between Leaf and Air Temperature

BtempDiff

a numeric vector

airTemp

Air Temperature

vapPress

Vapour Pressure

Source

J.H. Maindonald

Mouse Litters

Description

Data on the body and brain weights of 20 mice, together with the size of the litter. Two mice were taken from each litter size.

Usage

litters

Format

This data frame contains the following columns:

lsize

litter size

bodywt

body weight

brainwt

brain weight

Source

Wainright P, Pelkman C and Wahlsten D 1989. The quantitative relationship between nutritional effects on preweaning growth and behavioral development in mice. Developmental Psychobiology 22: 183-193.

Examples

print("Multiple Regression - Example 6.2")

pairs(litters, labels=c("lsize\n\n(litter size)", "bodywt\n\n(Body Weight)",
                        "brainwt\n\n(Brain Weight)"))
  # pairs(litters) gives a scatterplot matrix with less adequate labeling

mice1.lm <- lm(brainwt ~ lsize, data = litters) # Regress on lsize
mice2.lm <- lm(brainwt ~ bodywt, data = litters) #Regress on bodywt
mice12.lm <- lm(brainwt ~ lsize + bodywt, data = litters) # Regress on lsize & bodywt

summary(mice1.lm)$coef # Similarly for other coefficients.
# results are consistent with the biological concept of brain sparing

pause()

hat(model.matrix(mice12.lm))  # hat diagonal
pause()

plot(lm.influence(mice12.lm)$hat, residuals(mice12.lm))

print("Diagnostics - Example 6.3")

mice12.lm <- lm(brainwt ~ bodywt+lsize, data=litters)
oldpar <-par(mfrow = c(1,2))
bx <- mice12.lm$coef[2]; bz <- mice12.lm$coef[3]
res <- residuals(mice12.lm)
plot(litters$bodywt, bx*litters$bodywt+res, xlab="Body weight",
  ylab="Component + Residual")
panel.smooth(litters$bodywt, bx*litters$bodywt+res) # Overlay
plot(litters$lsize, bz*litters$lsize+res, xlab="Litter size", 
  ylab="Component + Residual")
panel.smooth(litters$lsize, bz*litters$lsize+res)
par(oldpar)

Return data required for diagnostic plots

Description

This extracts the code that provides the major part of the statistical information used by plot.lm, leaving out the code used to provide the graphs

Usage

lmdiags(x, which = c(1L:3L, 5L), cook.levels = c(0.5, 1), hii=NULL)

Arguments

Details

See plot.lm for additional information.

Value

Note

This function is designed, in the first place, for use in connection with plotSimDiags, used to give simulations of diagnostic plots for lm objects.

Author(s)

John Maindonald, using code that John Maindonald, Martin Maechler and others had contributed to plot.lm

References

See references for plot.lm

See Also

plotSimDiags, plot.lm

Examples

women.lm <- lm(weight ~ height, data=women)
veclist <- lmdiags(x=women.lm)
## Returns the statistics that are required for graphs 1, 2, 3, and 5

Simple Logistic Regression Data Simulator

Description

This function simulates simple regression data from a logistic model.

Usage

logisticsim(x = seq(0, 1, length=101), a = 2, b = -4, seed=NULL)

Arguments

Value

a list consisting of

Examples

logisticsim()

Cape Fur Seal Lung Measurements

Description

The lung vector consists of weight measurements of lungs taken from 30 Cape Fur Seals that died as an unintended consequence of commercial fishing.

Usage

lung

Murray-Darling basin monthly temperatures

Description

Australian Murray-Darling basin monthly temperatures

Usage

data("mdbAVtJtoD")

Format

The format is: Time-Series [1:867] from 1950 to 2022: 27.44 26.84 24.4 22.27 8.41 ...

Source

Australian Bureau of Meteorology web pages:

Go to the url http://www.bom.gov.au/climate/change/, choose timeseries to display, then click "Download data"

The website gives anomalies from 1961-1990 averages. The monthly means have been added, in order to obtain a series. The monthly means are shown along with plots for the individual months.

Examples

data(mdbAVtJtoD)
plot(window(mdbAVtJtoD, start=c(2000,1)), ylab="Mean monthly data")

Deaths in London from measles

Description

Deaths in London from measles: 1629 – 1939, with gaps.

Usage

data(measles)

Format

The format is: Time-Series [1:311] from 1629 to 1939: 42 2 3 80 21 33 27 12 NA NA ...

Source

Guy, W. A. 1882. Two hundred and fifty years of small pox in London. Journal of the Royal Statistical Society 399-443.

Stocks, P. 1942. Measles and whooping cough during the dispersal of 1939-1940. Journal of the Royal Statistical Society 105:259-291.

References

Lancaster, H. O. 1990. Expectations of Life. Springer.

Family Medical Expenses

Description

The medExpenses data frame contains average weekly medical expenses including drugs for 33 families randomly sampled from a community of 600 families which contained 2700 individuals. These data were collected in the 1970's at an unknown location.

Usage

medExpenses

Format

familysize

number of individuals in a family

expenses

average weekly cost for medical expenses per family member

Examples

with(medExpenses, weighted.mean(expenses, familysize))

Darwin's Wild Mignonette Data

Description

Data compare the heights of crossed plants with self-fertilized plants of the wild mignonette reseda lutea. Plants were paired within the pots in which they were grown, with one on one side and one on the other.

Usage

mignonette

Format

This data frame contains the following columns:

cross

heights of the crossed plants

self

heights of the self-fertilized plants

Source

Darwin, Charles. 1877. The Effects of Cross and Self Fertilisation in the Vegetable Kingdom. Appleton and Company, New York, page 118.

Examples

print("Is Pairing Helpful? - Example 4.3.1")

attach(mignonette)
plot(cross ~ self, pch=rep(c(4,1), c(3,12))); abline(0,1) 
abline(mean(cross-self), 1, lty=2)
detach(mignonette)

Milk Sweetness Study

Description

The milk data frame has 17 rows and 2 columns. Each of 17 panelists compared two milk samples for sweetness.

Usage

milk

Format

This data frame contains the following columns:

four

a numeric vector consisting of the assessments for four units of additive

one

a numeric vector while the is the assessment for one unit of additive

Source

J.H. Maindonald

Examples

print("Rug Plot - Example 1.8.1")
xyrange <- range(milk)
plot(four ~ one, data = milk, xlim = xyrange, ylim = xyrange, pch = 16)
rug(milk$one)
rug(milk$four, side = 2)
abline(0, 1)

Model Car Data

Description

The modelcars data frame has 12 rows and 2 columns. The data are for an experiment in which a model car was released three times at each of four different distances up a 20 degree ramp. The experimenter recorded distances traveled from the bottom of the ramp across a concrete floor.

Usage

modelcars

Format

This data frame contains the following columns:

distance.traveled

a numeric vector consisting of the lengths traveled (in cm)

starting.point

a numeric vector consisting of the distance of the starting point from the top of the ramp (in cm)

Source

W.J. Braun

Examples

plot(modelcars)
modelcars.lm <- lm(distance.traveled ~ starting.point, data=modelcars)
aov(modelcars.lm)
pause()

print("Response Curves - Example 4.6")
attach(modelcars)
stripchart(distance.traveled ~ starting.point, vertical=TRUE, pch=15,
           xlab = "Distance up ramp", ylab="Distance traveled")
detach(modelcars)

WHO Monica Data

Description

The monica data frame has 6357 rows and 12 columns. The dataset mifem (1295 rows) is the subset that has data for females.

Usage

  data(monica)
  data(mifem)
  

Format

Columns are:

outcome

mortality outcome, a factor with levels live, dead

age

age at onset

sex

m = male, f = female

hosp

y = hospitalized, n = not hospitalized

yronset

year of onset

premi

previous myocardial infarction event, a factor with levels y, n, nk not known

smstat

smoking status, a factor with levels c current, x ex-smoker, n non-smoker, nk not known

diabetes

a factor with levels y, n, nk not known

highbp

high blood pressure, a factor with levels y, n, nk not known

hichol

high cholesterol, a factor with levels y, n nk not known

angina

a factor with levels y, n, nk not known

stroke

a factor with levels y, n, nk not known

Source

Newcastle (Australia) centre of the Monica project; see the web site http://www.ktl.fi/monica

Examples

print("CART - Example 10.7")
summary(monica)
pause()

library(rpart)
monica.rpart <- rpart(outcome ~ ., data = monica, cp = 0.0025)
plotcp(monica.rpart)
printcp(monica.rpart)
pause()
monicab.rpart <- prune(monica.rpart, cp=0.006)
print(monicab.rpart)
summary(mifem)
pause()
mifem.rpart <- rpart(outcome ~ ., data = mifem, cp = 0.0025)
plotcp(mifem.rpart)
printcp(mifem.rpart)
pause()

mifemb.rpart <- prune(mifem.rpart, cp=0.006)
print(mifemb.rpart)

Moths Data

Description

The moths data frame has 41 rows and 4 columns. These data are from a study of the effect of habitat on the densities of two species of moth (A and P). Transects were set across the search area. Within transects, sections were identified according to habitat type.

