Package 'languageR'

Description

Data sets and functions accompanying 'Analyzing Linguistic Data: A practical introduction to statistics', Cambridge University Press, 2007.

Details

The main function of this package is to make available the data sets discussed and analyzed in 'Analyzing Linguistic Data: A practical introduction to statistics using R', to appear with Cambridge University Press. The following packages should be installed, as ancillary functions in this package depend on them.

zipfR

for word frequency distributions

lme4

for mixed-effects models

coda

for Markov-Chain Monte Carlo estimation

lattice

for trellis graphics

Matrix

for mixed-effects modeling

The following packages need to be installed for working through specific examples.

rms

for regression modeling

rpart

for CART trees

e1071

for support vector machines

MASS

for many useful functions

ape

for phylogenetic clustering

The main convenience functions in this library are, by category:

correspondence analysis

(extending code by Murtagh, 2005)

corres.fnc

correspondence analysis

corsup.fnc

supplementary data

vocabulary richness

(supplementing current zipfR functionality)

compare.richness.fnc

for two texts, compare richness

growth.fnc

empirical vocabulary growth data for text

growth2vgc

conversion to vgc object of zipfR

spectrum.fnc

creates frequency spectrum

text2spc.fnc

conversion to spc object of zipfR

lmer functions

(p-values for mixed-effects models with lme4)

pvals.fnc

p-values for table of coefficients including MCMC

aovlmer.fnc

p-values for anova tables and/or MCMC p-value for specified factor

simulation functions

(for comparing mixed models with traditional techniques including F1, F2, and F1+F2)

simulateRegression.fnc

simulate simple regression design

simulateQuasif.fnc

simulate data for Quasi-F ratios

simulateLatinsquare.fnc

simulating simple Latin-square design

miscellaneous

(convenience functions)

pairscor.fnc

scatterplot matrix with correlation tests

collin.fnc

collinearity diagnostics

pvals.fnc

p-values and MCMC confidence intervals for mixed models

plot.logistic.fit.fnc

diagnostic visualization for logistic models

xylowess.fnc

trellis scatterplots with smoother

mvrnormplot.fnc

scatterplot for bivariate standard normal random numbers with regression line

lmerPlotInt.fnc

offers choice of four ways to visualize an interaction between two numeric predictors in an lmer model

Author(s)

R. H. Baayen

University of Alberta, Edmonton, Canada

[email protected]

Maintainer: [email protected]

References

R. H. Baayen (2007) Analyzing Linguistic Data: A practical introduction to statistics using R, Cambridge: Cambridge University Press.

Examples

## Not run: 
  library(languageR)
  data(package="languageR")

## End(Not run)

Description

This function creates a trellis plot with autocorrelation functions for by-subject sequential dependencies in response latencies.

Usage

acf.fnc(dat, group="Subject", time="Trial", x = "RT", plot=TRUE, ...)

Arguments

Value

If plot=TRUE, a trellis graph, otherwise a data frame with as column names

Author(s)

R. H. Baayen

References

R. H. Baayen (2001) Word Frequency Distributions, Dordrecht: Kluwer.

See Also

lags.fnc

Examples

## Not run: 
data(beginningReaders)
acf.fnc(beginningReaders, x="LogRT")   # autocorrelations even though nonword responses not included

## End(Not run)

Description

Affix productivity, gauged by the P* productivity measure, for 27 English affixes in 44 texts.

Usage

data(affixProductivity)

Format

A data frame with 44 observations on the following 30 variables.

semi

a numeric vector of P*-values

anti

a numeric vector of P*-values

ee

a numeric vector of P*-values

ism

a numeric vector of P*-values

ian

a numeric vector of P*-values

ful

a numeric vector of P*-values

y

a numeric vector of P*-values

ness

a numeric vector of P*-values

able

a numeric vector of P*-values

ly

a numeric vector of P*-values

unV

a numeric vector of P*-values

unA

a numeric vector of P*-values

ize

a numeric vector of P*-values

less

a numeric vector of P*-values

erA

a numeric vector of P*-values

erC

a numeric vector of P*-values

ity

a numeric vector of P*-values

super

a numeric vector of P*-values

est

a numeric vector of P*-values

ment

a numeric vector of P*-values

ify

a numeric vector of P*-values

re

a numeric vector of P*-values

ation

a numeric vector of P*-values

in.

a numeric vector of P*-values

ex

a numeric vector of P*-values

en

a numeric vector of P*-values

be

a numeric vector of P*-values

AuthorCodes

a factor with levels

BLu

(King James Version: Luke-Acts)

BMo

(Book of Mormon)

CAs

(Aesop's fables, translation by Townsend)

CBo

(Baum, The Marvelous Land of Oz)

CBp

(Barrie, Peter Pan and Wendy)

CBw

(Baum, The Wonderful Wizard of Oz)

CCa

(Carroll, Alice's Adventures in Wonderland)

CCt

(Carroll, Through the Looking Glass and what Alice Found There)

CGr

(Grimm Fairy Tales, translations)

CKj

(Kipling, The Jungle Book)

LAp

(Austen, Pride and Prejudice)

LBp

(Burroughs, A Princess of Mars)

LBw

(Bronte, Wuthering Heights)

LCl

(Conrad, Lord Jim)

LCn

(Conrad, Nigger of the Narcissus)

LDb

(Doyle, The Casebook of Sherlock Holmes)

LDc

(Dickens, The Chimes: a Goblin Story)

LDC

(Dickens, A Christmas Carol)

LDh

(Doyle, The Hound of the Baskervilles)

LDv

(Doyle, The Valley of Fear)

LJc

(James, Confidence)

LJe

(James, The Europeans)

LLc

(London, The Call of the Wild)

LLs

(London, The Sea Wolf)

LMa

(Montgomery, Anne of Avonlea)

LMm

(Melville, Moby Dick)

LMn

(Morris, News from Nowhere)

LMp

(Milton, Paradise Lost)

LOs

(Orczy, The Scarlet Pimpernel)

LSd

(Stoker, Dracula)

LSs

(Chu, More than a Chance Meeting (Startrek))

LTa

(Trollope, Ayala's Angel)

LTe

(Trollope, The Eustace Diamonds)

LTf

(Trollope, Can you Forgive her?)

LTy

(Twain, A Connecticut Yankee in King Arthur's Court)

LWi

(Wells, The Invisible Man)

LWt

(Wells, The Time Machine)

LWw

(Wells, The War of the Worlds)

OAf

(The Federalist Papers)

OCh

(Texts sampled from Congress Hearings)

OCl

(Texts sampled from Clinton's Election Speeches)

ODo

(Darwin, On the Origin of the Species)

OGa

(Selected Texts from the Government Accounting Office)

OJe

(James, Essays in Radical Empiricism)

Registers

a factor with levels B (Biblical texts) C (Children's books) L (Literary texts) O (other)

Birth

a numeric vector for the author's year of birth (where available)

Source

Most texts were obtained from the Gutenberg Project (http://www.gutenberg.org) and the Oxford Text Archive (http://ota.ahds.ac.uk/).

