Package {s20x}

s20x: Functions for University of Auckland Course STATS 201/208 Data Analysis

Description

The s20x package provides teaching-oriented helper functions and datasets for University of Auckland STATS 201 and STATS 208 data analysis courses. The package keeps student-facing defaults stable so existing lecture notes, labs, and examples continue to behave as expected.

Details

Selected diagnostic plotting helpers now support optional plotting engines. The default base graphics engine preserves the original teaching output. Use engine = "ggplot2" only when a reusable plot object is useful for saving, arranging, or further customisation. The optional engine requires the plotting packages documented on the relevant help pages: ggplot2 for normcheck(), eovcheck(), and modelcheck(), and both ggplot2 and GGally for pairs20x().

Author(s)

Maintainer: James Curran j.curran@auckland.ac.nz (Wrote the original R package. Current maintainer.)

Authors:

• Brant Deppa (Wrote the original R scripts this package is derived from)

Other contributors:

• Hannah Yun hyun536@aucklanduni.ac.nz [contributor]

• Rachel Fewster r.fewster@auckland.ac.nz [contributor]

• Russell Millar r.millar@auckland.ac.nz [contributor]

• Ben Stevenson ben.stevenson@auckland.ac.nz [contributor]

• Andrew Balemi a.balemi@auckland.ac.nz [contributor]

• Chris Wild c.wild@auckland.ac.nz [contributor]

• Sophie Jones [contributor]

• Dineika Chandra [contributor]

• Brendan McArdle [contributor]

See Also

Useful links:

• https://github.com/STATS-UOA/s20x

• Report bugs at https://github.com/STATS-UOA/s20x/issues

International Airline Passengers

Description

Number of international airline passengers (in thousands) recorded monthly from January 1949 to December 1960.

Format

A data frame with 144 rows and 4 variables:

passengers

Monthly total number of international airline passengers (in thousands).

t

Integer time index from 1 to 144.

month

Month of observation as a factor with levels Jan to Dec.

year

Year of observation as a factor with levels 1949 to 1960.

ANOVA tables for time series linear models

Description

Produces analysis-of-variance-style tables for 'tslm' objects.

Usage

## S3 method for class 'tslm'
anova(object, ..., verbose = FALSE)

Arguments

Details

For ordinary 'tslm()' fits without autoregressive error terms, 'anova()' returns the usual analysis of variance table from [stats::anova.lm()].

For AR-error models fitted through [nlme::gls()], the reported tests are Wald-style tests of model terms. These test whether each term contributes to the fitted mean model after allowing for the estimated autocorrelation structure. Because these models do not use the ordinary independent-error sum-of-squares decomposition, the compact table reports 'Df', 'F value', and 'Pr(>F)', but does not report 'Sum Sq' or 'Mean Sq'. Compare nested AR-error models with care: 'verbose = TRUE' exposes the underlying 'nlme' comparison output rather than recreating an ordinary 'lm' ANOVA table.

Use 'verbose = TRUE' to see the underlying [nlme::anova.gls()] output.

Value

An analysis-of-variance-style table.

Examples

data(beer.df)
fit = tslm(beer ~ t + ar(1), data = beer.df, time = t)
anova(fit)

Apples Data

Description

These data come from a classic long-term experiment conducted at the East Malling Research Station, Kent, which is the centre for research into apple growing in the U.K. Commercial apple trees consist of two parts grafted together. The lowest part, the rootstock, largely determines the size of the tree, while the upper part (the scion) determines the fruit characteristics. Rootstocks propagated by cuttings (i.e. asexually produced) were once thought to result in smaller trees than those propagated from seeds (i.e. sexually produced). This hypothesis was re-examined in an experiment begun in 1918. Several trees of each type of 16 types of rootstock were planted, all trees having the same scion. Rootstocks I-IX were asexually produced, while X-XVI were sexually produced. In the winter of 1933-4 a number of trees were removed to make room for more, and the data presented here consists of the above-ground weights of 104 trees felled in this period. No trees of types VIII, XI or XIV were felled. The description is adapted from Lee (1994). The data are from Andrews and Herzberg (1985).

Format

The data consist of a data frame with 104 observations on 4 variables.

Rootstock

Factor giving the rootstock type (I, II, III, IV, IX, V, VI, VII, X, XII, XIII, XV, XVI).

Weight

Integer Above-ground weight of tree (pounds, lb).

Weight_kg

Numeric Above-ground weight of tree (kilograms, kg); Weight_kg = Weight * 0.45359237.

Propagated

Factor giving the propagation method (cutting, seed).

References

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

Lee, A. J. (1994). Data Analysis: An Introduction Based on R. University of Auckland.

Changes in Pupil Size with Emotional Arousal

Description

Data from an experiment to measure the effect of different images on emotional arousal, by measuring changes in pupil diameter. The experiment used 20 males and 20 females. Images included a nude man, nude woman, infant, and a landscape.

Format

A data frame with 160 observations on 3 variables.

arousal

Numeric Change in the subject's pupil size.

gender

Factor Subject's gender (female, male)

picture

Factor Picture shown to subject (infant, landscape, nude female, nude male)

Deprecated autocorrelation plot alias

Description

Provides a deprecated compatibility alias for 'autocorPlot()'.

Usage

autocor.plot(fit, main = "Current vs Lagged residuals", ...)

Arguments

Value

Invisibly returns the result of 'autocorPlot()', called for its plotting side effect.

Autocorrelation Plot

Description

Plots current vs lagged residuals along with quadrants dividing these residuals about the value zero.

Usage

autocorPlot(fit, main = "Current vs Lagged residuals", ...)

Arguments

Value

Plots current vs lagged residuals along with quadrants dividing these residuals about the value zero.

Note

autocor.plot is deprecated and no longer exported. Use autocorPlot() in new code.

Examples

data(airpass.df)
time = 1:144
airpass.fit = lm(passengers ~ time, data = airpass.df)
autocorPlot(airpass.fit)

US Beer Production

Description

Monthly United States beer production figures (in millions of 31-gallon barrels) for the period July 1970 to June 1978.

Format

A data frame with 96 rows and 4 variables:

beer

Monthly beer production, expressed in megalitres (converted from millions of 31-US-gallon barrels; 1 million 31-gallon barrels is approximately equal to 117.35 megalitres).

t

Integer time index from 1 to 96.

month

Month of observation as a factor with levels Jul, Aug, Sep, Oct, Nov, Dec, Jan, Feb, Mar, Apr, May, Jun.

year

Year of observation as a factor with levels 1970 to 1978.

Note

The original primary source for this monthly beer-production series is not identified in the available package materials.

Body Image and Ethnicity Data

Description

This dataset originates from a study conducted at the University of Auckland in the early 1990s by Dr. R.A. Marshall and colleagues from the Department of Psychology. The research explored how cultural background and ethnic identity influence body image perceptions within the specific context of Aotearoa New Zealand.

Format

A data frame with 246 observations on 8 variables.

ethnicity

Factor Subject's ethnicity (Asian, Europn, Maori, Pacific)

married

Factor Whether the subject is married (no, yes)

bodyim

Factor Subject's rating of themself (slight.uw, right, slight.ow, mod.ow, very.ow)

sm.ever

Factor Whether the subject has ever smoked (no, yes)

weight

Numeric Weight in kg.

height

Numeric Height in cm.

age

Numeric Age in years.

stressgp

Factor Stress level group (low, medium, high)

Details

The study specifically focused on a cohort of women who were generally "thin" (slightly underweight for their body size). This was designed to investigate whether body dissatisfaction and varying self-perceptions persisted even among individuals who already met or approached Western "thin" ideals, and how these perceptions differed across Asian, European, Māori, and Pacific ethnic groups.

Source

Marshall, R.A., Department of Psychology, University of Auckland.

References

Lee, A. J. (1994). Data Analysis: An Introduction Based on R. University of Auckland.

Books Data

Description

This data consists of 50 sentence lengths from each of 8 books. The books “Disclosure” and “Rising Sun” were written by Michael Crichton, whilst the others “Four Past Midnight”, “The Dark Half”, “ Eye of the Dragon”, “The Shining”, “The Stand” and “The Tommy-Knockers” were written by Stephen King. The pages and sentences were chosen using a multistage design where the pages were selected at random, and then sentences within each page were selected at random. These data were collected by James Curran.

Format

The data frame consists of 400 observations on 2 variables.

length

Integer sentence length, measured as the number of words in the sentence.

book

Factor giving the book from which the sentence was sampled (4.Past.Mid, Dark.Half, Disclosure, Eye.Drag, Rising.Sun, Shining, Stand, T.Knock).

Deprecated box plots and normal quantile-quantile plots

Description

'boxqq()' is deprecated and is no longer exported. It draws boxplots and normal quantile-quantile plots of 'x' for each level of the grouping variable 'g'.

Usage

boxqq(formula, ...)

Arguments

Value

Returns the plot.

Note

This is a legacy teaching helper retained for compatibility with older course material. New teaching material should prefer current diagnostic plotting workflows.