Usage

moths

Format

This data frame contains the following columns:

meters

length of transect

A

number of type A moths found

P

number of type P moths found

habitat

a factor with levels Bank, Disturbed, Lowerside, NEsoak, NWsoak, SEsoak, SWsoak, Upperside

Source

Sharyn Wragg, formerly of Australian National University

Examples

print("Quasi Poisson Regression - Example 8.3")
rbind(table(moths[,4]), sapply(split(moths[,-4], moths$habitat), apply,2,
sum))
A.glm <- glm(formula = A ~ log(meters) + factor(habitat), family =
quasipoisson, data = moths)
summary(A.glm)
  # Note the huge standard errors
moths$habitat <- relevel(moths$habitat, ref="Lowerside")
A.glm <- glm(A ~ habitat + log(meters), family=quasipoisson, data=moths)
summary(A.glm)$coef
## Consider as another possibility
A2.glm <- glm(formula = A ~ sqrt(meters) + factor(habitat), family =
                  quasipoisson(link=sqrt), data = moths)
summary(A2.glm)

Data Filtering Function

Description

A subset of data is selected for which the treatment to control ratio of non-binary covariates is never outside a specified range.

Usage

multilap(df = DAAG::nsw74psid1, maxf = 20, colnames = c("educ",
                 "age", "re74", "re75", "re78"))

Arguments

Author(s)

J.H. Maindonald

Airbag and other influences on accident fatalities

Description

US data, for 1997-2002, from police-reported car crashes in which there is a harmful event (people or property), and from which at least one vehicle was towed. Data are restricted to front-seat occupants, include only a subset of the variables recorded, and are restricted in other ways also.

Usage

nassCDS

Format

A data frame with 26217 observations on the following 15 variables.

dvcat

ordered factor with levels (estimated impact speeds) 1-9km/h, 10-24, 25-39, 40-54, 55+

weight

Observation weights, albeit of uncertain accuracy, designed to account for varying sampling probabilities.

dead

factor with levels alive dead

airbag

a factor with levels none airbag

seatbelt

a factor with levels none belted

frontal

a numeric vector; 0 = non-frontal, 1=frontal impact

sex

a factor with levels f m

ageOFocc

age of occupant in years

yearacc

year of accident

yearVeh

Year of model of vehicle; a numeric vector

abcat

Did one or more (driver or passenger) airbag(s) deploy? This factor has levels deploy nodeploy unavail

occRole

a factor with levels driver pass

deploy

a numeric vector: 0 if an airbag was unavailable or did not deploy; 1 if one or more bags deployed.

injSeverity

a numeric vector; 0:none, 1:possible injury, 2:no incapacity, 3:incapacity, 4:killed; 5:unknown, 6:prior death

caseid

character, created by pasting together the populations sampling unit, the case number, and the vehicle number. Within each year, use this to uniquely identify the vehicle.

Details

Data collection used a multi-stage probabilistic sampling scheme. The observation weight, called national inflation factor (national) in the data from NASS, is the inverse of an estimate of the selection probability. These data include a subset of the variables from the NASS dataset. Variables that are coded here as factors are coded as numeric values in that dataset.

Source

https://www.stat.colostate.edu/~meyer/airbags.htm\ https://www.nhtsa.gov/file-downloads

See also\ https://maths-people.anu.edu.au/~johnm/datasets/airbags/

References

Meyer, M.C. and Finney, T. (2005): Who wants airbags?. Chance 18:3-16.

Farmer, C.H. 2006. Another look at Meyer and Finney's ‘Who wants airbags?’. Chance 19:15-22.

Meyer, M.C. 2006. Commentary on "Another look at Meyer and Finney's ‘Who wants airbags?’. Chance 19:23-24.

For analyses based on the alternative FARS (Fatal Accident Recording System) data, and associated commentary, see:

Cummings, P; McKnight, B, 2010. Accounting for vehicle, crash, and occupant characteristics in traffic crash studies. Injury Prevention 16: 363-366. [The relatively definitive analyses in this paper use a matched cohort design,

Olson, CM; Cummings, P, Rivara, FP, 2006. Association of first- and second-generation air bags with front occupant death in car crashes: a matched cohort study. Am J Epidemiol 164:161-169. [The relatively definitive analyses in this paper use a matched cohort design, using data taken from the FARS (Fatal Accident Recording System) database.]

Braver, ER; Shardell, M; Teoh, ER, 2010. How have changes in air bag designs affected frontal crash mortality? Ann Epidemiol 20:499-510.

The web page https://www-fars.nhtsa.dot.gov/Main/index.aspx has a menu-based interface into the FARS (Fatality Analysis Recording System) data. The FARS database aims to include every accident in which there was at least one fatality.

Examples

data(nassCDS)
xtabs(weight ~ dead + airbag, data=nassCDS)
xtabs(weight ~ dead + airbag + seatbelt + dvcat, data=nassCDS)
tab <- xtabs(weight ~ dead + abcat, data=nassCDS,
             subset=dvcat=="25-39"&frontal==0)[, c(3,1,2)]
round(tab[2, ]/apply(tab,2,sum)*100,2)

Documentation of names of columns in nass9702cor

Description

SASname and longname are from the SAS XPT file nass9702cor.XPT that is available from the website noted below. The name shortname is the name used in the data frame nass9702cor, not included in this package, but available from my website that is noted below. It is also used in nassCDS, for columns that nassCDS includes.

Usage

data(nasshead)

Format

A data frame with 56 observations on the following 3 variables.

shortname

a character vector

SASname

a character vector

longname

a character vector

Details

For full details of the coding of values in columns of nass9702cor, consult one of the SAS format files that can be obtained by following the instructions on Dr Meyer's web site that is noted below.

Source

https://www.stat.colostate.edu/~meyer/airbags.htm\ https://www.nhtsa.gov/file-downloads\

See also https://maths-people.anu.edu.au/~johnm/datasets/airbags/

References

Meyer, M.C. and Finney, T. (2005): Who wants airbags?. Chance 18:3-16.

Farmer, C.H. 2006. Another look at Meyer and Finney's 'Who wants airbags?'. Chance 19:15-22.

Meyer, M.C. 2006. Commentary on "Another look at Meyer and Finney's ‘Who wants airbags?’". Chance 19:23-24.

Examples

data(nasshead)

Record times for Northern Ireland mountain running events

Description

Data were from the 2007 calendar for the Northern Ireland Mountain Running Association.

Usage

data(nihills)
data(lognihills)

Format

A data frame with 23 observations on the following 4 variables.

dist

distances in miles

climb

amount of climb in feet

time

record time in hours for males

timef

record time in hours for females

logdist

distances, log(miles)

logclimb

climb, log(feet)

logtime

record time for males, log(hours)

logtimef

record time for females, log(hours)

Details

These data make an interesting comparison with the dataset hills2000 in the DAAG package.

Source

For more recent information, see https://www.nimra.org.uk/index.php/fixtures/

Examples

data(nihills)
lm(formula = log(time) ~ log(dist) + log(climb), data = nihills)
lm(formula = log(time) ~ log(dist) + log(climb/dist), data = nihills)
lm(formula = logtime ~ logdist + I(logclimb-logdist), data = lognihills)

Labour Training Evaluation Data

Description

This nsw74demo data frame, with 445 rows and 10 columns, is the subset of the nswdemo dataset for which 1974 earnings are available. Data are for the male experimental control and treatment groups, in an investigation of the effect of training on changes, between 1974-1975 and 1978, in the earnings of individuals who had experienced employment difficulties.

Likewise, nsw74psid1 (2675 rows) is the subset of the nswpsid1 data, and nsw74psid3 (313 rows) is the subset of the nswpsid3 data, for which 1974 income is available. NB, also, the nsw74psidA data set.

Usage

  data(nsw74demo)
  data(nsw74psid1)
  data(nsw74psid3)
  data(nsw74psidA)

Format

Columns are:

trt

a numeric vector identifying the study in which the subjects were enrolled (0 = PSID, 1 = NSW).

age

age (in years).

educ

years of education.

black

(0 = not black, 1 = black).

hisp

(0 = not hispanic, 1 = hispanic).

marr

(0 = not married, 1 = married).

nodeg

(0 = completed high school, 1 = dropout).

re74

real earnings in 1974.

re75

real earnings in 1975.

re78

real earnings in 1978.

Details

The nsw74psidA data set (252 rows) was obtained from nsw74psid1 using:

here <- age <= 40 & re74<=5000 & re75 <= 5000 & re78 < 30000

nsw74psidA <- nsw74psid1[here, ]

Source

http://www.columbia.edu/~rd247/nswdata.html

References

Dehejia, R.H. and Wahba, S. 1999. Causal effects in non-experimental studies: re-evaluating the evaluation of training programs. Journal of the American Statistical Association 94: 1053-1062.

Lalonde, R. 1986. Evaluating the economic evaluations of training programs. American Economic Review 76: 604-620.

Labour Training Evaluation Data

Description

The nswdemo data frame contains 722 rows and 10 columns. These data are pertinent to an investigation of the way that earnings changed, between 1974-1975 and 1978, for an experimental treatment who were given job training as compared with a control group who did not receive such training.