References

Baayen, R. H. (1994) Derivational Productivity and Text Typology, Journal of Quantitative Linguistics, 1, 16-34.

Examples

## Not run: 
data(affixProductivity)
affixes.pr = prcomp(affixProductivity[,1:(ncol(affixProductivity)-3)], 
center = TRUE, scale. = TRUE)
library(lattice)
trellis.device()
super.sym = trellis.par.get("superpose.symbol")
splom(data.frame(affixes.pr$x[,1:3]), 
groups = affixProductivity$Registers, 
panel = panel.superpose,
key = list(title  = "texts in productivity space",
text   = list(c("Religious", "Children", "Literary", "Other")),
points = list(pch = super.sym$pch[1:4], col = super.sym$col[1:4])))

## End(Not run)

Description

The text of Lewis Carroll's 'Alice's Adventures in Wonderland', with punctuation marks removed.

Usage

data(alice)

Format

A character vector with 27269 words.

Source

The project Gutenberg at http://www.gutenberg.org

Examples

data(alice)
  alice[1:5]

Description

This function no longer works with recent versions of lme4. For p-values, see the anova() function in the lmerTest package.

Usage

aovlmer.fnc(object,  ...)

Arguments

Value

A warning message.

Author(s)

R. H. Baayen, D. Bates

See Also

See anova in lmerTest.

Examples

## Not run: 
  library(optimx)
  library(lme4)
  data(latinsquare)
  l.lmer = lmer(RT~SOA+(1|Word)+(1|Subject), data=latinsquare,
    control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
  library(lmerTest)
  summary(l.lmer)
  anova(l.lmer)
  
## End(Not run)

Description

For 285 regular and irregular Dutch verbs, the auxiliary for the present and past perfect is listed together with the count of verbal synsets in WordNet. Regular and irregular verbs are matched in the mean for lemma frequency.

Usage

data(auxiliaries)

Format

A data frame with 285 observations on the following 4 variables.

Verb

a factor with 285 monomorphemic Dutch verbs.

Aux

a factor with as levels the auxiliaries hebben, zijn and zijnheb (for verbs allowing both auxiliaries).

VerbalSynsets

a numeric vector witth the number of verbal synonym sets in WordNet in which the verb is listed.

Regularity

a factor with levels irregular and regular.

References

Baayen, R. H. and Moscoso del Prado Martin, F. (2005) Semantic density and past-tense formation in three Germanic languages, Language, 81, 666-698.

Examples

data(auxiliaries)
kruskal.test(auxiliaries$VerbalSynsets, auxiliaries$Aux)

Description

Visual lexical decision latencies for beginning readers (8 year-old Dutch children).

Usage

data(beginningReaders)

Format

A data frame with 7923 observations on the following 13 variables.

Word

a factor for the words.

Subject

a factor for the subjects.

LogRT

a numeric vector with the log-transformed reaction time (in ms).

Trial

a numeric vector coding the rank of the trial in the experimental list.

OrthLength

a numeric vector coding the word's length in letters.

LogFrequency

a numeric vector with log-transformed frequency in Vermeer's frequency dictionary of Dutch children's texts.

LogFamilySize

a numeric vector with the log-transformed morphological family size count (with family members judged to be unknown to young children removed).

ReadingScore

a numeric vector with a score for reading proficiency.

ProportionOfErrors

a numeric vector for the proportion of error responses for the word.

PC1

a numeric vector for the first principal component of a PCA orthogonalization of the preceding 4 reaction times

PC2

a numeric vector for the second principal component of a PCA orthogonalization of the preceding 4 reaction times

PC3

a numeric vector for the third principal component of a PCA orthogonalization of the preceding 4 reaction times

PC4

a numeric vector for the fourth principal component of a PCA orthogonalization of the preceding 4 reaction times

References

Perdijk, K., Schreuder, R., Verhoeven, L. and Baayen, R. H. (2006) Tracing individual differences in reading skills of young children with linear mixed-effects models. Manuscript, Radboud University Nijmegen.

Examples

## Not run: 
data(beginningReaders)
require(lme4)
require(optimx)
require(lmerTest)

beginningReaders.lmer = lmer(LogRT ~  PC1 + PC2 + PC3  + ReadingScore +
  OrthLength + I(OrthLength^2) + LogFrequency + LogFamilySize +
  (1|Word) + (1|Subject) + (0+LogFrequency|Subject) + 
  (0+OrthLength|Subject) + (0+PC1|Subject), 
  data = beginningReaders,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
summary(beginningReaders.lmer)

## End(Not run)

Description

Calculates the condition number with the intercept included, following Belsley, Kuh and Welsch (1980).

Usage

collin.fnc(data, colvector)

Arguments

Value

A list with components

Author(s)

F. J. Tweedie

References

Belsley, D. A. and Kuh, E. and Welsch, R. E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity, Wiley Series in Probability and Mathematical Statistics, New York.

See Also

kappa

Examples

## Not run: 
     data(english)
     collin.fnc(english[english$AgeSubj=="young",], 7:29)$cnumber
  
## End(Not run)

Description

Comparisons of lexical richness between two texts are carried out on the basis of the vocabulary size (number of types) and on the basis of the vocabulary growth rate. Variances of the number of types and of the number of hapax legomena required for the tests are estimated with the help of LNRE models.

Usage

compare.richness.fnc(text1, text2, digits = 5)

Arguments

Details

The comparison for the vocabulary size is carried out with the test statistic

$Z = \frac{E[V_1] - E[V_2]}{\sqrt{\sigma(V_1)^2 + \sigma(V_2)^2}}$

and the comparison of the growth rates with the test statistic

$Z = \frac{\frac{1}{N_1}E[V_1(1)] - \frac{1}{N_2}E[V_2]}{\sqrt{\frac{1}{N_1^2}\sigma(V_1(1))^2 + \frac{1}{N_2^2}\sigma(V_2(1))^2}}$

where $N$ denotes the sample size in tokens, $V$ the vocabulary size, and $V(1)$ the number of hapax legomena.

Value

A summary listing the Chi-Squared measure of goodness of fit for the LNRE models (available in the zipfR package) used to estimate variances, a table listing tokens, types, hapax legomena and the vocabulary growth rate, and two-tailed tests for differences in the vocabulary sizes and growth rates with Z-score and p-value.

Note

It is probably unwise to attempt to apply this function to texts comprising more than 500,000 words.

Author(s)

R. Harald Baayen Radboud University Nijmegen and Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands. [email protected]

References

Baayen, R. H. (2001) Word Frequency Distributions, Kluwer Academic Publishers, Dordrecht.