Bursary Results for Auckland Secondary Schools

Description

Data for the 2001 Bursary results for 75 secondary schools in the Auckland area. For each school the decile rating of the school is recorded along with the percentage of eligible students who gain a B Bursary or better.

Format

A data frame with 75 observations on 2 variables.

decile

Numeric Decile rating of the school.

pass.rate

Numeric percentage of eligible students who gained a B Bursary or better.

Butterfat Data

Description

This data gives the mean percentage of butterfat produced by different Canadian pure-bred diary cattle. There are five different breeds and two age groups, two years old and greater than five years old. For each combination of breed and age, there are measurements for 10 cows.

Format

A data frame with 100 observations on 3 variables.

Butterfat

Numeric mean percentage of butterfat per cow.

Breed

Factor giving the cattle breed (ayrshire, canadian, guernesy, holst.fres, jersey).

Age

Factor giving the age group (2yo, mature).

Source

A Handbook of Small Data Sets

References

Hand, D.J., Daly, F., Lunn, A.D., McConway, K.J. and Ostrowski, E. (1994). A Handbook of Small Data Sets. Boca Raton, Florida: Chapman and Hall/CRC.

Sokal, R.R. and Rohlf, F.J. (1981). Biometry, 2nd edition. San Francisco: W.H. Freeman, 368.

Age and Length of Camp Lake Bluegills

Description

66 bluegills were captured from Camp Lake, Minnesota. For each bluegill we have the length of the fish, its age in years and its age in scale radius.

Format

A data frame with 66 observations on 3 variables.

Age

Numeric age of the fish, in years.

Scale.Radius

Numeric radius of the key scale, in hundredths of a millimetre.

Length

Numeric length at capture, in millimetres.

Capture an optional column name

Description

Convert a supplied symbol or character value into a column name for 'tslm()' internals.

Usage

captureOptionalName(argumentExpression)

Arguments

Value

A character name or 'NULL' when no name was supplied.

Render a case study to HTML

Description

Renders a specified case study R Markdown file shipped with the package to HTML and optionally opens it in a web browser.

Usage

casestudy(
  id,
  output_dir = tempfile("s20x_case_study_"),
  open = interactive(),
  quiet = TRUE,
  ...
)

cs(...)

Arguments

Details

Case studies are expected to live in inst/case_studies and to be named using the pattern CS<chapter>_<number>.Rmd (for example, CS9_2.Rmd).

The case study is rendered on demand using rmarkdown::render(). Figures and other outputs are generated at render time; users therefore need any required packages installed for the selected case study.

The rendered HTML file is returned invisibly.

Value

Invisibly returns the path to the rendered HTML file.

Examples

if (interactive()) {
  casestudy("CS9_2")
  casestudy("9.2")
  casestudy("9_2", outputDir = tempdir())
  cs("9_2")
}

Chalk Data

Description

These data involve 11 laboratories and 2 brands of chalk. The laboratories tested the density of the chalk. The main interest was whether the different laboratories yielded the same density for the two different types of chalk.

Format

A data frame with 66 observations on 3 variables.

Density

Numeric density of the chalk.

Lab

Integer laboratory identifier.

Chalk

Factor giving the chalk brand tested (A, B).

Confidence Intervals for Regression models

Description

Calculates and prints the confidence intervals for the fitted model.

Usage

ciReg(fit, conf.level = 0.95, print.out = TRUE)

Arguments

Value

The function returns a two-column matrix containing the upper and lower endpoints of the intervals.

See Also

lm, summary, anova.

Examples

##Peruvian Indians data
data(peru.df)
fit = lm(BP ~ age + years + weight + height, data = peru.df)
ciReg(fit)

Computer Questionnaire

Description

Data from a test to see if a questionnaire was properly designed. The questionnaire measures managers' technical knowledge of computers. The test has 19 managers complete the questionnaire as well as rate their own technical expertise.

Format

A data frame with 19 observations on 2 variables.

score

Numeric questionnaire score.

selfassess

Ordered factor giving the self-assessed level of expertise (1 = low, 2 = medium, 3 = high).

Cook's distance plot

Description

Draws a Cook's distance plot.

Usage

cooks20x(
  x,
  main = "Cook's Distance plot",
  xlab = "observation number",
  ylab = "Cook's distance",
  line = c(0.5, 1.2, 2),
  cex.labels = 1,
  axisOpts = list(xAxis = TRUE, yAxisTight = FALSE),
  ...
)

Arguments

Value

Returns the plot and identifies the three highest Cook's values

Examples

# Peruvian Indians data
data(peru.df)
peru.fit = lm(BP ~ age + years + I(years^2) + weight + height, data = peru.df)
cooks20x(peru.fit)

Stats 20x Summer School Data

Description

Data from a summer school Stats 20x course. Each observation represents a single student.

Format

A data frame with 146 observations on 15 variables.

Grade

Factor Final grade for the course (A, B, C, D)

Pass

Factor Passed the course (No, Yes)

Exam

Numeric Mark in the final exam.

Degree

Factor Degree enrolled in (BA, BCom, BSc, Other)

Gender

Factor Gender (Female, Male)

Attend

Factor Regularly attended class (No, Yes)

Assign

Numeric Assignment mark.

Test

Numeric Test mark.

B

Numeric Mark for the short answer section of the exam.

C

Numeric Mark for the long answer section of the exam.

MC

Numeric Mark for the multiple choice section of the exam.

Colour

Factor Colour of the exam booklet (Blue, Green, Pink, Yellow)

Stage1

Factor Stage one grade (A, B, C)

Years.Since

Numeric Number of years since doing Stage 1.

Repeat

Factor Repeating the paper (No, Yes)

Exam Mark, Gender and Attendance for Stats 20x Summer School Students

Description

Data from a summer school Stats 20x course. Each observation represents a single student. It is of interest to see if there is a relationship between a student's final examination mark and both their gender and whether they regularly attend lectures.

Format

A data frame with 40 observations on 3 variables.

Exam

Numeric Final exam mark (out of 100)

Gender

Factor Gender (Female, Male)

Attend

Factor Regularly attended or not (No, Yes)

Crossed Factors

Description

Computes a factor that has a level for each combination of the factors 'fac1' and 'fac2'.

Usage

crossFactors(x, fac2 = NULL, ...)

## Default S3 method:
crossFactors(x, fac2 = NULL, ...)

## S3 method for class 'formula'
crossFactors(formula, fac2 = NULL, data = NULL, ...)

Arguments

Value

Returns a vector containing the factor which represents the interaction of the given factors.

Methods (by class)

• crossFactors(default): Crossed Factors

• crossFactors(formula): Crossed Factors

Note

This function actually returns a factor now instead of a character string, so coercion into a factor is no longer necessary.

See Also

factor.

Examples

## arousal data:
data(arousal.df)
gender.picture = crossFactors(arousal.df$gender, arousal.df$picture)
gender.picture

## arousal data:
data(arousal.df)
gender.picture = crossFactors(~ gender * picture, data = arousal.df)
gender.picture

Crosstabulation of two variables

Description

Produces a 2-way table of counts and the corresponding chi-square test of independence or homogeneity.

Usage

crosstabs(formula, data)

Arguments

Value

Invisibly returns an object of class ct.20x, which is a list containing the following components:

Note

This is a legacy teaching helper retained for compatibility with older course material. New code should usually prefer table() and chisq.test() directly, or a purpose-built teaching wrapper.

Examples

##body image data:
data(body.df)
crosstabs(~ ethnicity + married, body.df)

Prices and Weights of Diamonds

Description

Prices of ladies' diamond rings from a Singaporean retailer and the weight of their diamond stones.

Format

A data frame with 48 observations on 2 variables.

price

Numeric Price of ring (Singapore dollars)

weight

Numeric Weight of Diamond (carats)

Display within-level pairwise comparisons for saturated two-way ANOVA model.

Description

Displays within-level pairwise comparisons from a two-way ANOVA with interactions. Note that this is just a display function: it ignores any cross-level pairs included in allpairs, even though these will have contributed to the computations for the Tukey adjustments. The purpose is just to organise the output from emmeans into a more convenient format.

Usage

displayPairs(allpairs, levels1, levels2, brief = TRUE, asDF = FALSE)

Arguments

Details

allpairs is a pairwise output from a command like pairs(emmeans(fit, ~factor1 * factor2)). If allpairs is not already a data.frame it will be converted to a data.frame within this function. It must contain a column called contrast with text descriptions like 'lev1 lev2 - lev3 lev4' etc. levels1 and levels2 are character strings specifying which within-level comparisons are wanted, and in which order. They must match the order specified in emmeans, so if using emmeans(fit, ~factor1 * factor2) then levels1 must belong to factor1 and levels2 must belong to factor2. All this function does is to pick out the rows of allpairs with the requested contrasts, so if there are no contrasts of the requested format (e.g. because levels1 and levels2 have been switched) it will output a blank list. If brief = TRUE, columns labelled df, SE, and t.ratio or z.ratio will be removed for a more succinct display. If asDF = TRUE, the output is returned as a data-frame suitable for further manipulation, whereas if asDF = FALSE it is returned as a list for display only.