The psid1 data set is an alternative non-experimental "control" group. psid2 and psid3 are subsets of psid1, designed to be better matched to the experimental data than psid1. Note also the cps1, cps2 and cps3 datasets (DAAGxtras) that have been proposed as non-experimental controls.

Usage

data(nswdemo)

Format

This data frame contains the following columns:

trt

a numeric vector identifying the study in which the subjects were enrolled (0 = Control, 1 = treated).

age

age (in years).

educ

years of education.

black

(0 = not black, 1 = black).

hisp

(0 = not hispanic, 1 = hispanic).

marr

(0 = not married, 1 = married).

nodeg

(0 = completed high school, 1 = dropout).

re74

real earnings in 1974.

re75

real earnings in 1975.

re78

real earnings in 1978.

Source

https://users.nber.org/~rdehejia/nswdata.html

References

Dehejia, R.H. and Wahba, S. 1999. Causal effects in non-experimental studies: re-evaluating the evaluation of training programs. Journal of the American Statistical Association 94: 1053-1062.

Lalonde, R. 1986. Evaluating the economic evaluations of training programs. American Economic Review 76: 604-620.

Smith, J. A. and Todd, P.E. 2005,"Does Matching overcome. LaLonde?s critique of nonexperimental estimators", Journal of Econometrics 125: 305-353.

Dehejia, R.H. 2005. Practical propensity score matching: a reply to Smith and Todd. Journal of Econometrics 125: 355-364.

See Also

psid1, psid2, psid3

Labour Training Evaluation Data

Description

The cps1 (15992 rows) and psid1 (2490 rows) datasets are from non-experimental "control" groups, used in various studies of the effect of a labor training program, alternative to the experimental control group in nswdemo. The cps2 (2369 rows) and cps3 (429 rows) subsets of cps1 are designed to be better matched to the experimental data than cps1. Likewise, psid2 (253 rows) and psid3 (128 rows) are subsets of psid1 that are designed to be better matched to the experimental data than psid1. The nswpsid1 dataset (2675 rows) combines the experimental treatment group in nswdemo with the psid1 control data from the Panel Study of Income Dynamics (PSID) study.

Usage

  data(psid1)
  data(nswpsid1)
  

Format

Columns are:

trt

a numeric vector identifying the study in which the subjects were enrolled (0 = Control, 1 = treated).

age

age (in years).

educ

years of education.

black

(0 = not black, 1 = black).

hisp

(0 = not hispanic, 1 = hispanic).

marr

(0 = not married, 1 = married).

nodeg

(0 = completed high school, 1 = dropout).

re74

real earnings in 1974.

re75

real earnings in 1975.

re78

real earnings in 1978.

Details

The cps1 and psid1 data sets are two non-experimental "control" groups, alternative to that in nswdemo, used in investigating whether use of such a non-experimental control group can be satisfactory. cps2 and cps3 are subsets of cps1, designed to be better matched to the experimental data than cps1. Similary psid2 and psid3 are subsets of psid1, designed to be better matched to the experimental data than psid1. nswpsid1 combines data for the experimental treatment group in nswdemo with the psid1 control data from the Panel Study of Income Dynamics (PSID) study.

Source

https://users.nber.org/~rdehejia/nswdata.html

References

Dehejia, R.H. and Wahba, S. 1999. Causal effects in non-experimental studies: re-evaluating the evaluation of training programs. Journal of the American Statistical Association 94: 1053-1062.

Lalonde, R. 1986. Evaluating the economic evaluations of training programs. American Economic Review 76: 604-620.

Smith, J. A. and Todd, P.E. "Does Matching overcome. LaLonde?s critique of nonexperimental estimators", Journal of Econometrics 125: 305-353.

Dehejia, R.H. 2005. Practical propensity score matching: a reply to Smith and Todd. Journal of Econometrics 125: 355-364.

Bounce - obsolete

Description

A utility function for oneway.plot

Author(s)

J.H. Maindonald

Measurements on 12 books

Description

Data giving thickness (mm), height (cm), width (cm) and weight (g), of 12 books. Books were selected so that thickness decreased as page area increased

Usage

data(oddbooks)

Format

A data frame with 12 observations on the following 4 variables.

thick

a numeric vector

height

a numeric vector

breadth

a numeric vector

weight

a numeric vector

Source

JM took books from his library.

Examples

data(oddbooks)
str(oddbooks)
plot(oddbooks) 

Paired Sample t-test

Description

This function performs a t-test for the mean difference for paired data, and produces a scatterplot of one column against the other column, showing whether there was any benefit to using the paired design.

Usage

onesamp(dset, x="unsprayed", y="sprayed", xlab=NULL, ylab=NULL,
        dubious=NULL, conv=NULL, dig=2, ...)

Arguments

Value

A scatterplot of y against x together with estimates of standard errors and standard errors of the difference (y-x).

Also produced is a confidence interval and p-value for the test.

Author(s)

J.H. Maindonald

Examples

onesamp(dset = pair65, x = "ambient", y = "heated", xlab =
        "Amount of stretch (ambient)", ylab =
        "Amount of stretch (heated)") 

One Sample Permutation t-test

Description

This function computes the p-value for the one sample t-test using a permutation test. The permutation density can also be plotted.

Usage

onet.permutation(x = DAAG::pair65$heated - DAAG::pair65$ambient, nsim = 2000, 
plotit = TRUE)

Arguments

Value

The p-value for the test of the hypothesis that the mean of x differs from 0

Author(s)

J.H. Maindonald

References

Good, P. 2000. Permutation Tests. Springer, New York.

Examples

onet.permutation()

One Sample Permutation t-test

Description

This function computes the p-value for the one sample t-test using a permutation test. The permutation density can also be plotted.

Usage

onetPermutation(x = DAAG::pair65$heated - DAAG::pair65$ambient, nsim = 2000, 
plotit = TRUE)

Arguments

Details

This function calculates only a one-sided p-value. The EnvStats::oneSamplePermutationTest in the EnvStats package offers a choice between two-sided and one-sided tests. If the statmod package is available, a correction will be applied that accounts for a small bias that results when permutations are sampled.

Value

The p-value for the test of the hypothesis that the mean of x differs from 0

Author(s)

J.H. Maindonald

References

Good, P. 2000. Permutation Tests. Springer, New York.

Examples

onetPermutation() 

Display of One Way Analysis Results

Description

A line plot of estimates for unstructured comparison of factor levels

Usage

onewayPlot(obj, trtnam = "trt", axisht = 4.5, xlim = NULL,
    xlab = NULL, lsdht = 1.5, hsdht = 0.5, textht = axisht -
        2.5, oma = rep(1, 4), angle = 80, alpha = 0.05)
oneway.plot(obj, trtnam = "trt", axisht = 4.5, xlim = NULL,
    xlab = NULL, lsdht = 1.5, hsdht = 0.5, textht = axisht -
        2.5, oma = rep(1, 4), angle = 80, alpha = 0.05)

Arguments

Value

Estimates, labeled with level names, are set out along a line

Author(s)

J.H. Maindonald

Examples

rice.aov <- aov(ShootDryMass ~ trt, data=rice)
onewayPlot(obj=rice.aov)

Challenger O-rings Data

Description

Record of the number and type of O-ring failures prior to the tragic Challenger mission in January, 1986.

Usage

orings

Format

This data frame contains the following columns:

Temperature

O-ring temperature for each test firing or actual launch of the shuttle rocket engine

Erosion

Number of erosion incidents

Blowby

Number of blowby incidents

Total

Total number of incidents

Source

Presidential Commission on the Space Shuttle Challenger Accident, Vol. 1, 1986: 129-131.

References

Tufte, E. R. 1997. Visual Explanations. Graphics Press, Cheshire, Connecticut, U.S.A.

Examples

oldpar <- par(mfrow=c(1,2))
plot(Total~Temperature, data = orings[c(1,2,4,11,13,18),]) # the 
               # observations included in the pre-launch charts
plot(Total~Temperature, data = orings)
par(oldpar)

Overlapping Density Plots

Description

Densities for two distinct samples are estimated and plotted.

Usage

overlapDensity(x0, x1, ratio = c(0.05, 20), ratio.number = FALSE,
          plotvalues = c("Density", "Numbers"), gpnames = c("Control", "Treatment"),
          cutoffs = c(lower = TRUE, upper = TRUE), bw = FALSE,
          xlab = "Score", ylab = NULL,
          col = 1:2, lty = 1:2, lwd = c(1, 1), ...)

overlap.density(x0, x1, ratio = c(0.05, 20), ratio.number = FALSE,
          plotvalues = c("Density", "Numbers"), gpnames = c("Control", "Treatment"),
          cutoffs = c(lower = TRUE, upper = TRUE), bw = FALSE,
          xlab = "Score", ylab = NULL,
          col = 1:2, lty = 1:2, lwd = c(1, 1), ...)

Arguments

Author(s)

J.H. Maindonald

See Also

t.test

Examples

attach(two65)
overlapDensity(ambient,heated)
t.test(ambient,heated)

Ozone Data

Description

Monthly provisional mean total ozone (in Dobson units) at Halley Bay (approximately corrected to Bass-Paur).