Examples

## Not run: 
     data(alice, through, oz)
     compare.richness.fnc(tolower(alice), tolower(through[1:length(alice)]))
     compare.richness.fnc(tolower(alice), tolower(oz[1:25942]))
  
## End(Not run)

Description

A class for correspondence analysis

Objects from the Class

Objects can be created by calls of the form new("corres", ...). Correspondence objects can be plotted, summarized, and printed.

Slots

data:

Object of class "list"

Methods

No methods defined with class "corres" in the signature.

Note

to be expanded

Author(s)

R. H. Baayen

References

Murtagh

See Also

See Also corres.fnc.

Examples

showClass("corres")

Description

Correspondence analysis for a contingency table.

Usage

corres.fnc(xtab)

Arguments

Value

A correspondence object with summary and plot methods. The summary method lists eigenvalue rates and coordinates, correlations and contributions for Factor 1 and Factor 2. By default, only the first six rows of the factor tables are shown. Full tables are obtained by specifying header = FALSE when calling summary. For information on higher dimensions, set the option n to the desired number (e.g., n = 3) within summary. See plot.corres for documentation of plot options.

Author(s)

Extension of the code in Murtagh (2005) by R. Harald Baayen

Radboud University Nijmegen & Max Planck Institute for Psycholinguistics

Nijmegen, The Netherlands

email: [email protected]

References

F. Murtagh (2005) Correspondence Analysis and Data Coding with JAVA and R, Chapman & Hall/CRC, Boca Raton, FL.

See Also

See corsup.fnc for adding supplementary data to a correspondence plot, and plot.corres for plot options.

Examples

## Not run: 
  data(oldFrench)
  oldFrench.ca = corres.fnc(oldFrench)
  oldFrench.ca
  summary(oldFrench.ca, head = TRUE)
  plot(oldFrench.ca)

  # more readable plot
  data(oldFrenchMeta)
  plot(oldFrench.ca, rlabels = oldFrenchMeta$Genre, 
  rcol = as.numeric(oldFrenchMeta$Genre), rcex = 0.5, 
  extreme = 0.1, ccol = "blue")

  # create subset of proze texts

  prose = oldFrench[oldFrenchMeta$Genre=="prose" & 
          !is.na(oldFrenchMeta$Year),]
  proseinfo = oldFrenchMeta[oldFrenchMeta$Genre=="prose" & 
          !is.na(oldFrenchMeta$Year),]
  proseinfo$Period = as.factor(proseinfo$Year <= 1250)

  prose.ca = corres.fnc(prose)
  plot(prose.ca, addcol = FALSE, 
  rcol = as.numeric(proseinfo$Period) + 1, 
  rlabels = proseinfo$Year, rcex = 0.7)

  # and add supplementary data for texts with unknown date of composition
  proseSup = oldFrench[oldFrenchMeta$Genre == "prose" & 
          is.na(oldFrenchMeta$Year),]
  corsup.fnc(prose.ca, bycol = FALSE, supp = proseSup, font = 2, 
  cex = 0.8, labels = substr(rownames(proseSup), 1, 4)) 
 
## End(Not run)

Description

Corsup calculates supplementary rows or columns for correspondence analysis.

Usage

corsup.fnc(corres, bycol = TRUE, supp, plot = TRUE, font = 3, labels = "", 
cex = 1)

Arguments

Value

If plot = FALSE, a matrix with the supplementary coordinates. Otherwise, supplementary rows or columns are added to an already existing plot of a correspondence object.

Author(s)

Extension of the code in Murtagh (2005) by R. Harald Baayen

Radboud University Nijmegen & Max Planck Institute for Psycholinguistics

Nijmegen, The Netherlands

email: [email protected]

References

F. Murtagh (2005) Correspondence Analysis and Data Coding with JAVA and R, Chapman & Hall/CRC, Boca Raton, FL.

See Also

corres.fnc

Examples

## Not run: 
  data(oldFrench)
  data(oldFrenchMeta)
  prose = oldFrench[oldFrenchMeta$Genre=="prose" & 
          !is.na(oldFrenchMeta$Year),]
  proseinfo = oldFrenchMeta[oldFrenchMeta$Genre=="prose" & 
          !is.na(oldFrenchMeta$Year),]
  proseinfo$Period = as.factor(proseinfo$Year <= 1250)

  prose.ca = corres.fnc(prose)
  plot(prose.ca, addcol = FALSE, 
  rcol = as.numeric(proseinfo$Period) + 1, 
  rlabels = proseinfo$Year, rcex = 0.7)

  proseSup = oldFrench[oldFrenchMeta$Genre == "prose" & 
          is.na(oldFrenchMeta$Year),]
  corsup.fnc(prose.ca, bycol = FALSE, supp = proseSup, font = 2, 
  cex = 0.8, labels = substr(rownames(proseSup), 1, 4)) 
 
## End(Not run)

Description

Auditory lexical decision latencies for Danish complex words.

Usage

data(danish)

Format

A data frame with 3326 observations on the following 16 variables.

Subject

a random-effect factor coding participants in the experiment.

Word

a random-effect factor coding the words for which auditory lexical decision responses were elicited.

Affix

a random-effect factor coding the affixes in the words.

LogRT

the dependent variable, log response latency.

PC1

first principal component orthogonalizing the four response latencies preceding the current trial in the experiment.

PC2

second principal component orthogonalizing the four response latencies preceding the current trial in the experiment.

PrevError

factor with levels CORRECT and ERROR coding whether the preceding trial elicited a correct lexical decision.

Rank

the trial number in the experiment.

Sex

factor coding the sex of the participant, with levels F (female) and M (male).

LogWordFreq

log-transformed word frequency.

LogAffixFreq

log-transformed affix frequency.

ResidFamSize

residualized morphological family size (taking out LogWordFreq and LogAffixFreq).

ResidSemRating

residualized semantic rating (taking out morphological family size).

LogCUP

log-transformed complex uniqueness point (CUP).

LogUP

log-transformed uniqueness point (UP).

LogCUPtoEnd

log of the distance (in msec) from the CUP to the end of the word.

References

L. Balling and R. H. Baayen (2008). Morphological effects in auditory word recognition: Evidence from Danish. Submitted to Language and Cognitive Processes.

Examples

## Not run: 
data(danish)
require(lme4)
require(lmerTest)
require(optimx)

# ---- mixed-effects regression with three random intercepts

danish.lmer = lmer(LogRT ~ PC1 + PC2 + PrevError + Rank +
  ResidSemRating + ResidFamSize + LogWordFreq*LogAffixFreq*Sex +  
  poly(LogCUP, 2, raw=TRUE) + LogUP + LogCUPtoEnd + 
  (1|Subject) + (1|Word) + (1|Affix), data = danish,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
 
danish.lmerA = lmer(LogRT ~ PC1 + PC2 + PrevError + Rank +
  ResidSemRating + ResidFamSize + LogWordFreq*LogAffixFreq*Sex +  
  poly(LogCUP, 2, raw=TRUE) + LogUP + LogCUPtoEnd + 
  (1|Subject) + (1|Word) + (1|Affix), data = danish,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
  subset=abs(scale(resid(danish.lmer)))<2.5)

summary(danish.lmerA)

## End(Not run)

Description

Data describing the realization of the dative as NP or PP in the Switchboard corpus and the Treebank Wall Street Journal collection.