Author(s)

Rachel Fewster

Examples

## Fit a two-way ANOVA to the arousal data in arousal.df.
## The factors are gender (female, male) and picture shown to
## subject (infant, landscape, nude.f, nude.m):
data(arousal.df)
arousal.fit = lm(arousal ~ gender *  picture, data = arousal.df)

## Create all pairwise comparisons using emmeans, if available.
if (requireNamespace("emmeans", quietly = TRUE)) {
    emmeansFun = getExportedValue("emmeans", "emmeans")
    arousal.allpairs = pairs(
        emmeansFun(arousal.fit, ~ gender * picture),
        infer = TRUE
    )

    ## Display only the within-level comparisons:
    displayPairs(
        arousal.allpairs,
        levels1 = c("female", "male"),
        levels2 = c("infant", "landscape", "nude.f", "nude.m")
    )
}

Draw row-distribution comparison plots

Description

Draws the plotting side effects used by 'rowdistr()' for the selected comparison mode.

Usage

drawPlot(
  crosstablist,
  comp = c("basic", "within", "between"),
  conf.level = 0.95
)

Arguments

Value

Called for its plotting side effects.

Testing for equality of variance plot

Description

Plots the residuals versus the fitted (or predicted) values from a linear model. A horizontal line is drawn at y = 0, reflecting the fact that we expect the residuals to have a mean of zero. An optional lowess line is drawn if smoother is set to TRUE. This can be useful in determining whether a trend still exists in the residuals. An optional pair of lines is drawn at +/- 2 times the standard deviation of the residuals - which is estimated from the Residual Mean Sqare (Within group mean square = WGMS). This can be useful in highlighting potential outliers. If the model has one or two factors and no continous variables, i.e. if it is a oneway or twoway ANOVA model, and levene = TRUE then the P-value from Levene's test for equality variance is displayed in the top left hand corner, as long as the number of observations per group exceeds two.

Usage

eovcheck(x, ...)

## S3 method for class 'formula'
eovcheck(
  x,
  data = NULL,
  xlab = "Fitted values",
  ylab = "Residuals",
  col = NULL,
  smoother = FALSE,
  twosd = FALSE,
  levene = FALSE,
  engine = c("base", "ggplot2"),
  ...
)

## S3 method for class 'lm'
eovcheck(
  x,
  smoother = FALSE,
  twosd = FALSE,
  levene = FALSE,
  engine = c("base", "ggplot2"),
  ...
)

Arguments

Details

The default base graphics engine preserves the original teaching plot and draws directly on the active graphics device. The optional ggplot2 engine is intended for users who want a reusable plot object for reports or further customisation; it requires ggplot2 to be installed and returns a ggplot object instead of drawing a base graphics side effect.

Value

Draws the residual-versus-fitted diagnostic plot when using the base engine. With engine = "ggplot2", returns a ggplot object.

See Also

levene.test

Examples

# one way ANOVA - oysters
data(oysters.df)
oyster.fit = lm(Oysters ~ Site, data = oysters.df)
eovcheck(oyster.fit)

# Same model as the previous example, but using eovcheck.formula
data(oysters.df)
eovcheck(Oysters ~ Site, data = oysters.df)


# A two-way model without interaction
data(soyabean.df)
soya.fit = lm(yield ~ planttime + cultivar, data = soyabean.df)
eovcheck(soya.fit)

# A two-way model with interaction
data(arousal.df)
arousal.fit = lm(arousal ~ gender * picture, data = arousal.df)
eovcheck(arousal.fit)

# A regression model
data(peru.df)
peru.fit = lm(BP ~ height + weight + age + years, data = peru.df)
eovcheck(peru.fit)


# A time series model
data(airpass.df)
t = 1:144
month = factor(rep(1:12, 12))
airpass.df = data.frame(passengers = airpass.df$passengers, t = t, month = month)
airpass.fit = lm(log(passengers)[-1] ~ t[-1] + month[-1]
                 + log(passengers)[-144], data  = airpass.df)
eovcheck(airpass.fit)

# Optional ggplot2 engine for reusable plot objects
if (requireNamespace("ggplot2", quietly = TRUE)) {
  eovPlot = eovcheck(oyster.fit, engine = "ggplot2")
  class(eovPlot)

  eovcheck(peru.fit, engine = "ggplot2", smoother = TRUE)
  eovcheck(oyster.fit, engine = "ggplot2", twosd = TRUE, levene = TRUE)
}

Contrast Estimates

Description

Calculates and prints Tukey multiple confidence intervals for contrasts in one or two-way ANOVA.

Usage

estimateContrasts(
  contrast.matrix,
  fit,
  row = TRUE,
  alpha = 0.05,
  L = NULL,
  FUN = identity
)

Arguments

Value

Returns a matrix whose rows correspond to the different contrasts being estimated and whose columns correspond to the point estimate of the contrast, the Tukey lower and upper limits of the confidence interval, the unadjusted p-value, and the Tukey and Bonferroni p-values.

See Also

[summary1way()], [summary2way()], [multipleComp()]

Examples

## computer data:
data(computer.df)
computer.df = within(computer.df, {selfassess = factor(selfassess)})
computer.fit = lm(score ~ selfassess, data = computer.df)
contrast.matrix = matrix(c(-1 / 2, -1 / 2, 1), byrow = TRUE, nrow = 1, ncol = 3)
contrast.matrix
estimateContrasts(contrast.matrix, computer.fit)

Estimate one-factor contrasts

Description

Internal implementation for contrast estimation from one-factor linear models.

Usage

estimateContrasts1(contrast.matrix, fit, alpha = 0.05, L, FUN)

Arguments

Value

A matrix of contrast estimates and Tukey-adjusted p-values.

Estimate two-factor contrasts

Description

Internal implementation for contrast estimation from balanced two-factor linear models.

Usage

estimateContrasts2(contrast.matrix, fit, alpha = 0.05, row = TRUE, L, FUN)

Arguments

Value

A matrix of contrast estimates and Tukey-adjusted p-values.

Extract a tslm error specification

Description

Extract and validate the supported autoregressive error term from parsed formula terms.

Usage

extractTslmErrorSpec(termsObject)

Arguments

Value

'NULL' for independent errors, or a list describing the AR error structure.

Extract the underlying tslm fit

Description

Return the underlying fitted model from a 'tslm' object, or the input model unchanged.

Usage

extractTslmFit(model)

Arguments

Value

A fitted model object.

Fire Damage and Distance from the Fire Station

Description

House damage and distance from the fire station, of 15 house fires. Data collected by an insurance company for homes in a particular area.

Format

A data frame with 15 observations on 3 variables.

damage

Numeric Damage (1000s of dollars)

distance

Numeric Distance from the fire station (miles)

distance_km

Numeric Distance from the fire station (kilometres); distance_km = distance * 1.60934.

Format a tslm ANOVA table

Description

Convert the raw AR-error ANOVA table into the compact teaching table.

Usage

formatTslmAnovaTable(rawTable)

Arguments

Value

A data frame with compact ANOVA columns.

Format a tslm residual type label

Description

Convert an internal residual type into plot-label text.

Usage

formatTslmResidualTypeLabel(type)

Arguments

Value

A sentence-case residual type label.

Analysis of 1-dimensional frequency tables

Description

If hypothprob is absent: prints confidence intervals for the true proportions, a Chi-square test for uniformity, confidence intervals for differences in proportions (with no corrections for multiple comparisons), and plots the proportions.

Usage

freq1way(
  counts,
  hypothprob,
  conf.level = 0.95,
  addCIs = TRUE,
  digits = 4,
  arrowwid = 0.1,
  estimated = 0
)

Arguments

Details

If hypothprob is present: prints confidence intervals for the true proportions, a Chi-square test for the hypothesised probabilities, and plots the sample proportions (with attached confidence limits) alongside the corresponding hypothesised probabilities.

Value

An invisible list containing the following components:

Note

These confidence intervals have been Bonferroni adjusted for multiple comparisons. This is a legacy teaching helper retained for compatibility with older course material.

Examples

##Body image data:
data(body.df)
eth.table = with(body.df, table(ethnicity))
freq1way(eth.table)
freq1way(eth.table,hypothprob=c(0.2,0.4,0.3,0.1))

Fruitfly Data

Description

This data gives fecundity for female fruitflies, Drosophila melanogaster. The fecundity is the number of eggs laid, per day, for the fruitfly's first 14 days of life. There are three strains: A control group, NS, Nonselected Strain, as well as RS, a strain bred for resistance to DDT and SS, a strain bred for susceptibility to DDT. Each strain contains 25 measurements. It is of interest to compare the level of fecundity across strains.

Format

A data frame with 75 observations on 2 variables.

fecundity

Numeric Number of eggs laid, per day, per fruitfly.

strain

Factor Strain of fruitfly (NS, RS, SS)

Source

A Handbook of Small Data Sets

References

Hand, D.J., Daly, F., Lunn, A.D., McConway, K.J. and Ostrowski, E. (1994). A Handbook of Small Data Sets. Boca Raton, Florida: Chapman and Hall/CRC.

Sokal, R.R. and Rohlf, F.J. (1981). Biometry, 2nd edition. San Francisco: W.H. Freeman, 239.

