Contingency Table from a sample Survey

Description

Contingency table describing a survey of sources and quality of information about cancer for 1729 people.

Usage

data(contingency)

Format

A data frame with 32 observations on the following 6 variables.

source.1

a factor with levels N Y

source.2

a factor with levels N Y

source.3

a factor with levels N Y

source.4

a factor with levels N Y

knowledge

a factor with levels Good Poor

count

number of respondents with this pattern

Details

The sources of information are: (1) news media, (2) light reading, (3) solid reading, (4) lectures. The book (page 437) is fitting Bayesian loglinear models.

Examples

data(contingency)

Data from an Experiment with Treatment Assignment Based on Covariates

Description

An experiment was conducted on 50 cows to estimate the effect of a feed additive (methionine hydroxy analog) on six outcomes related to the amount of milk fat produced by each cow. Four diets (treatments) were considered, corresponding to different levels of the additive, and three variables were recorded before treatment assignment: lactation number ( seasons of lactation), age, and initial weight of cow. Multiple randomizations were calculated, and choosen that one with ‘best balane’, however that was defined.

Usage

data(cow)

Format

A data frame with 50 observations on the following 10 variables.

level

diet, treatment

lactation

lactation number, pretreatment

age

age of cow, pretreatment variable

initial.weight

initial weight, pretreatment

dry

response

milk

response

fat

response

solids

response

final.weight

response

protein

response

Examples

data(cow)
summary(cow)
names(cow)
# Investigating balance on pretreatment variables:
with(cow, tapply(lactation, level, mean))
with(cow, tapply(age, level, mean))

Serial Dilution Assay

Description

A serial dilution assay to study concentration of cockroach allergen in homes of asthma sufferes.

Usage

data(dilution)

Format

The format is: List of 4 \$ unknowns : num [1:8, 1:10] matrix with optical densities for unknowns in each column \$ standards : num [1:16] vector of standard solution optical densities \$ dil.unknowns : num [1:8] dilution factor for unknowns \$ dil.standards: num [1:16] dilution factor for standard solution

Details

Concentration of standard was 0.64

Examples

data(dilution)
str(dilution)
unknowns <- dilution$unknowns
standards <- dilution$standards
dil.unknowns <- dilution$dil.unknowns
dil.standards <- dilution$dil.standards
plot(dil.standards, standards)
matplot(dil.unknowns, unknowns, type="b")

Numbers of Faults Found in Each of 32 Rolls of Fabric

Description

Numbers of faults found in each of 32 rolls of fabric produced in a particular factory. Also given is the length of the roll.

Usage

data(fabric)

Format

A data frame with 32 observations on the following 2 variables.

length

length of roll

faults

number of faults in roll

Details

The book uses this for exercise 5. page 441

Examples

data(fabric)
str(fabric)
names(fabric)
# Identity link:
with(fabric, plot(faults ~ length))
# log link:
with(fabric, plot(faults ~ length, log="y"))
# Fitting poisson regression models:
mod1 <- glm(faults ~ length-1, data=fabric, family=poisson)
OK <- require(MCMCpack)
if(OK) mod2 <- MCMCpoisson(faults ~ length-1, data=fabric, b0=0, B0=0.0001)
summary(mod1)
confint(mod1)
if(OK) summary(mod2)
# The exercise is to investigate overdispersion ...

Data From a Chemical Experiment

Description

A factorial designed experiment from chemistry. Three experimental variables representing reactor conditions, and the response, conversion (%) from n-Heptane to acetylene.

Usage

data(factorial)

Format

A data frame with 16 observations on the following 4 variables.

temperature

reactor temperature

ratio

ratio of H2 to n-heptane (mole ratio)

contact

Contact time (sec)

conversion

the response, conversion from n-heptane to acetylene

Details

This data is used in an exercise on regression with many explanatory variables, page 413 of second edition. Original authors assume a quadratic functional form.

Examples

data(factorial)
summary(factorial)
# non-Bayesian analysis:
fac.mod1 <- lm(conversion ~ temperature+ratio+contact+
                            I(temperature*ratio)+I(temperature*contact)+
                            I(ratio*contact)+I(temperature^2)+I(ratio^2)+I(contact^2), 
                  data=factorial)
summary(fac.mod1)

Worldwide Airline Fatalities, 1976–1985

Description

Worldwide airline fatalities, 1976–1985. Death rate is passenger deaths per 100 million passenger miles.