Usage

ozone

Format

This data frame contains the following columns:

Year

the year

Aug

August mean total ozone

Sep

September mean total ozone

Oct

October mean total ozone

Nov

November mean total ozone

Dec

December mean total ozone

Jan

January mean total ozone

Feb

February mean total ozone

Mar

March mean total ozone

Apr

April mean total ozone

Annual

Yearly mean total ozone

Source

Shanklin, J. (2001) Ozone at Halley, Rothera and Vernadsky/Faraday.

http://www.antarctica.ac.uk/met/jds/ozone/data/zoz5699.dat

References

Christie, M. (2000) The Ozone Layer: a Philosophy of Science Perspective. Cambridge University Press.

Examples

AnnualOzone <- ts(ozone$Annual, start=1956)
plot(AnnualOzone)

Heated Elastic Bands

Description

The pair65 data frame has 9 rows and 2 columns. Eighteen elastic bands were divided into nine pairs, with bands of similar stretchiness placed in the same pair. One member of each pair was placed in hot water (60-65 degrees C) for four minutes, while the other was left at ambient temperature. After a wait of about ten minutes, the amounts of stretch, under a 1.35 kg weight, were recorded.

Usage

pair65

Format

This data frame contains the following columns:

heated

a numeric vector giving the stretch lengths for the heated bands

ambient

a numeric vector giving the stretch lengths for the unheated bands

Source

J.H. Maindonald

Examples

mean(pair65$heated - pair65$ambient)
sd(pair65$heated - pair65$ambient)

Scatterplot Panel

Description

This function produces a bivariate scatterplot with the Pearson correlation. This is for use with the function panelplot.

Usage

panel.corr(data, ...)

Arguments

Author(s)

J.H. Maindonald

Examples

# correlation between body and brain weights for 20 mice:

weights <- litters[,-1]
names(weights) <-  c("x","y")
weights <- list(weights)
weights[[1]]$xlim <- range(litters[,2])
weights[[1]]$ylim <- range(litters[,3])
panelplot(weights, panel.corr, totrows=1, totcols=1)

Scatterplot Panel

Description

This function produces a bivariate scatterplot with the Pearson correlation. This is for use with the function panelplot.

Usage

panelCorr(data, ...)

Arguments

Author(s)

J.H. Maindonald

Examples

# correlation between body and brain weights for 20 mice:

weights <- litters[,-1]
names(weights) <-  c("x","y")
weights <- list(weights)
weights[[1]]$xlim <- range(litters[,2])
weights[[1]]$ylim <- range(litters[,3])
panelplot(weights, panelCorr, totrows=1, totcols=1)

Panel Plot

Description

Panel plots of various types.

Usage

panelplot(data, panel=points, totrows=3, totcols=2, oma=rep(2.5, 4), 
par.strip.text=NULL, ...)

Arguments

Author(s)

J.H. Maindonald

Examples

       x1 <- x2 <- x3 <- (11:30)/5
          y1 <- x1 + rnorm(20)/2
          y2 <- 2 - 0.05 * x1 + 0.1 * ((x1 - 1.75))^4 + 1.25 * rnorm(20)
          r <- round(cor(x1, y2), 3)
          rho <- round(cor(rank(x1), rank(y2)), 3)
          y3 <- (x1 - 3.85)^2 + 0.015 + rnorm(20)/4
          theta <- ((2 * pi) * (1:20))/20
          x4 <- 10 + 4 * cos(theta)
          y4 <- 10 + 4 * sin(theta) + (0.5 * rnorm(20))
          r1 <- cor(x1, y1)
          xy <- data.frame(x = c(rep(x1, 3), x4), y = c(y1, y2, y3, y4),
                           gp = rep(1:4, rep(20, 4)))
          xy <- split(xy,xy$gp)
          xlimdf <- lapply(list(x1,x2,x3,x4), range)
          ylimdf <- lapply(list(y1,y2,y3,y4), range)
          xy <- lapply(1:4, function(i,u,v,w){list(xlim=v[[i]],ylim=w[[i]],
                             x=u[[i]]$x, y=u[[i]]$y)},
                                u=xy, v=xlimdf, w=ylimdf)

          panel.corr <- function (data, ...)
              {
              x <- data$x
              y <- data$y
              points(x, y, pch = 16)
              chh <- par()$cxy[2]
              x1 <- min(x)
              y1 <- max(y) - chh/4
              r1 <- cor(x, y)
              text(x1, y1, paste(round(r1, 3)), cex = 0.8, adj = 0)
          }


          panelplot(xy, panel=panel.corr, totrows=2, totcols=2,oma=rep(1,4))

Pause before continuing execution

Description

If a program produces several plots, isertion of pause() between two plots suspends execution until the <Enter> key is pressed, to allow inspection of the current plot.

Usage

pause()

Author(s)

From the ‘sm’ package of Bowman and Azzalini (1997)

Plot(s) of simulated sampling distributions

Description

Plots are based on the output from simulateSampDist(). By default, both density plots and normal probability plots are given, for a sample from the specified population and for samples of the relevant size(s)

Usage

plotSampDist(sampvalues, graph = c("density", "qq"), cex = 0.925,
             titletext = "Empirical sampling distributions of the",
             popsample=TRUE, ...)

Arguments

Value

Plots graph(s), as described above.

Author(s)

John Maindonald

References

Maindonald, J.H. and Braun, W.J. (3rd edn, 2010) “Data Analysis and Graphics Using R”, Sections 3.3 and 3.4.

See Also

See Also help(simulateSampDist)

Examples

## By default, sample from normal population
simAvs <- simulateSampDist()
par(pty="s")
plotSampDist(simAvs)
## Sample from empirical distribution
simAvs <- simulateSampDist(rpop=rivers)
plotSampDist(simAvs)

## The function is currently defined as
function(sampvalues, graph=c("density", "qq"), cex=0.925,
           titletext="Empirical sampling distributions of the",
           popsample=TRUE, ...){
    if(length(graph)==2)oldpar <- par(mfrow=c(1,2), mar=c(3.1,4.1,1.6,0.6),
               mgp=c(2.5, 0.75, 0), oma=c(0,0,1.5,0), cex=cex)
    values <- sampvalues$values
    numINsamp <- sampvalues$numINsamp
    funtxt <- sampvalues$FUN
    nDists <- length(numINsamp)+1
    nfirst <- 2
    legitems <- paste("Size", numINsamp)
    if(popsample){nfirst <- 1
                  legitems <- c("Size 1", legitems)
                }
    if(match("density", graph)){
      popdens <- density(values[,1], ...)
      avdens <- vector("list", length=nDists)
      maxht <- max(popdens$y)
      ## For each sample size specified in numINsamp, calculate mean
      ## (or other statistic specified by FUN) for numsamp samples
      for(j in nfirst:nDists){
        av <- values[, j]
        avdens[[j]] <- density(av, ...)
        maxht <- max(maxht, avdens[[j]]$y)
      }
    }
    if(length(graph)>0)
      for(graphtype in graph){
        if(graphtype=="density"){
          if(popsample)
          plot(popdens, ylim=c(0, 1.2*maxht), type="l", yaxs="i",
               main="")
          else plot(avdens[[2]], type="n", ylim=c(0, 1.2*maxht),
                    yaxs="i", main="")
          for(j in 2:nDists)lines(avdens[[j]], col=j)
          legend("topleft",
                 legend=legitems,
                 col=nfirst:nDists, lty=rep(1,nDists-nfirst+1), cex=cex)
        }
        if(graphtype=="qq"){
          if(popsample) qqnorm(values[,1], main="")
          else qqnorm(values[,2], type="n")
          for(j in 2:nDists){
            qqav <- qqnorm(values[, j], plot.it=FALSE)
            points(qqav, col=j, pch=j)
           }
            legend("topleft", legend=legitems,
                   col=nfirst:nDists, pch=nfirst:nDists, cex=cex)
       }
      }
    if(par()$oma[3]>0){
      outer <- TRUE
      line=0
    }  else
    {
      outer <- FALSE
      line <- 1.25
    }
    if(!is.null(titletext))
      mtext(side=3, line=line,
            paste(titletext, funtxt),
            cex=1.1, outer=outer)
    if(length(graph)>1)par(oldpar)
  }

Diagnostic plots for simulated data

Description

This provides diagnostic plots, closely equivalent to those provided by plot.lm, for simulated data. By default, simulated data are for the fitted model. Alternatively, simulated data can be supplied, making it possible to check the effct of fitting, e.g., an AR1 model.

Usage

plotSimDiags(obj, simvalues = NULL, seed = NULL,
types = NULL, which = c(1:3, 5), layout = c(4, 1), qqline=TRUE,
cook.levels = c(0.5, 1), caption = list("Residuals vs Fitted",
"Normal Q-Q", "Scale-Location", "Cook's distance", "Residuals vs Leverage",
expression("Cook's dist vs Leverage  " * h[ii]/(1 - h[ii]))),
...)

Arguments

Details

Diagnotic plots from repeated simulations from the fitted model provide a useful indication of the range of variation in the model diagnistics that are consistent with the fitted model.