Get model residual and fitted-value data

Description

Extract residuals and fitted values from a fitted model and validate that the two vectors are aligned for diagnostic plotting.

Usage

getModelResidualFittedData(object, residualType = NULL, context = "model")

Arguments

Value

A list with 'fitted' and 'residuals' components.

Get tslm autoregressive parameters

Description

Extract fitted autoregressive parameters from a 'tslm' object when present.

Usage

getTslmArParameters(object)

Arguments

Value

A named numeric vector of AR parameters.

Get a tslm coefficient table

Description

Extract a coefficient table from either an 'lm' or 'gls' summary object.

Usage

getTslmCoefficientTable(fitSummary)

Arguments

Value

A coefficient matrix.

Get tslm diagnostic data

Description

Collect fitted values, residuals, and time values for 'tslm' diagnostic plots.

Usage

getTslmDiagnosticData(object, residualType = "normalised")

Arguments

Value

A list with 'fitted', 'residuals', and 'time' components.

Get tslm error terms

Description

Identify formula term labels that use a supported 'tslm()' error-structure form.

Usage

getTslmErrorTerms(termsObject)

Arguments

Value

A character vector of error-structure term labels.

Get tslm residual degrees of freedom

Description

Extract residual degrees of freedom from a fitted model and its summary.

Usage

getTslmResidualDf(fit, fitSummary)

Arguments

Value

The residual degrees of freedom, or 'NA_integer_' if unavailable.

Get tslm time values

Description

Extract or reconstruct the time values used by 'tslm' diagnostic plots.

Usage

getTslmTimeValues(object, nResiduals)

Arguments

Value

A vector of time or observation-order values.

s20x package version number

Description

Returns the version number of the s20x package. This is useful if a student has problems running commands and the maintainer needs to check the version number.

Usage

getVersion()

Examples

getVersion()

Sale and Advertised Prices of Houses

Description

A random sample of 100 houses recently sold in Mt Eden, Auckland. For each house we have the advertised price and the actual sale price.

Format

A data frame with 100 observations on 2 variables.

advertised.price

Numeric Advertised price (dollars)

sell.price

Numeric Final sale price (dollars)

Mean Family Incomes

Description

Random sample of 152 families giving their mean income (1000s of dollars). The sample was taken by an advertising agency over their area of operations.

Format

A data frame with 152 observations on 1 variable.

incomes

Numeric mean family income, in thousands of dollars.

Interactions Plot for Two-way Analysis of Variance

Description

Displays data with intervals for each combination of the two factors and shows the mean differences between levels of the first factor for each level of the second factor. Note that there should be more than one observation for each combination of factors.

Usage

interactionPlots(y, ...)

## Default S3 method:
interactionPlots(
  y,
  fac1 = NULL,
  fac2 = NULL,
  xlab = NULL,
  xlab2 = NULL,
  ylab = NULL,
  data.order = TRUE,
  exlim = 0.1,
  jitter = 0.02,
  conf.level = 0.95,
  interval.type = c("tukey", "hsd", "lsd", "ci"),
  pooled = TRUE,
  tick.length = 0.1,
  interval.distance = 0.2,
  col.width = 2/3,
  xlab.distance = 0.1,
  xlen = 1.5,
  ylen = 1,
  ...
)

## S3 method for class 'formula'
interactionPlots(
  y,
  data = NULL,
  xlab = NULL,
  xlab2 = NULL,
  ylab = NULL,
  data.order = TRUE,
  exlim = 0.1,
  jitter = 0.02,
  conf.level = 0.95,
  interval.type = c("tukey", "hsd", "lsd", "ci"),
  pooled = TRUE,
  tick.length = 0.1,
  interval.distance = 0.2,
  col.width = 2/3,
  xlab.distance = 0.1,
  xlen = 1.5,
  ylen = 1,
  ...
)

Arguments

Methods (by class)

• interactionPlots(default): Interactions Plot for Two-way Analysis of Variance

• interactionPlots(formula): Interactions Plot for Two-way Analysis of Variance

See Also

summary2way.

Examples

data(arousal.df)
interactionPlots(arousal ~ gender + picture, data = arousal.df)

## This usage is deprecated.
with(arousal.df, interactionPlots(arousal, gender, picture))

Check a tslm error term

Description

Check whether a formula term label is a supported 'tslm()' error-structure term.

Usage

isTslmErrorTerm(termLabel)

Arguments

Value

'TRUE' when the term label is an error-structure term; otherwise 'FALSE'.

Ages and Lengths of Lake Mary Bluegills

Description

The ages and lengths of 78 bluegills captured from Lake Mary, Minnesota.

Format

A data frame with 78 observations on 2 variables.

Age

Numeric Age of the fish (years)

Length

Numeric Length at capture (mm)

Los Angeles Rainfall

Description

Annual rainfall (in inches) for Los Angeles from 1908 to 1973.

Format

A data frame with 66 rows and 4 variables:

LA.Rain

Annual rainfall in Los Angeles, measured in inches.

rain_mm

Annual rainfall in Los Angeles, measured in millimetres (mm); rain_mm = LA.Rain * 25.4.

t

Integer time index from 1 to 66.

year

Year of observation as an integer from 1908 to 1973.

Layout

Description

Allows a 'numRows' by 'numCols' matrix of plots to be displayed in a single plot. If the function is called with no arguments, then the plotting device layout will be reset to a single plot.

Usage

layout20x(numRows = 1, numCols = 1)

Arguments

Value

No return value.

Note

This is a legacy convenience wrapper retained for compatibility with older teaching material. New code can use par(mfrow = ...) directly.

Examples

data(course.df)
layout20x(1, 2)
stripchart(course.df$Exam)
boxplot(course.df$Exam)

Levene test for the ANOVA Assumption

Description

Perform a Levene test for equal group variances in both one-way and two-way ANOVA. A table with the results is (normally) displayed.

Usage

levene.test(formula, data, digit = 5, show.table = TRUE)

Arguments

Value

A list with the following elements:

See Also

crossFactors, anova.

Examples

##
data(computer.df)
levene.test(score ~ factor(selfassess), computer.df)

List available case studies

Description

Lists all case study R Markdown files shipped with the package and prints them as a formatted text table.

Usage

listCaseStudies()

listCS()

lcs()

Details

Case studies are expected to live in inst/case_studies and to be named using the pattern CS<chapter>_<number>.Rmd (e.g. CS9_2.Rmd).

The table has two columns: File (the case study identifier) and Title (extracted from the YAML header). Case studies are listed in numerical order, not alphabetical order.

The function invisibly returns a character vector of case study identifiers.

Value

Invisibly returns a character vector of case study identifiers.

Examples

if (interactive()) {
  listCaseStudies()
  ids = listCaseStudies()
}

Make tslm model data

Description

Build the model frame used internally by 'tslm()'.

Usage

makeTslmModelData(meanFormula, data, timeName = NULL)

Arguments

Value

A model frame containing the mean-model variables and, when supplied, the time variable.

Match a plotting engine argument

Description

Applies the standard plotting engine argument matching used by exported plotting functions. Keeping this in one place makes engine-dispatch cleanup stages less repetitive without changing the accepted engine values.

Usage

matchPlottingEngine(engine, choices = c("base", "ggplot2"))

Arguments

Value

The matched plotting engine.

Match a tslm residual type

Description

Match and normalise residual type aliases used by 'tslm' diagnostics.

Usage

matchTslmResidualType(type)

Arguments

Value

The matched residual type used internally.

Year and Price of Mazda Cars

Description

Prices and ages of 124 Mazda cars collected from the Melbourne Age newspaper in 1991.

Format

A data frame with 124 observations on 2 variables.

price

Numeric Price (Australian dollars)

year

Numeric Year of manufacture.

Monthly Notifications of Meningococcal Disease

Description

This data shows the monthly number of notifications meningococcal disease in New Zealand from January 1990 to December 2001.

Format

A data frame with 144 observations on 3 variables.

Month

Factor giving the month of notification.

Year

Factor giving the year of notification.

mening

Numeric number of notifications of meningococcal disease.

Merger Days

Description

A random selection of 38 consummated mergers from the USA, 1982, giving the number of days between the date the merger was announced and the date the merger became effective.

Format

A data frame with 38 observations on 1 variable.

mergerdays

Numeric number of days between the merger announcement and the effective date.

Deprecated model checking plots

Description

'modcheck()' is deprecated and is no longer exported. It plots four model checking plots: residuals versus fitted values, a normal Q-Q plot, a histogram of residuals with a normal distribution superimposed, and a Cook's distance plot.

Usage

modcheck(x, ...)

Arguments

Value

Draws the selected model checking plots for teaching diagnostics. The function is called for its plotting side effects and does not provide a stable data return object.

Model checking plots

Description

Draw the teaching diagnostic plots used by older 's20x' workflows. 'modelcheck()' is retained as an exported compatibility helper for model checking, while newer teaching material may use focused diagnostic helpers such as [eovcheck()], [normcheck()], and [cooks20x()] directly.

Usage

modelcheck(x, ...)