Usage

data(fatalities)

Format

A data frame with 10 observations on the following 4 variables.

year

year

facc

number of fatal accidents

pdeaths

number of passenger deaths

rdeath

death rate

Details

Source: Statistical Abstracts of the United States

Examples

data(fatalities)
summary(fatalities)

Football Point Spreads and Game Outcomes

Description

Data on football point spreads and game outcomes (north american football) for ten seasons, 1981, 1983-1986, 1988-1992, each season are 224 games and they are strung together. Only three first seasons are used in chapter one of book.

Usage

data(football)

Format

A data frame with 2240 observations on the following 7 variables.

home

home indicator

favorite

favorite score

underdog

underdog score

spread

point spread

favorite.name

a factor with levels ATL BUF CHI CIN CLE DAL DEN DET GB HOU IND KC LAA LAN MIA MIN NE NO NYG NYJ PHA PHX PIT SD SEA SF TB WAS

underdog.name

a factor with levels ATL BUF CHI CIN CLE DAL DEN DET GB HOU IND KC LAA LAN MIA MIN NE NO NYG NYJ PHA PHX PIT SD SEA SF TB WAS

week

a numeric vector

Details

Football experts provide the point spread as a measure of the difference in ability between the two teams. For example, team A might be a 3.5 favourite to team B. The implication of this is that the proposition that team A, the favourite, defeats team B, the underdog, by 4 or more points, are considered a fair bet. In other words, the probability that A wins by more than 3.5 points is 0.5. If the point spread are an integer, then the implication is that team A is as likely to win by more points than the point spread as it is to win by fewer points than the point spread (or to loose). If the win is by exactly the point spread then neither side is paid off.

Examples

data(football)
summary(football)
names(football)
# In chapter 1 only three first seasons are used:
cap1 <- football[1:672, ]

Number of Attempts and Successes at Golf Putts

Description

Number of attempts and successes of golf putts, by distance from the hole, for a sample of professional golfers.

Usage

data(golf)

Format

A data frame with 19 observations on the following 3 variables.

distance

Distance from hole in feet

n

number of attempts

y

number of successes

Details

This is used for an exercise on nonlinear modelling on page 515 in the second edition.

Examples

data(golf)
names(golf)
comment(golf)
with(golf, plot(distance, y/n))

Simon Newcomb's Measurements for the Speed of Light

Description

Simon Newcomb's measurements (1882) to measure the speed of light. The data are recordes as deviations from 24800 nanoseconds.

Usage

data(light)

Format

The format is: atomic [1:66] 28 26 33 24 34 -44 27 16 40 -2 ... - attr(*, "comment")= chr "Units: deviations from 24800 nanoseconds"

Details

The currently accepted value for the speed of light on this scale is 33.0.

Examples

data(light)
comment(light)
hist(light, breaks=40)
abline(v=33.0, col="red")

Results of 22 Clinical Trials of beta-Blockers

Description

Results of 22 clinical trials of beta-blockers for reducing mortality after myocardial infection. Used for meta-analysis.

Usage

data(meta)

Format

A data frame with 22 observations on the following 5 variables.

study

id code of study

control.deaths

number of deaths in control group

control.total

total number of patients in control group

treated.deaths

number of deaths in treatment group

treated.total

total number of patients in treatment group

Details

The 22 clinical trials each consist of two groups of heart attack patients randomly allocated to receive or not receive beta-blockers ( a family of drugs that affect the central nervous system and can relax the heart musckles).

Examples

data(meta)
names(meta)
# Calculating empirical log-odds and its sampling variances:
y <- apply(meta, 1, function(x) log( (x[4]/(x[5]-x[4]))/(x[2]/(x[3]-x[2])) ) )
s2 <- apply(meta, 1, function(x) 1/(x[5]-x[4]) + 1/x[4] +1/(x[3]-x[2]) + 1/x[2] )  
cbind("Study number"=meta[,1], "empirical log odds"=y, "empirical sampling variance of y"=s2)
#if(require(meta)){
#   funnel(y, sqrt(s2))
#   radial(y, sqrt(s2))
#}

Population of Municipalities in New York

Description

Population in all 804 municipalities in new York state in 1960, and two independent random samples from it.

Usage

data(newyork)

Format

A data frame with 804 observations on the following 2 variables.

population

a numeric vector Population

code

400 if in sample 1, 300 if in sample 2, 200 if in both, and 100 if in neither

Details

Discussed on page 265 of second edition.

Examples

data(newyork)
str(newyork)

Personality Data From an Experiment in Psychology

Description

This is a tree-way array showing responses to 15 possible reactions in 23 situations for 54 persons. It is used in the book as an example for posterior predictive checks.