Value

A list of lattice graphics objects is returned, one for each value of which. List elements for which a graphics object is not returned are set to NULL. Or if which is of length 1, a lattice graphics object.

For the default which=c(1:3,5), list items 1, 2, 3 and 5 above contain graphics objects, with list elements 4 and 6 set to NULL.

Note

The graphics objects contained in individual list elements can be extracted for printing, or updating and printing, as required. If the value is returned to the command line, list elements that are not NULL will be printed in turn.

Author(s)

John Maindonald, with some code chunks adapted from plot.lm

References

See plot.lm

See Also

plot.lm, lmdiags

Examples

women.lm <- lm(height ~ weight, data=women)
gphlist <- plotSimDiags(obj=women.lm, which=c(1:3,5))

Simulate scatterplots, from lm object with a single explanatory variable.

Description

This plots simulated y-values, or residuals from such simulations, against x-values .

Usage

plotSimScat(obj, sigma = NULL, layout = c(4, 1), type = c("p", "r"),
show = c("points", "residuals"), ...)

Arguments

Value

A lattice graphics object is returned.

Author(s)

J H Maindonald

See Also

plotSimDiags

Examples

nihills.lm <- lm(timef~time, data=nihills)
plotSimDiags(nihills.lm)

## The function is currently defined as
function (obj, sigma = NULL, layout = c(4, 1), type = c("p",
    "r"), show = c("points", "residuals"))
{
    nsim <- prod(layout)
    if (is.null(sigma))
        sigma <- summary(obj)[["sigma"]]
    hat <- fitted(obj)
    xnam <- all.vars(formula(obj))[2]
    ynam <- all.vars(formula(obj))[1]
    df <- data.frame(sapply(1:nsim, function(x) rnorm(length(hat),
        sd = sigma)))
    if (show[1] == "points")
        df <- df + hat
    simnam <- names(df) <- paste("Simulation", 1:nsim, sep = "")
    df[, c(xnam, ynam)] <- model.frame(obj)[, c(xnam, ynam)]
    if (show[1] != "points") {
        df[, "Residuals"] <- df[, ynam] - hat
        ynam <- "Residuals"
        legadd <- "residuals"
    }
    else legadd <- "data"
    leg <- list(text = paste(c("Simulated", "Actual"), legadd),
        columns = 2)
    formula <- formula(paste(paste(simnam, collapse = "+"), "~",
        xnam))
    parset <- simpleTheme(pch = c(16, 16), lty = 2, col = c("black",
        "gray"))
    gph <- xyplot(formula, data = df, outer = TRUE, par.settings = parset,
        auto.key = leg, lty = 2, layout = layout, type = type)
    formxy <- formula(paste(ynam, "~", xnam))
    addgph <- xyplot(formxy, data = df, pch = 16, col = "gray")
    gph + as.layer(addgph, under = TRUE)
  }

Simple Poisson Regression Data Simulator

Description

This function simulates simple regression data from a Poisson model. It also has the option to create over-dispersed data of a particular type.

Usage

poissonsim(x = seq(0, 1, length=101), a = 2, b = -4, intcp.sd=NULL,
           slope.sd=NULL, seed=NULL)

Arguments

Value

a list consisting of

Examples

poissonsim()

Possum Measurements

Description

The possum data frame consists of nine morphometric measurements on each of 104 mountain brushtail possums, trapped at seven Australian sites from Southern Victoria to central Queensland. See possumsites for further details. The fossum data frame is the subset of possum that has measurements for the 43 females.

Usage

  data(possum)
  data(fossum)
  

Format

This data frame contains the following columns:

case

observation number

site

one of seven locations where possums were trapped. The sites were, in order,Cambarville, Bellbird, Whian Whian, Byrangery, Conondale, Allyn River and Bulburin

Pop

a factor which classifies the sites as Vic Victoria, other New South Wales or Queensland

sex

a factor with levels f female, m male

age

age

hdlngth

head length

skullw

skull width

totlngth

total length

taill

tail length

footlgth

foot length

earconch

ear conch length

eye

distance from medial canthus to lateral canthus of right eye

chest

chest girth (in cm)

belly

belly girth (in cm)

Source

Lindenmayer, D. B., Viggers, K. L., Cunningham, R. B., and Donnelly, C. F. 1995. Morphological variation among columns of the mountain brushtail possum, Trichosurus caninus Ogilby (Phalangeridae: Marsupiala). Australian Journal of Zoology 43: 449-458.

Examples

boxplot(earconch~sex, data=possum)
pause()

sex <- as.integer(possum$sex)
oldpar <- par(oma=c(2,4,5,4))
pairs(possum[, c(9:11)], pch=c(0,2:7), col=c("red","blue"),
  labels=c("tail\nlength","foot\nlength","ear conch\nlength"))
chh <- par()$cxy[2]; xleg <- 0.05; yleg <- 1.04
oldpar <- par(xpd=TRUE)
legend(xleg, yleg, c("Cambarville", "Bellbird", "Whian Whian  ",
  "Byrangery", "Conondale  ","Allyn River", "Bulburin"), pch=c(0,2:7),
  x.intersp=1, y.intersp=0.75, cex=0.8, xjust=0, bty="n", ncol=4)
text(x=0.2, y=yleg - 2.25*chh, "female", col="red", cex=0.8, bty="n")
text(x=0.75, y=yleg - 2.25*chh, "male", col="blue", cex=0.8, bty="n")
par(oldpar)
pause()

sapply(possum[,6:14], function(x)max(x,na.rm=TRUE)/min(x,na.rm=TRUE))
pause()

here <- na.omit(possum$footlgth)
possum.prc <- princomp(possum[here, 6:14])
pause()

plot(possum.prc$scores[,1] ~ possum.prc$scores[,2],
  col=c("red","blue")[as.numeric(possum$sex[here])],
  pch=c(0,2:7)[possum$site[here]], xlab = "PC1", ylab = "PC2")
  # NB: We have abbreviated the axis titles
chh <- par()$cxy[2]; xleg <- -15; yleg <- 20.5
oldpar <- par(xpd=TRUE)
legend(xleg, yleg, c("Cambarville", "Bellbird", "Whian Whian  ",
  "Byrangery", "Conondale  ","Allyn River", "Bulburin"), pch=c(0,2:7),
  x.intersp=1, y.intersp=0.75, cex=0.8, xjust=0, bty="n", ncol=4)
text(x=-9, y=yleg - 2.25*chh, "female", col="red", cex=0.8, bty="n")
summary(possum.prc, loadings=TRUE, digits=2)
par(oldpar)
pause()

require(MASS)
here <- !is.na(possum$footlgth)
possum.lda <- lda(site ~ hdlngth+skullw+totlngth+ taill+footlgth+
  earconch+eye+chest+belly, data=possum, subset=here)
options(digits=4)
possum.lda$svd   # Examine the singular values
plot(possum.lda, dimen=3)
  # Scatterplot matrix - scores on 1st 3 canonical variates (Figure 11.4)
possum.lda
pause()
boxplot(fossum$totlngth)

Possum Sites

Description

The possumsites data frame consists of Longitudes, Latitudes, and altitudes for the seven sites from Southern Victoria to central Queensland where the possum observations were made.

Usage

possumsites

Format

This data frame contains the following columns:

Longitude

a numeric vector

Latitude

a numeric vector

altitude

in meters

Source

Lindenmayer, D. B., Viggers, K. L., Cunningham, R. B., and Donnelly, C. F. 1995. Morphological variation among columns of the mountain brushtail possum, Trichosurus caninus Ogilby (Phalangeridae: Marsupiala). Australian Journal of Zoology 43: 449-458.

Examples

require(oz)
oz(sections=c(3:5, 11:16))
attach(possumsites)
points(Longitude, Latitude, pch=16, col=2)
chw <- par()$cxy[1]
chh <- par()$cxy[2]
posval <- c(2,4,2,2,4,2,2)
text(Longitude+(3-posval)*chw/4, Latitude, row.names(possumsites), pos=posval)

Plot of Power Functions

Description

This function plots powers of a variable on the interval [0,10].

Usage

powerplot(expr="x^2", xlab="x", ylab="y", ...)

Arguments

Details

Other expressions such as "sin(x)" and "cos(x)", etc. could also be plotted with this function, but results are not guaranteed.

Value

A plot of the given expression on the interval [0,10].

Author(s)

J.H. Maindonald

Examples

   oldpar <- par(mfrow = c(2, 3), mar = par()$mar - c(
        1, 1, 1.0, 1),  mgp = c(1.5, 0.5, 0),  oma=c(0,1,0,1))
#    on.exit(par(oldpar))
    powerplot(expr="sqrt(x)", xlab="")
    powerplot(expr="x^0.25", xlab="", ylab="")
    powerplot(expr="log(x)", xlab="", ylab="")
    powerplot(expr="x^2")
    powerplot(expr="x^4", ylab="")
    powerplot(expr="exp(x)", ylab="")
par(oldpar)

Deaths from various causes, in London from 1629 till 1881, with gaps

Description

Deaths from "flux" or smallpox, measles, all causes, and ratios of the the first two categories to total deaths.