Usage

data(dative)

Format

A data frame with 3263 observations on the following 15 variables.

Speaker

a factor coding speaker; available only for the subset of spoken English.

Modality

a factor with levels spoken, written.

Verb

a factor with the verbs as levels.

SemanticClass

a factor with levels a (abstract: 'give it some thought'), c (communication: 'tell, give me your name'), f (future transfer of possession: 'owe, promise'), p (prevention of possession: 'cost, deny'), and t (transfer of possession: 'give an armband, send').

LengthOfRecipient

a numeric vector coding the number of words comprising the recipient.

AnimacyOfRec

a factor with levels animate and inanimate for the animacy of the recipient.

DefinOfRec

a factor with levels definite and indefinite coding the definiteness of the recipient.

PronomOfRec

a factor with levels nonpronominal and pronominal coding the pronominality of the recipient.

LengthOfTheme

a numeric vector coding the number of words comprising the theme.

AnimacyOfTheme

a factor with levels animate and inanimate coding the animacy of the theme.

DefinOfTheme

a factor with levels definite and indefinite coding the definiteness of the theme.

PronomOfTheme

a factor with levels nonpronominal and pronominal coding the pronominality of the theme.

RealizationOfRecipient

a factor with levels NP and PP coding the realization of the dative.

AccessOfRec

a factor with levels accessible, given, and new coding the accessibility of the recipient.

AccessOfTheme

a factor with levels accessible, given, and new coding the accessibility of the theme.

References

Bresnan, J., Cueni, A., Nikitina, T. and Baayen, R. H. (2007) Predicting the dative alternation, in Bouma, G. and Kraemer, I. and Zwarts, J. (eds.), Cognitive Foundations of Interpretation, Royal Netherlands Academy of Sciences, 33 pages, in press.

Examples

## Not run: 
data(dative)

# analysis with CART tree

library(rpart)

# ---- initial tree

dative.rp = rpart(RealizationOfRecipient ~ ., 
 data = dative[ ,-c(1, 3)]) # exclude the columns with subjects, verbs
plot(dative.rp, compress = TRUE, branch = 1, margin = 0.1)
text(dative.rp, use.n = TRUE, pretty = 0)

# ---- pruning the initial tree

plotcp(dative.rp)
dative.rp1 = prune(dative.rp, cp = 0.041)
plot(dative.rp1, compress = TRUE, branch = 1, margin = 0.1)
text(dative.rp1, use.n = TRUE, pretty = 0)


# analysis with logistic regression

# ---- logistic regression with the rms package

library(rms)
dative.dd = datadist(dative)
options(datadist = 'dative.dd')
dative.lrm = lrm(RealizationOfRecipient ~ 
  AccessOfTheme + AccessOfRec + LengthOfRecipient + AnimacyOfRec +
  AnimacyOfTheme + PronomOfTheme + DefinOfTheme + LengthOfTheme+
  SemanticClass + Modality, data = dative)
anova(dative.lrm)
plot(Predict(dative.lrm))

# ---- mixed-effects logistic regression with the lme4 package

require(lme4) 
require(lmerTest)
require(optimx)

dative.lmer = glmer(RealizationOfRecipient ~ AccessOfTheme +
  AccessOfRec + LengthOfRecipient + AnimacyOfRec + 
  AnimacyOfTheme + PronomOfTheme + DefinOfTheme + LengthOfTheme + 
  SemanticClass + Modality + (1|Verb), 
  control=glmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
  data = dative, family = "binomial") 

summary(dative.lmer)

# multiple comparisons for Accessibility of Theme
require(multcomp)
par(mar=c(5,8,3,1))
AcOfTheme.glht <- glht(dative.lmer, linfct = mcp(AccessOfTheme = "Tukey"))
plot(AcOfTheme.glht)
abline(v=0)
summary(AcOfTheme.glht)



## End(Not run)

Description

Data describing the realization of the dative as NP or PP in the Switchboard corpus and the Treebank Wall Street Journal collection. Simplified version of the dative data set.

Usage

data(dativeSimplified)

Format

A data frame with 903 observations on the following 5 variables.

Verb

a factor with the verbs as levels.

AnimacyOfRec

a factor with levels animate and inanimate for the animacy of the recipient.

LengthOfTheme

a numeric vector coding the number of words comprising the theme.

AnimacyOfTheme

a factor with levels animate and inanimate coding the animacy of the theme.

RealizationOfRec

a factor with levels NP and PP coding the realization of the dative.

References

Bresnan, J., Cueni, A., Nikitina, T. and Baayen, R. H. (2007) Predicting the dative alternation, in Bouma, G. and Kraemer, I. and Zwarts, J. (eds.), Cognitive Foundations of Interpretation, Royal Netherlands Academy of Sciences, 33 pages, in press.

Examples

## Not run: 
    data(dative)
  
## End(Not run)

Description

Extract degree of polynomial or knots for restricted cubic spline from the predictor name

Usage

degreesOrKnots.fnc(name)

Arguments

Details

attempts to find degrees or knots if present in input name

Value

Returns an integer for degrees or knots

Note

not intended for independent use

Author(s)

R. H. Baayen

See Also

See Also as plotLMER.fnc

Examples

## Not run: not intended for independent use

Description

Durational measurements on the Dutch prefix ge- in the Spoken Dutch Corpus.

Usage

data(durationsGe)

Format

A data frame with 428 observations on the following 8 variables.

Word

a factor with the words as levels.

Frequency

a numeric vector with the word's absolute frequency in the Spoken Dutch Corpus.

Speaker

a factor with the speakers as levels.

Sex

a factor with levels female and male, this information is missing for one speaker.

YearOfBirth

a numeric vector with years of birth.

DurationOfPrefix

a numeric vector with the duration of the prefix -ont in seconds.

SpeechRate

a numeric vector coding speech rate in number of syllables per second.

NumberSegmentsOnset

a numeric vector for the number of segments in the onset of the stem.

References

Pluymaekers, M., Ernestus, M. and Baayen, R. H. (2005) Frequency and acoustic length: the case of derivational affixes in Dutch, Journal of the Acoustical Society of America, 118, 2561-2569.

Examples

## Not run: 
    data(durationsGe)
    durationsGe$Frequency = log(durationsGe$Frequency + 1)
    durationsGe$YearOfBirth = durationsGe$YearOfBirth - 1900

    durationsGe.lm = lm(DurationOfPrefix ~ Frequency+SpeechRate, data = durationsGe)
    summary(durationsGe.lm)

    # ---- model criticism
    
    plot(durationsGe.lm)
    outliers = c(271, 392, 256, 413, 118, 256)
    durationsGe.lm = lm(DurationOfPrefix ~ Frequency + SpeechRate, 
      data = durationsGe[-outliers, ])
    summary(durationsGe.lm)
  
## End(Not run)

Description

Durational measurements on the Dutch prefix ont- in the Spoken Dutch Corpus.