## S3 method for class 'lm'
modelcheck(
  x,
  which = 1:3,
  mar = c(3, 4, 1.5, 4),
  engine = c("base", "ggplot2"),
  ...
)

Arguments

Details

The default base graphics engine preserves the original teaching plots and draws directly on the active graphics device. The optional ggplot2 engine is intended for users who want reusable plot objects for reports or further customisation; it requires ggplot2 to be installed and returns ggplot objects instead of drawing base graphics side effects.

Value

Draws diagnostic plots for teaching model checking when using the base engine. With engine = "ggplot2", returns a ggplot object for a single selected plot, or a named list of ggplot objects for multiple selected plots.

Examples

data(peru.df)
lmFit = lm(BP ~ weight, data = peru.df)

# Plot residuals versus fitted values only
modelcheck(lmFit, 1)

# Plot residuals versus fitted values, histogram, and Q-Q plot
modelcheck(lmFit, 1:2)

# Plot all diagnostics
modelcheck(lmFit)

# Optional ggplot2 engine for reusable plot objects
if (requireNamespace("ggplot2", quietly = TRUE)) {
  diagnosticPlots = modelcheck(lmFit, engine = "ggplot2")
  names(diagnosticPlots)

  modelcheck(lmFit, which = 1, engine = "ggplot2")
  modelcheck(lmFit, which = 2, engine = "ggplot2")
  modelcheck(lmFit, which = 3, engine = "ggplot2")
}

Length of Mozart's Movements

Description

Length of movements from 11 of Mozart's early symphonies and 11 of his late symphonies.

Format

A data frame with 88 observations on 3 variables.

Time

Numeric Time of each movement (seconds)

Movement

Factor Movement (M1, M2, M3, M4)

Period

Factor Period that the symphony was written (early, late)

Multiple Comparisons

Description

Calculates and prints the estimate, multiple 95% confidence intervals, unadjusted, Tukey and Bonferroni p-values for all possible differences in means in a one-way ANOVA.

Usage

multipleComp(fit, conf.level = 0.95, FUN = identity)

Arguments

Value

Returns a list of estimates, confidence intervals and p-values.

Examples

## computer data
data(computer.df)
fit = lm(score ~ factor(selfassess), data = computer.df)
multipleComp(fit)

## butterfat data
data("butterfat.df")
fit = lm(log(Butterfat) ~ Breed, data = butterfat.df)
multipleComp(fit, FUN = exp)

Nail Polish Data

Description

These data were collected to determine whether quick drying nail polish or regular nail polish dried faster. The time for each type of nail polish to dry was recorded.

Format

A data frame with 60 observations on 2 variables.

polish

Factor Type of polish (Regular, Quick)

dry

Integer Time (in seconds) for the polish to dry.

Testing for normality plot

Description

Plots two plots side by side. First, it draws a normal Q-Q plot of the residuals, along with a line with intercept equal to the mean of the residuals and slope equal to the standard deviation of the residuals. If shapiro.wilk = TRUE, the P-value from the Shapiro-Wilk test for normality is shown in the top-left corner of the Q-Q plot. Second, it draws a histogram of the residuals. A normal distribution is fitted and superimposed over the histogram. Note: if you want to leave the x-axis blank in the histogram then use xlab = c("Theoretical Quantiles", " ") , i.e. leave a space between the quotes. If you do not leave a space, information will be extracted from x.

Usage

normcheck(x, ...)

## Default S3 method:
normcheck(
  x,
  xlab = c("Theoretical Quantiles", ""),
  ylab = c("Sample Quantiles", ""),
  main = c("", ""),
  col = "light blue",
  bootstrap = FALSE,
  B = 5,
  bpch = 3,
  bcol = "lightgrey",
  shapiro.wilk = FALSE,
  whichPlot = 1:2,
  usePar = TRUE,
  engine = c("base", "ggplot2"),
  ...
)

## S3 method for class 'lm'
normcheck(
  x,
  xlab = c("Theoretical Quantiles", ""),
  ylab = c("Sample Quantiles", ""),
  main = c("", ""),
  col = "light blue",
  bootstrap = FALSE,
  B = 5,
  bpch = 3,
  bcol = "lightgrey",
  shapiro.wilk = FALSE,
  whichPlot = 1:2,
  usePar = TRUE,
  engine = c("base", "ggplot2"),
  ...
)

## S3 method for class 'tslm'
normcheck(
  x,
  xlab = c("Theoretical Quantiles", ""),
  ylab = c("Sample Quantiles", ""),
  main = c("", ""),
  col = "light blue",
  bootstrap = FALSE,
  B = 5,
  bpch = 3,
  bcol = "lightgrey",
  shapiro.wilk = FALSE,
  whichPlot = 1:2,
  usePar = TRUE,
  residualType = "normalised",
  engine = c("base", "ggplot2"),
  ...
)

Arguments

Details

The default base graphics engine preserves the original teaching plots and draws directly on the active graphics device. The optional ggplot2 engine is intended for users who want reusable plot objects for reports or further customisation; it requires ggplot2 to be installed and returns ggplot objects instead of drawing base graphics side effects.

Value

Draws the selected normality diagnostic plots when using the base engine. With engine = "ggplot2", returns a ggplot object for a single selected plot or a named list of ggplot objects for multiple selected plots. When multiple ggplot2 plots are selected, printing the returned object draws the plots side by side to match the base graphics teaching layout.

See Also

shapiro.test.

Examples

# Synthetic teaching example: an exponential growth curve
set.seed(123)
e = rnorm(100, 0, 0.1)
x = rnorm(100)
y = exp(5 + 3 * x + e)
fit = lm(y ~ x)
normcheck(fit)

# An exponential growth curve with the correct transformation
fit = lm(log(y) ~ x)
normcheck(fit)

# Same example as above except we use normcheck.default
normcheck(residuals(fit))

# Peruvian Indians data
data(peru.df)
peruFit = lm(BP ~ weight, data = peru.df)
normcheck(peruFit)

# Optional ggplot2 engine for reusable plot objects
if (requireNamespace("ggplot2", quietly = TRUE)) {
  normPlots = normcheck(peruFit, engine = "ggplot2")
  names(normPlots)

  normcheck(peruFit, engine = "ggplot2", whichPlot = 1)
  normcheck(peruFit, engine = "ggplot2", whichPlot = 2)
}

Quarterly Alcohol Available for Consumption in New Zealand

Description

Quarterly alcohol available for consumption in New Zealand from 1935 to 2021. The data give volumes of alcoholic beverages available for consumption, grouped into broad beverage categories.

Format

A data frame with quarterly observations on 4 variables.

year

Integer Year.

month

Ordered factor giving the month at the end of the quarter.

volume

Numeric volume available for consumption, in million litres.

category

Factor beverage category: 'Total beer', 'Total wine', or 'Total spirits'.

Details

The 'month' variable gives the month ending the quarter. It should be treated in calendar order for plotting and summaries. For this quarterly data set the intended order is March, June, September, and December.

The 'category' variable has three levels:

'Total beer'

Total beer available for consumption.

'Total wine'

Total wine available for consumption.

'Total spirits'

Total spirits and spirit-based drinks available for consumption.

Source

Stats NZ, Alcohol available for consumption: Year ended December 2021.

Monthly Arrivals to New Zealand

Description

Monthly international passenger arrivals to New Zealand from January 1921 to February 2026. Missing monthly observations, if present in the source series, are retained as rows with missing 'arrivals.count' values.

Format

A data frame with monthly observations on 3 variables.

year

Integer year.

month

Factor month abbreviation with levels given by 'month.abb'.

arrivals.count

Integer number of international passenger arrivals.

Source

Stats NZ Infoshare, table ITM049AA, Total passenger movements (monthly), Arrivals, Actual Counts. Last updated 14 April 2026.

One-way Analysis of Variance Plot

Description

Displays stripplot/boxplot of the reponse variable with intervals by factor levels. It is used as part of a one-way ANOVA analysis.

Usage

onewayPlot(x, ...)

## Default S3 method:
onewayPlot(
  x,
  f,
  conf.level = 0.95,
  interval.type = "tukey",
  pooled = TRUE,
  strip = TRUE,
  vert = TRUE,
  verbose = FALSE,
  ylabel = deparse(terms(formula)[[2]]),
  flabel = deparse(terms(formula)[[3]]),
  ...
)

## S3 method for class 'formula'
onewayPlot(
  formula,
  data = parent.frame(),
  conf.level = 0.95,
  interval.type = "tukey",
  pooled = TRUE,
  strip = TRUE,
  vert = TRUE,
  verbose = FALSE,
  ylabel = deparse(terms(formula)[[2]]),
  flabel = deparse(terms(formula)[[3]]),
  ...
)

## S3 method for class 'lm'
onewayPlot(x, ..., ylabel = nms[1], flabel = nms[2])

Arguments

Methods (by class)

• onewayPlot(default): One-way Analysis of Variance Plot

• onewayPlot(formula): One-way Analysis of Variance Plot

• onewayPlot(lm): One-way Analysis of Variance Plot

See Also

summary1way, t.test.