Usage

data(poxetc)

Format

This is a multiple time series consisting of 5 series: fpox, measles, all, fpox2all, measles2all.

Source

Guy, W. A. 1882. Two hundred and fifty years of small pox in London. Journal of the Royal Statistical Society 399-443.

References

Lancaster, H. O. 1990. Expectations of Life. Springer.

Examples

data(poxetc)
str(poxetc)
plot(poxetc) 

Predictive Error Sum of Squares

Description

Allen's PRESS statistic is computed for a fitted model.

Usage

press(obj)

Arguments

Value

A single numeric value.

Author(s)

W.J. Braun

See Also

lm

Examples

litters.lm <- lm(brainwt ~ bodywt + lsize, data = litters)
press(litters.lm)
litters.lm0 <- lm(brainwt ~ bodywt + lsize -1, data=litters)
press(litters.lm0) # no intercept
litters.lm1 <- lm(brainwt ~ bodywt, data=litters)
press(litters.lm1) # bodywt only
litters.lm2 <- lm(brainwt ~ bodywt + lsize + lsize:bodywt, data=litters)
press(litters.lm2) # include an interaction term

Primate Body and Brain Weights

Description

A subset of Animals data frame from the MASS library. It contains the average body and brain measurements of five primates.

Usage

primates

Format

This data frame contains the following columns:

Bodywt

a numeric vector consisting of the body weights (in kg) of five different primates

Brainwt

a numeric vector consisting of the corresponding brain weights (in g)

Source

P. J. Rousseeuw and A. M. Leroy (1987) Robust Regression and Outlier Detection. Wiley, p. 57.

Examples

attach(primates)
plot(x=Bodywt, y=Brainwt, pch=16,
       xlab="Body weight (kg)", ylab="Brain weight (g)",
       xlim=c(5,300), ylim=c(0,1500))
chw <- par()$cxy[1]
chh <- par()$cxy[2]
text(x=Bodywt+chw, y=Brainwt+c(-.1,0,0,.1,0)*chh,
       labels=row.names(primates), adj=0)
detach(primates)

Progression of Record times for track races, 1912 - 2008

Description

Progression in world record times for track and road races.

Usage

data(progression)

Format

A data frame with 227 observations on the following 4 columns.

year

Year that time was first recorded

Distance

distance in kilometers

Time

time in minutes

race

character; descriptor for event (100m, mile, ...)

Details

Record times for men's track events, from 1912 onwards. The series starts with times that were recognized as record times in 1912, where available.

Source

Links to sources for the data are at

https://en.wikipedia.org/wiki/Athletics_world_record

Examples

data(progression)
plot(log(Time) ~ log(Distance), data=progression)
res <- resid(lm(log(Time) ~ log(Distance), data=progression))
plot(res ~ log(Distance), data=progression,
     ylab="Residuals from regression line on log scales")
library(lattice)
xyplot(log(Time) ~ log(Distance), data=progression, type=c("p","r"))
xyplot(log(Time) ~ log(Distance), data=progression,
       type=c("p","smooth"))

Simulate QQ reference plots

Description

This function computes the QQ plot for given data and specified distribution, then repeating the comparison for data simulated from the specified distribution. The plots for simulated data give an indication of the range of variation that is to expected, and thus calibrate the eye.

Usage

qreference(test = NULL, m = 30, nrep = 6, pch=c(16,2), distribution = function(x) qnorm(x, 
    mean = ifelse(is.null(test), 0, mean(test)), sd = ifelse(is.null(test), 
    1, sd(test))), seed = NULL, nrows = NULL, cex.strip = 0.75, 
    xlab = NULL, ylab = NULL) 

Arguments

Value

QQ plots of the sample (if test is non-null) and all reference samples

Author(s)

J.H. Maindonald

Examples

# qreference(rt(30,4))

# qreference(rt(30,4), distribution=function(x) qt(x, df=4))

# qreference(rexp(30), nrep = 4)

# toycars.lm <- lm(distance ~ angle + factor(car), data = toycars)
# qreference(residuals(toycars.lm), nrep = 9)

Scottish Hill Races Data - 2000

Description

The record times in 2000 for 77 Scottish long distance races. We believe the data are, for the most part, trustworthy. However, the dist variable for Caerketton (record 58) seems to have been variously recorded as 1.5 mi and 2.5 mi.

Usage

races2000

Format

This data frame contains the following columns:

dist

distance, in miles (on the map)

climb

total height gained during the route, in feet

time

record time in hours

timef

record time in hours for females

type

a factor, with levels indicating type of race, i.e. hill, marathon, relay, uphill or other

Source

The Scottish Running Resource, http://www.hillrunning.co.uk

Examples

    pairs(races2000[,-5])

Rainforest Data

Description

The rainforest data frame has 65 rows and 7 columns.

Usage

rainforest

Format

This data frame contains the following columns:

dbh

a numeric vector

wood

a numeric vector

bark

a numeric vector

root

a numeric vector

rootsk

a numeric vector

branch

a numeric vector

species

a factor with levels Acacia mabellae, C. fraseri, Acmena smithii, B. myrtifolia

Source

J. Ash, Australian National University

References

Ash, J. and Helman, C. (1990) Floristics and vegetation biomass of a forest catchment, Kioloa, south coastal N.S.W. Cunninghamia, 2: 167-182.

Examples

table(rainforest$species)

Rare and Endangered Plant Species

Description

These data were taken from species lists for South Australia, Victoria and Tasmania. Species were classified as CC, CR, RC and RR, with C denoting common and R denoting rare. The first code relates to South Australia and Victoria, and the second to Tasmania. They were further classified by habitat according to the Victorian register, where D = dry only, W = wet only, and WD = wet or dry.

Usage

rareplants

Format

The format is: chr "rareplants"

Source

Jasmyn Lynch, Department of Botany and Zoology at Australian National University

Examples

chisq.test(rareplants)

Summary results from Reproducibility Study: Psychology

Description

The chief interest, in collating this dataset, was in the measures of effect size, for the originl study and for the replication.

Usage

data("repPsych")

Format

A data frame with 97 observations on the following 12 variables.

stat

Test statistic. Character

Journal

Where published. Character.

Discipline

Cognitive or Social. Character.

reportedP.O

Reported p-value. Character.

effSizeO

Original effect size. Character.

T_r.O

Original effect size, as correlation. Numeric.

T_r.R

Replication effect size, as correlation. Numeric.

efftype

a character vector

tlike

Was test statistic t or F(1, m). Logical.

d_O

Original effect size, on Cohen's d scale. Numeric.

d_R

Replication effect size, on Cohen's d scale. Numeric.

Details

Effect estimates on a correlation scale were converted to a Cohen's d scale using d = 2r/sqrt(1-r^2).

Source

https://osf.io/fgjvw/

References

https://osf.io/ezum7/ https://osf.io/z7aux Open Science Collaboration, 2015. Estimating the reproducibility of psychological science. Science, 349(6251), p.aac4716.

Examples

data(repPsych)

Genetically Modified and Wild Type Rice Data

Description

The rice data frame has 72 rows and 7 columns. The data are from an experiment that compared wild type (wt) and genetically modified rice plants (ANU843), each with three different chemical treatments (F10, NH4Cl, and NH4NO3).

Usage

rice

Format

This data frame contains the following columns:

PlantNo

a numeric vector

Block

a numeric vector

RootDryMass

a numeric vector

ShootDryMass

a numeric vector

trt

a factor with levels F10, NH4Cl, NH4NO3, F10 +ANU843, NH4Cl +ANU843, NH4NO3 +ANU843

fert

a factor with levels F10 NH4Cl NH4NO3

variety

a factor with levels wt ANU843

Source

Perrine, F.M., Prayitno, J., Weinman, J.J., Dazzo, F.B. and Rolfe, B. 2001. Rhizobium plasmids are involved in the inhibition or stimulation of rice growth and development. Australian Journal of Plant Physiology 28: 923-927.

Examples

print("One and Two-Way Comparisons - Example 4.5")
attach(rice)
oldpar <- par(las = 2)
stripchart(ShootDryMass ~ trt, pch=1, cex=1, xlab="Level of factor 1")
detach(rice)
pause()

rice.aov <- aov(ShootDryMass ~ trt, data=rice); anova(rice.aov)
anova(rice.aov)
pause()

summary.lm(rice.aov)$coef
pause()

rice$trt <- relevel(rice$trt, ref="NH4Cl")
  # Set NH4Cl as the baseline

fac1 <- factor(sapply(strsplit(as.character(rice$trt)," \\+"), function(x)x[1]))
anu843 <- sapply(strsplit(as.character(rice$trt), "\\+"), 
function(x)c("wt","ANU843")[length(x)])
anu843 <- factor(anu843, levels=c("wt", "ANU843"))
attach(rice)
interaction.plot(fac1, anu843, ShootDryMass)
detach(rice)
par(oldpar)

Pacific Rock Art features

Description

Data characterise rock art at 103 sites in the Pacific.

Usage

rockArt

Format

A data frame with 103 observations on the following 641 variables.