Usage

data(durationsOnt)

Format

A data frame with 102 observations on the following 11 variables.

Word

a factor with the words as levels.

Frequency

a numeric vector with the word's logarithmically transformed frequency in the Spoken Dutch Corpus.

Speaker

a factor with speakers as levels.

Sex

a factor with levels female and male.

YearOfBirth

a numeric vector coding year of birth of the speaker - 1900.

DurationOfPrefix

a numeric vector for the duration of ont- in seconds

DurationPrefixVowel

a numeric vector for the duration of the vowel in the prefix in seconds.

DurationPrefixNasal

a numeric vector for the duration of the nasal in the prefix in seconds.

DurationPrefixPlosive

a numeric vector for the duration of the plosive in the prefix in seconds.

NumberOfSegmentsOnset

a numeric vector for the number of segments in the onset of the stem.

PlosivePresent

a factor with levels no and yes for whether the plosive is realized in the signal.

SpeechRate

a numeric vector coding speech rate in number of syllables per second.

References

Pluymaekers, M., Ernestus, M. and Baayen, R. H. (2005) Frequency and acoustic length: the case of derivational affixes in Dutch, Journal of the Acoustical Society of America, 118, 2561-2569.

Examples

data(durationsOnt)

###### modeling the duration of the prefix

prefix.lm = lm(DurationOfPrefix ~ (YearOfBirth + SpeechRate) * Frequency, 
   data = durationsOnt)
summary(prefix.lm)

# ---- model criticism

plot(prefix.lm)
outliers = c(36, 35, 17, 72)
prefix.lm = lm(DurationOfPrefix ~ (YearOfBirth + SpeechRate) * Frequency, 
   data = durationsOnt[-outliers,])
summary(prefix.lm)

###### modeling the presence of the /t/ 

library(rms)
durationsOnt.dd = datadist(durationsOnt)
options(datadist = 'durationsOnt.dd')

plosive.lrm = lrm(PlosivePresent ~ SpeechRate + YearOfBirth, 
   data = durationsOnt, x = TRUE, y = TRUE)
plosive.lrm
validate(plosive.lrm, bw = TRUE, B = 200)

###### modeling the duration of the /n/

nasal.lm = lm(DurationPrefixNasal ~ PlosivePresent + Frequency + 
   YearOfBirth, data = durationsOnt)
summary(nasal.lm)

# ---- model criticism

plot(nasal.lm)
outliers = c(71, 28, 62, 33)
nasal.lm = lm(DurationPrefixNasal ~ PlosivePresent + Frequency + 
   YearOfBirth, data = durationsOnt[-outliers,])
summary(nasal.lm)

Description

A distance matrix for the conversations of 165 speakers in the Spoken Dutch Corpus. Metadata on the speakers are available in a separate dataset, dutchSpeakersDistMeta.

Usage

data(dutchSpeakersDist)

Format

A data frame for a 165 by 165 matrix of between-speaker differences.

Source

http://lands.let.kun.nl/cgn/ data collected and analyzed in collaboration with Patrick Juola

References

Juola, P. (2003) The time course of language change, Computers and the Humanities, 37, 77-96.

Juola, P. and Baayen, R. H. (2005) A Controlled-corpus Experiment in Authorship Identification by Cross-entropy, Literary and Linguistic Computing, 20, 59-67.

Examples

## Not run: 
    data(dutchSpeakersDist)
    dutchSpeakersDist.d = as.dist(dutchSpeakersDist)
    dutchSpeakersDist.mds = cmdscale(dutchSpeakersDist.d, k = 3)

    data(dutchSpeakersDistMeta)
    dat = data.frame(dutchSpeakersDist.mds, 
       Sex = dutchSpeakersDistMeta$Sex, 
       Year = dutchSpeakersDistMeta$AgeYear, 
       EduLevel = dutchSpeakersDistMeta$EduLevel)
    dat = dat[!is.na(dat$Year),]

    par(mfrow=c(1,2))
    plot(dat$Year, dat$X1, xlab="year of birth", 
       ylab = "dimension 1", type = "p")
    lines(lowess(dat$Year, dat$X1))
    boxplot(dat$X3 ~ dat$Sex, ylab = "dimension 3")
    par(mfrow=c(1,1))

    cor.test(dat$X1, dat$Year, method="sp")
    t.test(dat$X3~dat$Sex)
	
## End(Not run)

Description

Meta-data for the cross-entropy based between-speaker distance matrix dutchSpeakersDist

Usage

data(dutchSpeakersDistMeta)

Format

A data frame with 165 observations on the following 6 variables.

Speaker

a factor with speakers as levels.

Sex

a factor with levels female and male.

AgeYear

a numeric vector with the speakers' year of birth.

AgeGroup

a factor with levels age18to24, age25to34, age35to44, age45to55, and age56up

.

ConversationType

a factor with levels femaleOnly maleFemale, maleOnly, and unknown.

EduLevel

a factor with levels EduUnknown, high, low mid

Source

http://lands.let.kun.nl/cgn/

References

Juola, P. (2003) The time course of language change, Computers and the Humanities, 37, 77-96.

Juola, P. and Baayen, R. H. (2005) A Controlled-corpus Experiment in Authorship Identification by Cross-entropy, Literary and Linguistic Computing, 20, 59-67.

Examples

## Not run: 
     data(dutchSpeakersDistMeta)
  
## End(Not run)

Description

This data set gives mean visual lexical decision latencies and word naming latencies to 2284 monomorphemic English nouns and verbs, averaged for old and young subjects, with various predictor variables.

Usage

data(english)

Format

A data frame with 4568 observations on the following variables.

RTlexdec

numeric vector of log RT in visual lexical decision.

RTnaming

numeric vector of log RT in word naming.

Familiarity

numeric vector of subjective familiarity ratings.

Word

a factor with 2284 words.

AgeSubject

a factor with as levels the age group of the subject: young versus old.

WordCategory

a factor with as levels the word categories N (noun) and V (verb).

WrittenFrequency

numeric vector with log frequency in the CELEX lexical database.

WrittenSpokenFrequencyRatio

numeric vector with the logged ratio of written frequency (CELEX) to spoken frequency (British National Corpus).

FamilySize

numeric vector with log morphological family size.

DerivationalEntropy

numeric vector with derivational entropy.

InflectionalEntropy

numeric vector with inflectional entropy.

NumberSimplexSynsets

numeric vector with the log-transformed count of synonym sets in WordNet in which the word is listed.

NumberComplexSynsets

numeric vector with the log-transformed count of synonym sets in WordNet in which the word is listed as part of a compound.