Examples

##see example in 'summary1way'

##computer data:
data(computer.df)
onewayPlot(score~selfassess, data = computer.df)


##apple data:
data(apples.df)
twosampPlot(Weight~Propagated, data = apples.df)

##oyster data:
data(oysters.df)
onewayPlot(log(Oysters)~Site, data = oysters.df)

##oyster data:
data(oysters.df)
oyster.fit = lm(log(Oysters)~Site, data = oysters.df)
onewayPlot(oyster.fit)

Open a case study source file in the editor

Description

Opens a case study .Rmd file for interactive use. The file shipped inside the package is copied to dest_dir (so it is writable), then opened in the RStudio editor when available (otherwise the system editor).

Usage

openCaseStudy(id, dest_dir = getwd(), overwrite = FALSE, ...)

opencs(id, dest_dir = getwd(), overwrite = FALSE, ...)

ocs(id, dest_dir = getwd(), overwrite = FALSE, ...)

Arguments

Value

Invisibly returns the path to the copied file.

Examples

if (interactive()) {
  openCaseStudy("2.1")
  openCaseStudy("2.1", destDir = tempdir())
}

Oyster Abundances over Different Sites

Description

Data from an experiment to determine the abundance of oysters recruiting from three sites in two different estuaries in New South Wales. One in Georges River and two in Port Stephens. The number of oysters was recorded for 10 cm by 10 cm panels over a two year period.

Format

A data frame with 87 observations on 2 variables.

Oysters

Numeric number of oysters on each experimental panel.

Site

Factor giving the location of the experimental panels (GR = Georges River, PS1 = first Port Stephens site, PS2 = second Port Stephens site).

Pairwise Scatter Plots with Histograms and Correlations

Description

Plots pairwise scatter plots with histograms and correlations for the data frame.

Usage

pairs20x(x, na.rm = TRUE, engine = c("base", "ggplot2"), ...)

Arguments

Details

The default base graphics engine preserves the original s20x teaching plot and draws directly on the active graphics device. The optional ggplot2 engine uses GGally when both optional packages are installed and returns a reusable plot matrix for reports or further customisation. The ggplot2/GGally output is intentionally optional so existing teaching material can continue to rely on the base graphics default.

Value

Returns the plot.

See Also

'pairs', 'panel.smooth', 'panel.cor', 'panel.hist'

Examples

## Peruvian Indians
data(peru.df)
pairs20x(peru.df)

# Optional ggplot2/GGally engine for a reusable plot matrix
if (requireNamespace("ggplot2", quietly = TRUE) &&
    requireNamespace("GGally", quietly = TRUE)) {
  pairsPlot = pairs20x(peru.df, engine = "ggplot2")
  class(pairsPlot)
}

Parse a tslm formula

Description

Separate the mean-model formula from a supported 'tslm()' error structure.

Usage

parseTslmFormula(formula)

Arguments

Value

A list containing 'meanFormula' and 'errorSpec'.

Peruvian Indians

Description

A random sample of Peruvian Indians born in the Andes mountains, but who have since migrated to lower altitudes. The sample was collected to assess the long term effects of altitude on blood pressure.

Format

A data frame with 39 observations on 5 variables.

age

Numeric Subject's age.

years

Numeric Number of years since migration.

weight

Numeric Subject's weight (kg)

height

Numeric Subject's height (mm)

BP

Numeric Subject's systolic blood pressure (mm Hg; standard clinical unit in New Zealand).

Plot tslm residuals against fitted values

Description

Draw the residuals-versus-fitted diagnostic panel for 'tslm' objects.

Usage

plotTslmResiduals(diagnosticData, residualType = "normalised", ...)

Arguments

Value

Called for its plotting side effect.

Plot tslm residuals over time

Description

Draw the residuals-over-time diagnostic panel for 'tslm' objects.

Usage

plotTslmTimeResiduals(diagnosticData, object, residualType = "normalised", ...)

Arguments

Value

Called for its plotting side effect.

Deprecated Teaching Predictions for a Linear Model

Description

Teaching helper for linear-model predictions. It wraps predict.lm and prints a compact table containing fitted values, confidence intervals for the mean response, and prediction intervals for new observations.

Usage

predict20x(object, newdata, cilevel = 0.95, digit = 3, print.out = TRUE, ...)

Arguments

Details

This is not an S3 predict() method and is not intended to be a drop-in replacement for base R prediction methods. It is a compatibility helper for older teaching material that expects confidence and prediction intervals to be printed together. The standard predict interface is preferred for new work.

Note: newdata must be a data frame with the same column order and data types as those used in fitting the model. This is stricter than the usual predict.lm() interface and is kept for compatibility with the original teaching wrapper.

Value

Invisibly returns a list with components

frame

printed data frame containing predictions, confidence intervals, and prediction intervals.

fit

prediction values.

se.fit

standard errors of predictions.

residual.scale

residual standard deviation.

df

residual degrees of freedom.

cilevel

confidence level of the interval.

Note

This function is deprecated because it is no longer used in class. Prefer the standard predict method for new work.

See Also

predict, predict.lm, as.data.frame.

Examples

# Zoo data
data(zoo.df)
zoo.df = within(zoo.df, {day.type = factor(day.type)})
zoo.fit = lm(log(attendance) ~ time + sun.yesterday + nice.day + day.type + tv.ads,
             data = zoo.df)
pred.zoo = data.frame(time = 8, sun.yesterday = 10.8, nice.day = 0,
                      day.type = factor(3), tv.ads = 1.181)
predict20x(zoo.fit, pred.zoo)

# Peruvian Indians data
data(peru.df)
peru.fit = lm(BP ~ age + years + I(years^2) + weight + height, data = peru.df)
pred.peru = data.frame(age = 21, years = 2, `I(years^2)` = 2, weight = 71, height = 1629)
predict20x(peru.fit, pred.peru)

Predicted Counts for a Log-Link Generalised Linear Model

Description

Teaching helper for count predictions from a log-link generalised linear model. It wraps predict.glm, constructs confidence intervals on the link scale, exponentiates the fitted values and limits, rounds the result, and optionally prints the returned table.

Usage

predictCount(object, newdata, cilevel = 0.95, digit = 3, print.out = TRUE, ...)

Arguments

Details

This is not an S3 predict() method and is not intended to be a drop-in replacement for base R prediction methods. It is a specialised count-focused teaching wrapper. For a more general log-link or logit-link GLM helper, see predictGLM.

Note: newdata must be a data frame with the same column order and data types as those used in fitting the model. This stricter interface is kept for compatibility with the original teaching wrapper.

Value

Invisibly returns a data frame with three columns:

Predicted

the predicted count on the response scale.

Conf.lower

the lower confidence limit on the response scale.

Conf.upper

the upper confidence limit on the response scale.

See Also

predict, predict.glm, predictGLM, as.data.frame.

Prediction Intervals for Log-Link and Logit-Link Generalised Linear Models

Description

Teaching helper for predictions from log-link and logit-link generalised linear models. It wraps predict.glm with standard errors and returns fitted values with confidence limits on either the link scale or the response scale.

Usage

predictGLM(object, newdata, type = "link", cilevel = 0.95, quasit = FALSE, ...)

Arguments

Details

This is not an S3 predict() method and is not intended to be a drop-in replacement for base R prediction methods. It is the more general GLM teaching helper in this package; predictCount remains a specialised count-focused wrapper with rounded response-scale output.

Note: newdata must include all first-order terms used in the fitted model. This simplified requirement reflects the teaching-wrapper interface and is not a complete reproduction of predict.glm().

Value

A data frame with columns fit, lwr, and upr containing fitted values and confidence limits on the requested scale.

See Also

predict, predict.glm, predictCount.

Prepare row-distribution table summaries

Description

Converts a two-way count table into the row, column, whole-table, and total summaries used by 'rowdistr()'.

Usage

prepCrosstabList(crosstablist)

Arguments

Value

A list containing row proportions, column proportions, whole-table proportions, and totals.

Print ggplot2 modelcheck plots

Description

Draws multiple ggplot2 modelcheck plots together so the optional ggplot2 engine gives a single printed diagnostic display rather than showing list structure at the console.

Usage

## S3 method for class 's20xModelcheck_ggplot2'
print(x, ...)

Arguments

Value

Invisibly returns x.

Print ggplot2 normcheck plots

Description

Draws multiple ggplot2 normcheck plots side by side so the optional ggplot2 engine mirrors the base graphics layout for the default whichPlot = 1:2 case.

Usage

## S3 method for class 's20xNormcheck_ggplot2'
print(x, ...)

Arguments

Value

Invisibly returns x.

Print row-distribution summaries

Description

Prints the teaching summaries used by 'rowdistr()' for the selected comparison mode.

Usage

printOutput(
  crosstablist,
  comp = c("basic", "within", "between"),
  conf.level = 0.95
)

Arguments

Value

Invisibly returns the row-proportion matrix printed in the summary.

LSD-Display Intervals

Description

This function is called by rowdistr.

Usage

propslsd.new(crosstablist, conf.level = 0.95, arrowlength = 0.1)

Arguments

Note

This is an internal legacy helper used by rowdistr(). It is not exported and should not be called directly by users.