Site.No.

a numeric vector

Site.Name

a character vector

Site.Code

a character vector

District

a character vector

Island

a character vector

Country

a character vector

Technique

a character vector

Engtech

a character vector

red

a numeric vector

black

a numeric vector

yellow

a numeric vector

white

a numeric vector

green

a numeric vector

red.blk

a numeric vector

red.wh

a numeric vector

red.yell

a numeric vector

r.w.y

a numeric vector

black.white

a numeric vector

blue

a numeric vector

Geology

a character vector

Topography

a character vector

Location

a character vector

Proxhab.km.

a character vector

Proxcoast.km.

a numeric vector

Maxheight.m.

a numeric vector

Language

a character vector

No.motif

a character vector

Ca1

a numeric vector

Ca2

a numeric vector

Ca3

a numeric vector

Ca4

a numeric vector

Cb5

a numeric vector

Cb6

a numeric vector

Cc7

a numeric vector

Cc8

a numeric vector

Cc9

a numeric vector

Cc10

a numeric vector

Cc11

a numeric vector

Cc12

a numeric vector

Cc13

a numeric vector

Cc14

a numeric vector

Cc15

a numeric vector

Cc16

a numeric vector

Cc17

a numeric vector

Cc18

a numeric vector

Cc19

a numeric vector

Cc20

a numeric vector

Cd21

a numeric vector

Cd22

a numeric vector

Cd23

a numeric vector

Cd24

a numeric vector

Cd25

a numeric vector

Cd26

a numeric vector

Cd27

a numeric vector

Ce28

a numeric vector

Ce29

a numeric vector

Cf30

a numeric vector

Cf31

a numeric vector

Cf32

a numeric vector

Cf33

a numeric vector

Cf34

a numeric vector

Cf35

a numeric vector

Cf36

a numeric vector

Cf37

a numeric vector

Cf38

a numeric vector

Cg39

a numeric vector

Cg40

a numeric vector

Ch41

a numeric vector

Ch42

a numeric vector

Ci43

a numeric vector

Ci44

a numeric vector

Cj45

a numeric vector

Ck46

a numeric vector

Ck47

a numeric vector

Cl48

a numeric vector

Cm49

a numeric vector

Cm50

a numeric vector

Cm51

a numeric vector

Cm52

a numeric vector

Cm53

a numeric vector

Cm54

a numeric vector

Cm55

a numeric vector

Cm56

a numeric vector

Cm57

a numeric vector

Cm58

a numeric vector

Cn59

a numeric vector

Cn60

a numeric vector

Cn61

a numeric vector

Cn62

a numeric vector

Cn63

a numeric vector

Cn64

a numeric vector

Cn65

a numeric vector

Cn66

a numeric vector

Cn67

a numeric vector

Cn68

a numeric vector

Cn69

a numeric vector

Cn70

a numeric vector

Cn71

a numeric vector

Co72

a numeric vector

Co73

a numeric vector

Co74

a numeric vector

Co75

a numeric vector

Co76

a numeric vector

Co77

a numeric vector

Co78

a numeric vector

Co79

a numeric vector

Cp80

a numeric vector

Cq81

a numeric vector

Cq82

a numeric vector

Cq83

a numeric vector

Cq84

a numeric vector

Cq85

a numeric vector

Cq86

a numeric vector

Cq87

a numeric vector

Cq88

a numeric vector

Cq89

a numeric vector

Cq90

a numeric vector

Cq91

a numeric vector

Cq92

a numeric vector

Cq93

a numeric vector

Cq94

a numeric vector

Cq95

a numeric vector

Cq96

a numeric vector

Cq97

a numeric vector

Cr98

a numeric vector

Cr99

a numeric vector

Cr100

a numeric vector

Cr101

a numeric vector

Cs102

a numeric vector

Cs103

a numeric vector

Cs104

a numeric vector

Cs105

a numeric vector

Cs106

a numeric vector

Ct107

a numeric vector

C108

a numeric vector

C109

a numeric vector

C110

a numeric vector

C111

a numeric vector

SSa1

a numeric vector

SSd2

a numeric vector

SSd3

a numeric vector

SSd4

a numeric vector

SSd5

a numeric vector

SSd6

a numeric vector

SSd7

a numeric vector

SSd8

a numeric vector

SSf9

a numeric vector

SSg10

a numeric vector

SSj11

a numeric vector

SSj12

a numeric vector

SSj13

a numeric vector

SSl14

a numeric vector

SSm15

a numeric vector

SSm16

a numeric vector

SSn17

a numeric vector

SSn18

a numeric vector

SSn19

a numeric vector

SSn20

a numeric vector

SSn21

a numeric vector

SSn22

a numeric vector

SSn23

a numeric vector

SSn24

a numeric vector

SSn25

a numeric vector

SSn26

a numeric vector

SSn27

a numeric vector

SSn28

a numeric vector

SSn29

a numeric vector

SSn30

a numeric vector

SSn31

a numeric vector

SSn32

a numeric vector

SSn33

a numeric vector

SSn34

a numeric vector

SSn35

a numeric vector

SSo36

a numeric vector

SSo37

a numeric vector

SSp38

a numeric vector

SSq39

a numeric vector

SSq40

a numeric vector

SSt41

a numeric vector

SSu42

a numeric vector

Oa1

a numeric vector

Oc2

a numeric vector

Od3

a numeric vector

Od4

a numeric vector

Oe5

a numeric vector

Of6

a numeric vector

Of7

a numeric vector

Of8

a numeric vector

Of9

a numeric vector

Og10

a numeric vector

Og11

a numeric vector

Og12

a numeric vector

Og13

a numeric vector

Og14

a numeric vector

Og15

a numeric vector

Oi16

a numeric vector

Om17

a numeric vector

Om18

a numeric vector

Om19

a numeric vector

Om20

a numeric vector

Om21

a numeric vector

On22

a numeric vector

On23

a numeric vector

On24

a numeric vector

Oq25

a numeric vector

Oq26

a numeric vector

Oq27

a numeric vector

.u28

a numeric vector

Ov29

a numeric vector

Ov30

a numeric vector

O31

a numeric vector

O32

a numeric vector

O33

a numeric vector

Sa1

a numeric vector

Sb2

a numeric vector

Sb3

a numeric vector

Sd4

a numeric vector

Sd5

a numeric vector

Sd6

a numeric vector

Sd7

a numeric vector

Se8

a numeric vector

Si9

a numeric vector

Sm10

a numeric vector

Sm11

a numeric vector

S12

a numeric vector

S13

a numeric vector

Sx14

a numeric vector

Sx15

a numeric vector

Sx16

a numeric vector

Sx17

a numeric vector

Sy18

a numeric vector

Sz19

a numeric vector

S20

a numeric vector

S21

a numeric vector

S22

a numeric vector

S23

a numeric vector

S24

a numeric vector

S25

a numeric vector

SCd1

a numeric vector

SCd2

a numeric vector

SCd3

a numeric vector

SCd4

a numeric vector

SCd5

a numeric vector

SCd6

a numeric vector

SCd7

a numeric vector

SCm8

a numeric vector

SCn9

a numeric vector

SCn10

a numeric vector

SCw11

a numeric vector

SCx12

a numeric vector

SCx13

a numeric vector

SCx14

a numeric vector

SCx15

a numeric vector

SCx16

a numeric vector

SCy17

a numeric vector

SCy18

a numeric vector

SC19

a numeric vector

SC20

a numeric vector

SC21

a numeric vector

SC22

a numeric vector

SC23

a numeric vector

SC24

a numeric vector

SC25

a numeric vector

SC26

a numeric vector

SRd1

a numeric vector

SRd2

a numeric vector

SRd3

a numeric vector

SRd4

a numeric vector

SRf5

a numeric vector

SRf6

a numeric vector

SRf7

a numeric vector

SRj8

a numeric vector

SR9

a numeric vector

SR10

a numeric vector

Bd1

a numeric vector

Bn2

a numeric vector

Bn3

a numeric vector

Bn4

a numeric vector

Bt5

a numeric vector

Bx6

a numeric vector

Ha1

a numeric vector

Hg2

a numeric vector

Hn3

a numeric vector

Hq4

a numeric vector

Hq5

a numeric vector

TDd1

a numeric vector

TDf2

a numeric vector

TDj3

a numeric vector

TDn4

a numeric vector

TDq5

a numeric vector

TD6

a numeric vector

TD7

a numeric vector

TD8

a numeric vector

TD9

a numeric vector

Dc1

a numeric vector

Dg2

a numeric vector

Dh3

a numeric vector

Dk4

a numeric vector

Dm5

a numeric vector

Dm6

a numeric vector

D7

a numeric vector

D8

a numeric vector

D9

a numeric vector

D10

a numeric vector

D11

a numeric vector

D12

a numeric vector

D13

a numeric vector

Ta1

a numeric vector

Tc2

a numeric vector

Tc3

a numeric vector

Tc4

a numeric vector

Td5

a numeric vector

Tf6

a numeric vector

Tf7

a numeric vector

Tg8

a numeric vector

Th9

a numeric vector

To10