LengthInLetters

numeric vector with length of the word in letters.

Ncount

numeric vector with orthographic neighborhood density, defined as the number of lemmas in CELEX with the same length (in letters) at Hamming distance 1.

MeanBigramFrequency

numeric vector with mean log bigram frequency.

FrequencyInitialDiphone

numeric vector with log frequency of initial diphone.

ConspelV

numeric vector with type count of orthographic neighbors.

ConspelN

numeric vector with token count of orthographic neighbors.

ConphonV

numeric vector with type count of phonological neighbors.

ConphonN

numeric vector with token count of phonological neighbors.

ConfriendsV

numeric vector with type counts of consistent words.

ConfriendsN

numeric vector with token counts of consistent words.

ConffV

numeric vector with type count of forward inconsistent words

ConffN

numeric vector with token count of forward inconsistent words

ConfbV

numeric vector with type count of backward inconsistent words

ConfbN

numeric vector with token count of backward inconsistent words

NounFrequency

numeric vector with the frequency of the word used as noun.

VerbFrequency

numeric vector with the frequency of the word used as verb.

CV

factor specifying whether the initial phoneme of the word is a consonant (C) or a vowel (V).

Obstruent

factor specifying whether the initial phoneme of the word is a continuant (cont) or an obstruent (obst).

Frication

factor specifying whether the initial phoneme has a burst (burst) or frication (frication) for consonant-initial words, and for vowel-initial words whether the vowel is long or short.

Voice

factor indicating whether the initial phoneme is voiced or voiceless.

FrequencyInitialDiphoneWord

numeric vector with the log-transformed frequency of the initial diphone given that it is syllable-initial.

FrequencyInitialDiphoneSyllable

numeric vector with the log-transformed frequency of the initial diphone given that it is word initial.

CorrectLexdec

numeric vector with the proportion of subjects that accepted the item as a word in lexical decision.

Source

Balota, D.A., Cortese, M.J. and Pilotti, M. (1999) Visual lexical decision latencies for 2906 words,

and

Spieler, D. H. and Balota, D. A. (1998) Naming latencies for 2820 words.

Available at http://psychnet.wustl.edu/coglab/?page_id=208

References

Balota, D., Cortese, M., Sergent-Marshall, S., Spieler, D. and Yap, M. (2004) Visual word recognition for single-syllable words, Journal of Experimental Psychology:General, 133, 283-316.

Baayen, R.H., Feldman, L. and Schreuder, R. (2006) Morphological influences on the recognition of monosyllabic monomorphemic words, Journal of Memory and Language, 53, 496-512.

Examples

## Not run: 
data(english)

# ---- orthogonalize orthographic consistency measures

items = english[english$AgeSubject == "young",]
items.pca = prcomp(items[ , c(18:27)], center = TRUE, scale = TRUE)
x = as.data.frame(items.pca$rotation[,1:4])
items$PC1 =  items.pca$x[,1]
items$PC2 =  items.pca$x[,2]
items$PC3 =  items.pca$x[,3]
items$PC4 =  items.pca$x[,4]
items2 = english[english$AgeSubject != "young", ]
items2$PC1 =  items.pca$x[,1]
items2$PC2 =  items.pca$x[,2]
items2$PC3 =  items.pca$x[,3]
items2$PC4 =  items.pca$x[,4]
english = rbind(items, items2) 

# ---- add Noun-Verb frequency ratio

english$NVratio = log(english$NounFrequency+1)-log(english$VerbFrequency+1)

# ---- build model with ols() from rms

library(rms)
english.dd = datadist(english)
options(datadist = 'english.dd')

english.ols = ols(RTlexdec ~ Voice + PC1 + MeanBigramFrequency + 
   rcs(WrittenFrequency, 5) + rcs(WrittenSpokenFrequencyRatio, 3) + 
   NVratio + WordCategory + AgeSubject +
   rcs(FamilySize, 3) + InflectionalEntropy + 
   NumberComplexSynsets + rcs(WrittenFrequency, 5) : AgeSubject,
   data = english, x = TRUE, y = TRUE)

# ---- plot partial effects

plot(Predict(english.ols))

# ---- validate the model

validate(english.ols, bw = TRUE, B = 200)


## End(Not run)

Description

Estimated etymological age for regular and irregular monomorphemic Dutch verbs, together with other distributional predictors of regularity.

Usage

data(etymology)

Format

A data frame with 285 observations on the following 14 variables.

Verb

a factor with the verbs as levels.

WrittenFrequency

a numeric vector of logarithmically transformed frequencies in written Dutch (as available in the CELEX lexical database).

NcountStem

a numeric vector for the number of orthographic neighbors.

MeanBigramFrequency

a numeric vector for mean log bigram frequency.

InflectionalEntropy

a numeric vector for Shannon's entropy calculated for the word's inflectional variants.

Auxiliary

a factor with levels hebben, zijn and zijnheb for the verb's auxiliary in the perfect tenses.

Regularity

a factor with levels irregular and regular.

LengthInLetters

a numeric vector of the word's orthographic length.

Denominative

a factor with levels Den and N specifying whether a verb is derived from a noun according to the CELEX lexical database.

FamilySize

a numeric vector for the number of types in the word's morphological family.

EtymAge

an ordered factor with levels Dutch, DutchGerman, WestGermanic, Germanic and IndoEuropean.

Valency

a numeric vector for the verb's valency, estimated by its number of argument structures.

NVratio

a numeric vector for the log-transformed ratio of the nominal and verbal frequencies of use.

WrittenSpokenRatio

a numeric vector for the log-transformed ratio of the frequencies in written and spoken Dutch.

References

Baayen, R. H. and Moscoso del Prado Martin, F. (2005) Semantic density and past-tense formation in three Germanic languages, Language, 81, 666-698.

Tabak, W., Schreuder, R. and Baayen, R. H. (2005) Lexical statistics and lexical processing: semantic density, information complexity, sex, and irregularity in Dutch, in Kepser, S. and Reis, M., Linguistic Evidence - Empirical, Theoretical, and Computational Perspectives, Berlin: Mouton de Gruyter, pp. 529-555.