See Also

crosstabs, rowdistr

Cloud Seeding and Levels of Rainfall

Description

Data from an experiment to see if seeding clouds with Silver Nitrate effects the amount of rainfall.

Format

A data frame with 50 observations on 3 variables.

rain

Numeric amount of rain, measured in acre-feet (the volume of water required to cover one acre of land to a depth of one foot).

rain_ML

Numeric amount of rain expressed in megalitres (ML); rain_ML = rain * 1.23348184.

seed

Factor indicating whether the clouds were seeded (seeded, unseeded).

Remove tslm error terms

Description

Remove supported error-structure terms from the formula used for the fitted mean model.

Usage

removeTslmErrorTerms(formula, termsObject)

Arguments

Value

A formula containing only the mean-model terms.

Require an optional plotting package

Description

Checks that an optional plotting package is installed and gives a consistent error message for optional plotting engines.

Usage

requirePlottingPackage(package, engine = "ggplot2")

Arguments

Value

Invisibly returns TRUE when the package is available.

Require a suggested package

Description

Check that a suggested package is installed before optional functionality uses it.

Usage

requireSuggestedPackage(package)

Arguments

Value

Invisibly returns 'TRUE', or errors if the package is unavailable.

Fitted values versus residuals plot

Description

Plots a scatter plot for the variables of the residuals and fitted values from the linear model, lmfit. A lowess smooth line for the underlying trend, as well as one standard deviation error bounds for the scatter about this trend, are added to this scatter plot. A test for a quadratic relationship between the residuals and the fitted values is also computed.

Usage

residPlot(lmfit, f = 0.5)

Arguments

Value

Returns the plot.

Note

This is a legacy diagnostic plotting helper retained for compatibility with older teaching material. New code should usually prefer the current diagnostic workflow used by modelcheck().

See Also

trendscatter

Examples

# Peruvian Indians data
data(peru.df)
fit = lm(BP ~ age + years + weight + height, data = peru.df)
residPlot(fit)

Resolve case-study destination directory

Description

Normalise legacy and camelCase destination-directory arguments for 'openCaseStudy()'.

Usage

resolveCaseStudyDestinationDir(dest_dir = getwd(), ...)

Arguments

Value

A single destination-directory path.

Resolve case-study output arguments

Description

Normalise legacy and camelCase output-directory arguments for 'casestudy()'.

Usage

resolveCaseStudyOutputArgs(output_dir, outputDirWasSupplied, ...)

Arguments

Value

A list containing 'outputDir' and remaining 'renderArgs'.

Row distributions from a cross-tabulation of two variables

Description

Produces summaries and plots from a cross-tabulation. The output produced depends on the parameter 'comp'. Columns relate to response categories and rows to different populations.

Usage

rowdistr(
  crosstablist,
  comp = c("basic", "within", "between"),
  conf.level = 0.95,
  plot = TRUE,
  suppressText = FALSE
)

Arguments

Details

The 'basic' option (default) produces the response distribution for each row population together with comparative bar charts.

If comp = 'between' the resulting output displays how the probability of falling into a response class (column) differs between populations. Confidence intervals for differences in proportions are produced together with a set of barcharts with LSD intervals.

If comp = 'within' the resulting output shows the extent to which the component probabilities of the same row distribution differ. Separate Chi-square tests for uniformity are produced for each row distribution as are confidence intervals for differences in proportions within the same distribution.

Arguments plot and suppressText are really only used when producing knitr or Sweave documents so that just the plot or just the text can be displayed in the document.

Value

Invisibly returns the matrix of row proportions printed by the teaching summary when suppressText = FALSE. When suppressText = TRUE, the function invisibly returns NULL because no text summary is constructed. Plotting remains a side effect controlled by plot.

See Also

crosstabs

Examples

data(body.df)
z = crosstabs(~ ethnicity + married, data = body.df)
rowdistr(z)
rowdistr(z, comp = "between")
rowdistr(z, comp = "within")

## from matrix of counts
z = matrix(c(4, 3, 2, 6, 47, 20, 40, 62, 11, 8, 7, 22, 3, 0, 1, 10), 4, 4)
rowdistr(z)

Read Data

Description

For internal use.

Usage

rr()

Build a base-like ggplot2 theme

Description

Keeps optional ggplot2 diagnostic plots visually close to the original teaching plots by removing the default grey panel and grid.

Usage

s20x_ggplot2_base_theme()

Value

A ggplot2 theme object.

Save graphics parameters for later restoration

Description

Captures a graphics-parameter state and returns a closure that restores it. This helper centralises the common 'par()'/'on.exit()' pattern used by diagnostic plotting functions.

Usage

saveGraphicsParameters(..., noReadonly = FALSE)

Arguments

Value

A function that restores the saved graphics parameters and invisibly returns them.

Seeds Data

Description

These data record the number of seeds (out of 100) that germinated when given different amounts of water. The seeds were either exposed to light or kept in the dark. Four identical boxes were used for each combination of water and light

Format

A data frame with 48 observations on 3 variables.

Light

Factor indicating whether the seeds were exposed to light (N = No, Y = Yes).

Water

Integer amount of water, with higher levels corresponding to more water (1, 2, 3, 4, 5, 6).

Count

Integer number of seeds that germinated, out of 100.

Convert text to sentence case

Description

Capitalise the first character of a string used in diagnostic labels.

Usage

sentenceCase(x)

Arguments

Value

'x' with the first character capitalised.

Sheep Data

Description

Weight measurements for sheep under combinations of copper and cobalt supplementation.

Format

A data frame with 100 observations on 3 variables.

Weight

Integer Weight of sheep (kilograms, kg).

Copper

Factor indicating whether copper supplementation was given (No, Yes).

Cobalt

Factor indicating whether cobalt supplementation was given (No, Yes).

Skewness Statistic

Description

Calculates the skewness statistic of the data in 'x'. Values close to zero correspond to reasonably symmetric data, positive values of this measure indicate right-skewed data whereas negative values indicate left-skewness.

Usage

skewness(x, ...)

Arguments

Value

Returns the value of the skewness.

Examples

## Merger data:
data(mergers.df)
skewness(mergers.df$mergerdays)

Skulls Data

Description

Male Egyptian skulls from five different epochs. Each skull has had four measurements taken of it, BH, Basibregmatic Height, BL, Basialveolar Length, MB, Maximum Breadth and NH, Nasal Height. It is of interest to investigate the change in shape over time. A gradual change, would indicate inbreeding of the populations. This data only includes the maximum breadth measurements.

Format

A data frame with 150 observations on 2 variables.

measurement

Integer maximum breadth measurement of the skull.

year

Integer epoch year group for the skull.

Source

A Handbook of Small Data Sets

References

Hand, D.J., Daly, F., Lunn, A.D., McConway, K.J. and Ostrowski, E. (1994). A Handbook of Small Data Sets. Boca Raton, Florida: Chapman and Hall/CRC.

Thomson, A. and Randall-Maciver, R. (1905). Ancient Races of the Thebaid. Oxford: Oxford University Press.

Snapper Weight Data

Description

Weight and length measurements of 844 snapper (Pagrus auratus) caught in the Hauraki Gulf, near Auckland, New Zealand.

Format

A data frame with 844 observations on 2 variables.

len

Numeric fork length in centimetres. Fork length is measured from the tip of the snout to the fork of the tail.

wgt

Numeric weight of the fish, in kilograms.

Source

Russell Millar, University of Auckland.

Soya Bean Yields

Description

Data from an experiment to examine the effects of different planting times on the yield of soya beans, given four different cultivars.

Format

A data frame with 32 observations on 3 variables.

yield

Numeric Yield of each plant.

cultivar

Factor Cultivar used (cult1, cult2, cult3, cult4)

planttime

Factor Month of planting (Novemb, Decemb)

Source

Littler, R. University of Waikato

Deprecated strip charts and normal quantile-quantile plots

Description

'stripqq()' is deprecated and is no longer exported. It draws strip charts and normal quantile-quantile plots of 'x' for each level of the grouping variable 'g'.

Usage

stripqq(formula, ...)

Arguments

Note

This is a legacy teaching helper retained for compatibility with older course material. New teaching material should prefer current diagnostic plotting workflows.

One-way Analysis of Variance Summary

Description

Displays summary information for a one-way anova analysis. The lm object must come from a numerical response variable and a single factor. The output includes: (i) anova table; (ii) numeric summary; (iii) table of effects; (iv) plot of data with intervals.

Usage

summary1way(
  fit,
  digit = 5,
  conf.level = 0.95,
  inttype = "tukey",
  pooled = TRUE,
  print.out = TRUE,
  draw.plot = TRUE,
  ...
)

Arguments

Value

Invisibly returns a list containing the one-way ANOVA summary components used in the printed teaching output. The list contains:

The printed ANOVA table, numeric summary, effects table, and optional plot are the primary teaching interface. The returned list is invisible so classroom use can focus on the printed output while programmatic callers can still inspect the computed values.