a numeric vector

T11

a numeric vector

T12

a numeric vector

T13

a numeric vector

T14

a numeric vector

T15

a numeric vector

T16

a numeric vector

CNg1

a numeric vector

CN2

a numeric vector

CN3

a numeric vector

CN4

a numeric vector

CN5

a numeric vector

CN6

a numeric vector

CN7

a numeric vector

CN8

a numeric vector

Ld1

a numeric vector

Lf2

a numeric vector

Lg3

a numeric vector

Lp4

a numeric vector

L5

a numeric vector

L6

a numeric vector

L7

a numeric vector

L8

a numeric vector

L9

a numeric vector

L10

a numeric vector

L11

a numeric vector

LS1

a numeric vector

LS2

a numeric vector

LL1

a numeric vector

LL2

a numeric vector

LL3

a numeric vector

LL4

a numeric vector

LL5

a numeric vector

EGd1

a numeric vector

EGf2

a numeric vector

CCd1

a numeric vector

CCn2

a numeric vector

CCn3

a numeric vector

EMc1

a numeric vector

EMd2

a numeric vector

EMd3

a numeric vector

EMf4

a numeric vector

EMf5

a numeric vector

EMn6

a numeric vector

EMx7

a numeric vector

EM8

a numeric vector

EM9

a numeric vector

EM10

a numeric vector

EM11

a numeric vector

EM12

a numeric vector

TE1

a numeric vector

TE2

a numeric vector

TE3

a numeric vector

TE4

a numeric vector

TE5

a numeric vector

BWe1

a numeric vector

BWn2

a numeric vector

BWn3

a numeric vector

TS1

a numeric vector

TS2

a numeric vector

TS3

a numeric vector

TS4

a numeric vector

TS5

a numeric vector

TS6

a numeric vector

TS7

a numeric vector

TS8

a numeric vector

TS9

a numeric vector

Pg1

a numeric vector

Pg2

a numeric vector

Pg3

a numeric vector

DUaa1

a numeric vector

DUw2

a numeric vector

DU3

a numeric vector

CP1

a numeric vector

CP2

a numeric vector

CP3

a numeric vector

CP4

a numeric vector

CP5

a numeric vector

CP6

a numeric vector

CP7

a numeric vector

CP8

a numeric vector

CP9

a numeric vector

CP10

a numeric vector

CP11

a numeric vector

CP12

a numeric vector

STd1

a numeric vector

STd2

a numeric vector

STd3

a numeric vector

STg4

a numeric vector

STaa5

a numeric vector

STaa6

a numeric vector

STaa7

a numeric vector

STaa8

a numeric vector

ST9

a numeric vector

ST10

a numeric vector

ST11

a numeric vector

ST12

a numeric vector

Wd1

a numeric vector

Wd2

a numeric vector

Wd3

a numeric vector

Wd4

a numeric vector

Wn5

a numeric vector

Waa6

a numeric vector

Waa7

a numeric vector

W8

a numeric vector

W9

a numeric vector

W10

a numeric vector

W11

a numeric vector

W12

a numeric vector

W13

a numeric vector

Zd1

a numeric vector

Zd2

a numeric vector

Zn3

a numeric vector

Zw4

a numeric vector

Zw5

a numeric vector

Zaa6

a numeric vector

Z7

a numeric vector

Z8

a numeric vector

Z9

a numeric vector

Z10

a numeric vector

Z11

a numeric vector

Z12

a numeric vector

CLd1

a numeric vector

CLd2

a numeric vector

CLd3

a numeric vector

CLd4

a numeric vector

CLd5

a numeric vector

CLd6

a numeric vector

CLd7

a numeric vector

CLd8

a numeric vector

CLd9

a numeric vector

CLd10

a numeric vector

CLd11

a numeric vector

CLd12

a numeric vector

CLd13

a numeric vector

CLd14

a numeric vector

CLd15

a numeric vector

CLd16

a numeric vector

CLd17

a numeric vector

CLd18

a numeric vector

CLd19

a numeric vector

CLd20

a numeric vector

CLd21

a numeric vector

CLd22

a numeric vector

CLd23

a numeric vector

CLd24

a numeric vector

CLd25

a numeric vector

CLd26

a numeric vector

CLd27

a numeric vector

CLd28

a numeric vector

CLd29

a numeric vector

CLd30

a numeric vector

CLd31

a numeric vector

CLd32

a numeric vector

CLd33

a numeric vector

CLd34

a numeric vector

CLd35

a numeric vector

CLd36

a numeric vector

CLd37

a numeric vector

CLd38

a numeric vector

CLn39

a numeric vector

CLn40

a numeric vector

CLn41

a numeric vector

CLn42

a numeric vector

CLn43

a numeric vector

CLn44

a numeric vector

CLn45

a numeric vector

CLn46

a numeric vector

CLn47

a numeric vector

CLn48

a numeric vector

CLw49

a numeric vector

CL50

a numeric vector

CL51

a numeric vector

CL52

a numeric vector

CL53

a numeric vector

CL54

a numeric vector

CL55

a numeric vector

CL56

a numeric vector

CL57

a numeric vector

CL58

a numeric vector

CL59

a numeric vector

Xd1

a numeric vector

Xd2

a numeric vector

Xd3

a numeric vector

Xd4

a numeric vector

Xd5

a numeric vector

Xd6

a numeric vector

Xd7

a numeric vector

Xd8

a numeric vector

Xd9

a numeric vector

Xd10

a numeric vector

Xd11

a numeric vector

Xd12

a numeric vector

Xd13

a numeric vector

Xf14

a numeric vector

Xk15

a numeric vector

Xn16

a numeric vector

Xn17

a numeric vector

Xn18

a numeric vector

Xn19

a numeric vector

Xn20

a numeric vector

Xn21

a numeric vector

Xn22

a numeric vector

Xn23

a numeric vector

Xn24

a numeric vector

Xn25

a numeric vector

Xn26

a numeric vector

Xn27

a numeric vector

Xn28

a numeric vector

Xn29

a numeric vector

Xn30

a numeric vector

Xn31

a numeric vector

Xn32

a numeric vector

Xp33

a numeric vector

Xp34

a numeric vector

Xp35

a numeric vector

Xq36

a numeric vector

Xq37

a numeric vector

Xq38

a numeric vector

X39

a numeric vector

X40

a numeric vector

X41

a numeric vector

X42

a numeric vector

X43

a numeric vector

X44

a numeric vector

X45

a numeric vector

X46

a numeric vector

X47

a numeric vector

X48

a numeric vector

X49

a numeric vector

X50

a numeric vector

Qd1

a numeric vector

Qe2

a numeric vector

Qe3

a numeric vector

Qh4

a numeric vector

Qh5

a numeric vector

Qh6

a numeric vector

Qh7

a numeric vector

Qh8

a numeric vector

Qh9

a numeric vector

Qn10

a numeric vector

Qn11

a numeric vector

Qt12

a numeric vector

Q13

a numeric vector

Q14

a numeric vector

Q15

a numeric vector

Q16

a numeric vector

Q17

a numeric vector

Q18

a numeric vector

Q19

a numeric vector

Q20

a numeric vector

Q21

a numeric vector

Q22

a numeric vector

TZd1

a numeric vector

TZf2

a numeric vector

TZh3

a numeric vector

TZ4

a numeric vector

CRd1

a numeric vector

CR2

a numeric vector

CR3

a numeric vector

EUd1

a numeric vector

EUd2

a numeric vector

EUg3

a numeric vector

EUm4

a numeric vector

EUw5

a numeric vector

EU6

a numeric vector

Ud1

a numeric vector

Ud2

a numeric vector

Ud3

a numeric vector

Uaa4

a numeric vector

U5

a numeric vector

Vd1

a numeric vector

V2

a numeric vector

V3

a numeric vector

V4

a numeric vector

V5

a numeric vector

LWE1

a numeric vector

LWE2

a numeric vector

Ad1

a numeric vector

Al2

a numeric vector

Am3

a numeric vector

An4

a numeric vector

Aw5

a numeric vector

Aaa6

a numeric vector

A7

a numeric vector

A8

a numeric vector

A9

a numeric vector

EVd1

a numeric vector

EVg2

a numeric vector

TK1

a numeric vector

ECL1

a numeric vector

EFe1

a numeric vector

EFm2

a numeric vector

EFm3

a numeric vector

EF4

a numeric vector

LPo1

a numeric vector

LPq2

a numeric vector

LP3

a numeric vector

LP4

a numeric vector

LP5

a numeric vector

PT1

a numeric vector

CSC

a numeric vector

CSR

a numeric vector

CCRC

a numeric vector

SA

a numeric vector

Anthrop

a numeric vector

Turtle

a numeric vector

Boat

a numeric vector

Canoe

a numeric vector

Hand

a numeric vector

Foot

a numeric vector

Lizard

a numeric vector

Crocodile

a numeric vector

Jellyfish

a numeric vector

Bird

a numeric vector

Anthrobird

a numeric vector

Axe

a numeric vector

Marine

a numeric vector

Face

a numeric vector

Zoo1

a numeric vector

Zoo2

a numeric vector

Zoo3

a numeric vector

Zoo4

a numeric vector

Zoo5

a numeric vector

Zoo6

a numeric vector