Examples

## Not run: 
data(etymology)

# ---- EtymAge should be an ordered factor, set contrasts accordingly

etymology$EtymAge = ordered(etymology$EtymAge, levels = c("Dutch",
"DutchGerman", "WestGermanic", "Germanic", "IndoEuropean")) 
options(contrasts=c("contr.treatment","contr.treatment"))

library(rms)
etymology.dd = datadist(etymology)
options(datadist = 'etymology.dd')

# ---- EtymAge as additional predictor for regularity

etymology.lrm = lrm(Regularity ~ WrittenFrequency + 
rcs(FamilySize, 3) + NcountStem + InflectionalEntropy + 
Auxiliary + Valency + NVratio + WrittenSpokenRatio + EtymAge, 
data = etymology, x = TRUE, y = TRUE)
anova(etymology.lrm)

# ---- EtymAge as dependent variable

etymology.lrm = lrm(EtymAge ~ WrittenFrequency + NcountStem +
MeanBigramFrequency + InflectionalEntropy + Auxiliary +
Regularity + LengthInLetters + Denominative + FamilySize + Valency + 
NVratio + WrittenSpokenRatio, data = etymology, x = TRUE, y = TRUE)

# ---- model simplification 

etymology.lrm = lrm(EtymAge ~ NcountStem + Regularity + Denominative, 
data = etymology, x = TRUE, y = TRUE)
validate(etymology.lrm, bw=TRUE, B=200)

# ---- plot partial effects and check assumptions ordinal regression

plot(Predict(etymology.lrm))
plot(etymology.lrm)
resid(etymology.lrm, 'score.binary', pl = TRUE)
plot.xmean.ordinaly(EtymAge ~ NcountStem, data = etymology)

## End(Not run)

Description

Frequencies of references to previous years in issues of the Frankfurter Allgemeine Zeiting published in 1994.

Usage

data(faz)

Format

A data frame with 800 observations on the following 2 variables.

Year

a numeric vector coding years referenced in articles published in 1994.

Frequency

a numeric vector for the frequencies with which years are referenced.

References

Pollman, T. and Baayen, R. H. (2001) Computing historical consciousness. A quantitative inquiry into the presence of the past in newspaper texts, Computers and the Humanities, 35, 237-253.

Examples

## Not run: 
data(faz)
faz$Distance = 1:nrow(faz)

# ---- visualization

plot(log(faz$Distance), log(faz$Frequency + 1), 
xlab = "log Distance", ylab = "log Frequency")
abline(v = log(49), lty=1, col="red")   # 1945
abline(v = log(54), lty=1, col="red")   # 1940
abline(v = log(76), lty=2, col="blue")  # 1918
abline(v = log(80), lty=2, col="blue")  # 1914

# ---- breakpoint analysis

deviances = rep(0, nrow(faz)-1)
faz$LogFrequency = log(faz$Frequency + 1)
faz$LogDistance = log(faz$Distance)
for (pos in 1 : (nrow(faz)-1)) {                             # be patient
  breakpoint = log(pos)
  faz$ShiftedLogDistance = faz$LogDistance - breakpoint
  faz$PastBreakPoint = as.factor(faz$ShiftedLogDistance > 0)
  faz.both = lm(LogFrequency~ShiftedLogDistance:PastBreakPoint, data = faz)
  deviances[pos] = deviance(faz.both)
}
breakpoint = log(which(deviances == min(deviances)))

# ---- refit and plot

faz$ShiftedLogDistance = faz$LogDistance - breakpoint
faz$PastBreakPoint = as.factor(faz$ShiftedLogDistance > 0)
faz.both = lm(LogFrequency ~ ShiftedLogDistance:PastBreakPoint, data = faz)

plot(faz$LogDistance, faz$LogFrequency, 
xlab = "log Distance", ylab = "log Frequency", col = "darkgrey")
lines(faz$LogDistance, fitted(faz.both))

## End(Not run)

Description

Phonological specifications for onset, nucleus and offset for 1697 Dutch monomorphemic words with a final obstruent. These final obstruents may exhibit a voicing alternation that is traditionally described as syllable-final devoicing: underlying /d/ in /hond/ becomes a /t/ when syllable-final ([hOnt]) and remains a /d/ otherwise ([hOn-den]).

Usage

data(finalDevoicing)

Format

A data frame with 1697 observations on the following 9 variables.

Word

a factor with the words as levels.

Onset1Type

a factor for the first consonant in the onset, with levels None, Obstruent and Sonorant.

Onset2Type

a factor for the second consonant in the onset, with levels None, Obstruent and Sonorant.

VowelType

a factor describing the vowel with levels iuy, long and short.

ConsonantType

a factor for the first consonant in the offset, with levels None, Obstruent and Sonorant.

Obstruent

a factor describing place and manner of articulation of the final obstruent, with levels F (/f,v/), P (/p,b/), S (/s,z/), T (/t,d/) and X (/x,g/).

Nsyll

a numeric vector for the number of syllables in the word.

Stress

a factor with levels A (antepenult), F (final) and P (penult).

Voice

a factor with levels voiced and voiceless.

References

Ernestus, M. and Baayen, R. H. (2003) Predicting the unpredictable: Interpreting neutralized segments in Dutch, Language, 79, 5-38.

Examples

## Not run: 
data(finalDevoicing)
library(rpart)

# ---- CART tree 

finalDevoicing.rp = rpart(Voice ~ ., data = finalDevoicing[ , -1])
plotcp(finalDevoicing.rp)
finalDevoicing.pruned = prune(finalDevoicing.rp, cp = 0.021)
plot(finalDevoicing.pruned, margin = 0.1, compress = TRUE)
text(finalDevoicing.pruned, use.n = TRUE, pretty = 0, cex=0.8)

# ---- logistic regression 

library(rms)

finalDevoicing.dd = datadist(finalDevoicing)
options(datadist='finalDevoicing.dd')

finalDevoicing.lrm = lrm(Voice ~ VowelType + ConsonantType + Obstruent + 
Nsyll + Stress + Onset1Type + Onset2Type, data = finalDevoicing)
anova(finalDevoicing.lrm)

# ---- model simplification

fastbw(finalDevoicing.lrm)

finalDevoicing.lrm = lrm(Voice ~ VowelType + ConsonantType + 
Obstruent + Nsyll, data = finalDevoicing, x = TRUE, y = TRUE)

plot(Predict(finalDevoicing.lrm))

# ---- model validation

validate(finalDevoicing.lrm, B = 200)

## End(Not run)

Description

Extracts knots for predictor specified simply as, e.g., X from column names of model@X or model@frame

Usage

getKnots.fnc(colnms, xlb)

Arguments

Details

not intended for independent use

Value

an integer (number of knots)

Note

not intended for independent use

Author(s)

R. H. Baayen

See Also

See Also as plotLMER.fnc

Examples

## Not run: not intended for independent use

Description

calculate HPD 95% prediction intervals

Usage

getMCMCintervals.fnc(fixf, mcmcMatrix, m)

Arguments

Details

not intended for independent use

Value

A matrix with columns '"lower"' and '"upper"' and rows corresponding to the values of the predictor to be plotted on the X-axis.

Note

not intended for independent use

Author(s)

R. H. Baayen

References

languageR

See Also

See Also as plotLMER.fnc

Examples

## Not run: not intended for independent use

Description

determines the position (in the X and Y vectors) for the adding of text to an interaction plot

Usage

getPos.fnc(vec, pos)

Arguments

Details

not intended for independent use

Value

an integer specifying position in vector for X and Y values in plot

Note

not indended for independent use

Author(s)

R. H. Baayen

See Also

See Also as plotLMER.fnc

Examples

## Not run: not intended for independent use

Description

Extracts range of predicted values from list of data frames

Usage

getRange.fnc(lst)

Arguments

Details

not intended for independent use.