See Also

summary2way, anova, aov, dummy.coef, onewayPlot

Examples

## Computer questionnaire data:
data(computer.df)
computer.df = within(computer.df, {
    selfassess = factor(selfassess)
})
computer.fit = lm(score ~ selfassess, data = computer.df)
result = summary1way(computer.fit)
result

Two-way Analysis of Variance Summary

Description

Displays summary information for a two-way anova analysis. The lm object must come from a numerical response variable and factors. The output depends on the value of page:

Usage

summary2way(
  fit,
  page = c("table", "means", "effects", "interaction", "nointeraction"),
  digit = 5,
  conf.level = 0.95,
  print.out = TRUE,
  new = TRUE,
  all = FALSE,
  FUN = "identity",
  ...
)

Arguments

Details

• page = "table": ANOVA table.

• page = "means": cell means matrix and numeric summary.

• page = "effects": table of effects.

• page = "interaction": interaction contrast tables.

• page = "nointeraction": main-effect contrast tables.

Value

'summary2way()' prints the requested teaching summary page and invisibly returns the current summary components. The returned list has the following components:

.

See Also

summary1way, model.tables, TukeyHSD

Examples

## Arousal data:
data(arousal.df)
arousal.fit = lm(arousal ~ gender * picture, data = arousal.df)
summary2way(arousal.fit)

## Butterfat data:
data("butterfat.df")
fit = lm(log(Butterfat) ~ Breed + Age, data = butterfat.df)
summary2way(fit, page = "nointeraction", FUN = exp)

Summary Statistics

Description

Produces a table of summary statistics for the data. If the argument group is missing, calculates a matrix of summary statistics for the data in x. If group is present, the elements of group are interpreted as group labels and the summary statistics are displayed for each group separately.

Usage

summaryStats(x, ...)

## Default S3 method:
summaryStats(
  x,
  group = rep("Data", length(x)),
  data.order = TRUE,
  digits = 2,
  ...
)

## S3 method for class 'formula'
summaryStats(x, data = NULL, data.order = TRUE, digits = 2, ...)

## S3 method for class 'matrix'
summaryStats(x, data.order = TRUE, digits = 2, ...)

Arguments

Value

A teaching summary is printed as a side effect. The returned value is invisible so that classroom use can focus on the printed summary while programmatic use can still save the result.

If x is a single variable and no grouping is supplied, an invisible list is returned with the following named items:

If grouping is provided, either by using the group argument, by using a formula, or by passing a matrix whose columns represent groups, the function invisibly returns a data.frame with one row for each group and columns containing the summary statistics.

Methods (by class)

• summaryStats(default): Summary Statistics

• summaryStats(formula): Summary Statistics

• summaryStats(matrix): Summary Statistics

Examples

## STATS20x data:
data(course.df)

## Single variable summary
with(course.df, summaryStats(Exam))

## Using a formula
summaryStats(Exam ~ Stage1, course.df)

## Using a matrix
courseMatrix = cbind(course.df$Exam, course.df$Assign, course.df$Test)
summaryStats(courseMatrix)

## Saving and extracting the information
sumStats = summaryStats(Exam ~ Degree, course.df)
sumStats

## Just the BAs
sumStats['BA', ]

## Just the means
sumStats$mean

Comparison of Three Teaching Methods

Description

Data from an experiment to assess the impact of three different teaching methods on language ability. 30 students were randomly allocated into three groups, one for each method. The students' IQ before instruction and a language test score after instruction were recorded.

Format

A data frame with 30 observations on 3 variables.

lang

Numeric Language test score after instruction.

IQ

Numeric Student's IQ.

method

Factor Teaching method (1, 2, 3)

Technitron Salary Information

Description

Salary information for all salaried employees of the Technitron Company.

Format

A data frame with 46 observations on 8 variables.

salary

Numeric Annual Salary (dollars)

yrs.empl

Numeric Number of years employed at Technitron.

prior.yrs

Numeric Number of years prior experience.

educ

Numeric Years of education after high school.

id

Numeric Company identification number.

gender

Numeric Gender (0 = female, 1 = male)

dept

Numeric Department employee works in (1 = Sales, 2 = Purchasing, 3 = Advertising, 4 = Engineering)

super

Numeric Number of employees supervised.

Effect of a New Drug on Thyroid Weights

Description

Data from an experiment to assess the effect of a new drug on the weight of the thyroid gland using 16 laboratory animals. The animals were randomly assigned into either a control group, or a treatment group, and each animal had its bodyweight recorded at the beginning of the experiment and its thyroid weight measured at the end of the experiment.

Format

A data frame with 16 observations on 3 variables.

thyroid

Numeric Weight of thyroid gland after 7 days (mg)

body

Numeric Animal body weight before experiment began (g)

group

Factor Animal's group (1 = control, 2 = drug)

Crest Toothpaste

Description

Two random samples of households, one of households who purchase Crest toothpaste and one of households who do not. For each household the age is recorded of the person responsible for purchasing the toothpaste.

Format

A data frame with 20 observations on 2 variables.

purchasers

Numeric Age of the person in the household responsible for purchases of Crest.

nonpurchasers

Numeric Age of the person in the household responsible for purchases of other brands of toothpaste.

Trend and scatter plot

Description

Plots a scatter plot for the variables x, y along with a lowess smooth for the underlying trend. One standard deviation error bounds for the scatter about this trend are also plotted.

Usage

trendscatter(x, ...)

## Default S3 method:
trendscatter(x, y = NULL, f = 0.5, xlab = NULL, ylab = NULL, main = NULL, ...)

## S3 method for class 'formula'
trendscatter(
  x,
  f = 0.5,
  data = NULL,
  xlab = NULL,
  ylab = NULL,
  main = NULL,
  ...
)

Arguments

Value

Returns the plot.

Methods (by class)

• trendscatter(default): Trend and scatter plot

• trendscatter(formula): Trend and scatter plot

See Also

residPlot

Examples

# Synthetic teaching example: a simple polynomial
set.seed(123)
x = rnorm(100)
e = rnorm(100)
y = 2 + 3 * x - 2 * x^2 + 4 * x^3 + e
trendscatter(y ~ x)

# Synthetic teaching example: an exponential growth curve
e = rnorm(100, 0, 0.1)
y = exp(5 + 3 * x + e)
trendscatter(log(y) ~ x)

# Peruvian Indians data
data(peru.df)
trendscatter(BP ~ weight, data = peru.df)

# Note: this usage is deprecated
with(peru.df, trendscatter(weight, BP))

Fit a linear model with optional autoregressive errors

Description

'tslm()' is a teaching-friendly wrapper for fitting linear models with optional AR(p) error structures. Students specify the mean model using an ordinary formula and add an 'ar(p)' term to request autoregressive errors.

Usage

tslm(formula, data = parent.frame(), time, method = "REML", ...)

Arguments

Details

When no 'ar(p)' term is present, 'tslm()' fits an ordinary [stats::lm()] model. When an 'ar(p)' term is present, 'tslm()' fits a [nlme::gls()] model with an AR(p) correlation structure using [nlme::corARMA()]. The 'ar(p)' term changes the error model, not the mean-model terms printed in the formula.

The formula describes the mean model, just as it does for [stats::lm()]. The special term 'ar(p)' is removed from the mean model before fitting and is used only to specify the correlation structure for the errors. For example, 'log(passengers) ~ t + month + ar(1)' fits a trend and seasonal mean model with AR(1) errors.

For AR-error models, 'time' should usually name the variable giving the time order of the observations. If 'time' is omitted, 'tslm()' fits the model using the row order of the model data and gives a warning so that this assumption is visible.

Diagnostic methods for AR-error models use normalised residuals by default, because these residuals account for the fitted correlation structure. Use 'residualType = "response"' when the raw response residuals are required. '"normalised"' and '"normalized"' are both accepted for compatibility.

Value

An object of class 'tslm', containing the original formula, the mean formula fitted internally, the AR order, the time variable if supplied, and the underlying fitted model.

See Also

[stats::lm()], [nlme::gls()], [nlme::corARMA()]

Examples

data(beer.df)
fit = tslm(beer ~ t + ar(1), data = beer.df, time = t)
coef(fit)

data(airpass.df)
fitAr = tslm(log(passengers) ~ t + month + ar(1),
  data = airpass.df,
  time = t
)
summary(fitAr)
anova(fitAr)

plot(fitAr)
plot(fitAr, residualType = "response")

Zoo Attendance during an Advertising Campaign

Description

Data for 455 days of attendance records for Auckland Zoo, from January 1, 1993. Note that only 440 values are given due to missing values. It was of interest to assess whether an advertising campaign was effective in increasing attendance.

Format

A data frame with 440 observations on 6 variables.

attendance

Numeric Number of visitors.

time

Numeric Time in days since the start of the study.

sun.yesterday

Numeric Hours of sunshine the previous day.

tv.ads

Numeric Average spending on TV advertising in the previous week (1000s of dollars per day)

nice.day

Factor Assessment based on number of hours of sunshine (0 = No, 1 = Yes)

day.type

Factor Type of day (1 = ordinary weekday, 2 = weekend day, 3 = school holiday weekday, 4 = public holiday)