Usage

data(personality)

Format

The format is: num [1:15, 1:23, 1:54] 0 0 0 1 0 0 1 2 0 0 ... - attr(*, "dimnames")=List of 3 ..\$ response : chr [1:15] "1" "2" "3" "4" ... ..\$ situation: chr [1:23] "1" "2" "3" "4" ... ..\$ person : chr [1:54] "1" "2" "3" "4" ...

Examples

data(personality)
str(personality)
# Following code adapted from file personality3.R on the book's webpage:
nsubjects <- 6
nrep <- 7

test <- function (a){
  output <- as.vector(a)>0
  glm.data.frame <- data.frame (output, response, situation, person)
  glm0 <- glm (output ~
    factor(response) + factor(situation) + factor(person),
    family=binomial(link=logit),
    data=glm.data.frame)
  pred0 <- predict.glm (glm0, type="response")
  mean (ifelse(output, (1-pred0)^2, pred0^2))
}
data <- personality[,,1:nsubjects]
attrs <- attributes(data)
data <- ifelse (data>0, 1, 0)
attributes(data) <- attrs

CBS Telephone Survey

Description

Respondents to the CBS telephone survey classified by opinion, number of residential telephone lines, and number of adults in the household.

Usage

data(phones)

Format

A data frame with 27 observations on the following 7 variables.

adults

number of adults in household

preference

a factor with levels Bush Dukakis No opinion/other

lines.1

number with one tele line

lines.2

number with 2 tele lines

lines.3

number with 3 tele lines

lines.4

number with 4 tele lines

lines.Q

number with unknown tele lines

Details

This is used in exercises pages 242–243 in the book.

Examples

data(phones)     
summary(phones)

Tumor Incidence in Historical Control Groups and Current Group of Rats

Description

In the evaluation of drugs for possible clinical application, studies are routinely performed on rodents. For a particular study drawn from the statistical literature, suppose the immediate aim is to estimate theta, the probability of tumor in a population of female laboratory rats of type 'F344' that receive a zero dose of the drug — a control group. This gives data from historical control groups, and one current experimental control group.

Usage

data(rats)

Format

A data frame with 71 observations on the following 3 variables.

y

number of rats with tumors

N

number of rats in experiment

Current

a factor with levels 0 1

Examples

data(rats)
summary(rats)
# moment estimate of (alfa, beta) in beta distribution is (1.4, 8.6)
with(subset(rats, Current=="0"), hist( y/N, freq=FALSE))
plot(function(x) dbeta(x, 1.4, 8.6), from=0, to=1, col="red", add=TRUE)
# plotting posterior in same plot:
plot(function(x) dbeta(x, 5.4, 18.6), from=0, to=1, col="blue", add=TRUE)

Data on Response Times for Scizophrenics and non-Schizophrenics

Description

Response times (in milliseconds) for 11 non-schizophrenic and 6 schizophrenic individuals.

Usage

data(schiz)

Format

The format is: num [1:30, 1:17] 312 272 350 286 268 328 298 356 292 308 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:17] "nonsch1" "nonsch2" "nonsch3" "nonsch4" ...

A numerical matrix with individuals as columns.

Details

Psychological theory from the last half century and before suggests a model in which schizophrenics suffer from an attentional deficit on some trials, as well as a general motor reflex retardation.

Examples

data(schiz)
str(schiz)
# Making figure 18.1 in the book:
opar <- par(no.readonly=TRUE)
par( mar=c(2.0, 1,1,1))
par(mfrow=c(5,4))
for (i in 1:11) hist( log(schiz[,i]), main="", xlab="", ylab="", xlim=c(5.4, 7.5))
par( mfg=c(4, 1))
for (i in 1:6) hist( log(schiz[,11+i]), main="", xlab="", ylab="", xlim=c(5.4, 7.5))
par(opar)

Results of CBS News Survey of 1447 Adults in the United States

Description

Results of CBS News survey of 1447 adults in the United States, divided into 16 strata. The sampling is assumed to be proportional, so that the population proportions N_j/N, are approximately equal to the sampling proportions, n_j/n.

Usage

data(stratified)

Format

A data frame with 16 observations on the following 5 variables.

region

a character vector

bush

proportio declaring to vote for Bush

dukakis

proportion declaring to vote for Dukakis

other

proportion declaring to vote for other

proportion

sample proportion

Examples

data(stratified)
str(stratified)