Value

Note

not intended for separate use.

Author(s)

R. H. Baayen

See Also

See Also as plotLMER.fnc

Examples

## Not run: 
  not intended for independent use

## End(Not run)

Description

extract X from expressions such as poly(X, 3, raw = TRUE

Usage

getRoot.fnc(xlabel)

Arguments

Details

not intended for independent use

Value

a character string (simple name of predictor)

Note

not intended for independent use

Author(s)

R. H. Baayen

See Also

See Also as plotLMER.fnc

Examples

## Not run: not intended for independent use

Description

A class for the analysis of word frequency distributions

Objects from the Class

Objects can be created by calls of the form new("growth", ...). Growth objects can be plotted, summarized, and printed.

Slots

data:

Object of class "list"

Methods

No methods defined with class "growth" in the signature.

Note

to be expanded

Author(s)

R. H. Baayen

References

R. H. Baayen, 2007

See Also

See Also growth.fnc.

Examples

showClass("corres")

Description

This function calculates, for an increasing sequence of text sizes, the observed number of types, hapax legomena, dis legomena, tris legomena, and selected measures of lexical richness.

Usage

growth.fnc(text = languageR::alice, size = 646, nchunks = 40, chunks = 0)

Arguments

Value

A growth object with methods for plotting, printing. As running this function on large texts may take some time, a period is printed on the output device for each completed chunk to indicate progress.

The data frame with the actual measures, which can be extracted with object.name@data$data, has the following columns.

Author(s)

R. H. Baayen

References

R. H. Baayen (2001) Word Frequency Distributions, Dordrecht: Kluwer Academic Publishers.

Tweedie, F. J. & Baayen, R. H. (1998) How variable may a constant be? Measures of lexical richness in perspective, Computers and the Humanities, 32, 323-352.

See Also

See Also plot.growth, and the zipfR package.

Examples

## Not run: 
  data(alice)
  alice.growth = growth.fnc(alice)
  plot(alice.growth)

## End(Not run)

Description

This function converts a growth object (as defined in the languageR package) to a vgc object (as defined in the zipfR package).

Usage

growth2vgc.fnc(growth)

Arguments

Value

A vcg object as defined in the zipfR library.

Author(s)

R. H. Baayen

References

R. H. Baayen (2001) Word Frequency Distributions, Dordrecht: Kluwer Academic Publishers.

zipfR Website: <URL: http://purl.org/stefan.evert/zipfR/>

See Also

See also growth.fncand the zipfR package.

Examples

## Not run: 
library(zipfR)

data(alice)
alice.growth = growth.fnc(text = alice, size = 648, nchunks = 40)
alice.vgc = growth2vgc.fnc(alice.growth)
plot(alice.vgc)

## End(Not run)

Description

The frequency of the determiner 'het' in the Dutch novel 'Max Havelaar' by Multatuli (Eduard Douwes Dekker), in 99 consecutive text fragments of 1000 tokens each.

Usage

data(havelaar)

Format

A data frame with 99 observations on the following 2 variables.

Chunk

a numeric vector with the indices of the text fragments.

Frequency

a numeric vector with the frequencies of the determiner 'het' in the text fragments.

Source

The text of Max Havelaar was obtained from the Project Gutenberg at at http://www.gutenberg.org

Examples

## Not run: 
data(havelaar)

n = 1000                          # token size of text fragments
p = mean(havelaar$Frequency / n)  # relative frequencies

plot(qbinom(ppoints(99), n, p), sort(havelaar$Frequency),
   xlab = paste("quantiles of (", n, ",", round(p, 4), 
   ")-binomial", sep=""), ylab = "frequencies")


lambda = mean(havelaar$Frequency)
ks.test(havelaar$Frequency, "ppois", lambda)
ks.test(jitter(havelaar$Frequency), "ppois", lambda)



## End(Not run)

Description

A simplified version of the primingHeid dataset.

Usage

data(heid)

Format

A data frame with 832 observations on the following 4 variables.

Subject

a factor with subjects as levels.

Word

a factor with words as levels.

RT

a numeric vector with logarithmically transformed reaction times in visual lexical decision.

BaseFrequency

a numeric vector with the logarithmically transformed frequency of the base adjective of the word with the suffix -heid.

References

De Vaan, L., Schreuder, R. and Baayen, R. H. (2007) Regular morphologically complex neologisms leave detectable traces in the mental lexicon, The Mental Lexicon, 2, in press.

Examples

## Not run: 
data(heid)
heid = aggregate(heid$RT, list(heid$Word, heid$BaseFrequency), mean)
colnames(heid) = c("Word", "BaseFrequency", "MeanRT")

## End(Not run)

Description

This function calculates Herdan's constant C.

Usage

herdan.fnc(text, chunks)

Arguments

Value

A list with components

Author(s)

R. H. Baayen

References

Herdan, G. (1960) Type-Token Mathematics, The Hague: Mouton.

Herdan, G. (1964) Quantitative Linguistics, London: Buttersworths.

See Also

See Also growth.fnc.

Examples

## Not run: 
  data(alice)
  herdan.fnc(alice, cumsum(rep(floor(length(alice)/40), 40)))

## End(Not run)

Description

Filtered fMRI signal at the most significant voxel and average priming scores for brain-damaged patients, in a study addressing the extent to which phonological and semantic processes recruit the same brain areas.

Usage

data(imaging)

Format

A data frame with 35 observations on the following 3 variables.

Condition

a factor with levels irregulars (the morphological condition involving priming using inflected forms of irregular English verbs, e.g., 'began'-'begin') and semantics (priming with semantically related words such as 'card' and 'paper').

BehavioralScore

a numeric vector for the average priming scores.

FilteredSignal

a numeric vector for the intensity of the filtered fMRI signal at the most significant voxel.

Details

Location of data points reconstructed from the pixel map of Figure 2b of Tyler et al. 2005.

Source

Tyler, L.K., Marslen-Wilson, W.D. and Stamatakis, E.A. (2005) Differentiating lexical form, meaning, and structure in the neural language system, PNAS, 102, 8375-8380.

Examples

## Not run: 
data(imaging)

imaging.lm = lm(FilteredSignal~BehavioralScore*Condition, data=imaging)
summary(imaging.lm)

plot(imaging$BehavioralScore, imaging$FilteredSignal, type = "n", 
  xlim = c(-30, 40), ylim = c(0, 80))
semantics = imaging[imaging$Condition == "semantics",]
irregulars = imaging[imaging$Condition == "irregulars",]
points(semantics$BehavioralScore, semantics$FilteredSignal, col = "black")
points(irregulars$BehavioralScore, irregulars$FilteredSignal, col = "darkgrey")
abline(lm(FilteredSignal ~ BehavioralScore, data = semantics), col = 'black')
abline(lm(FilteredSignal ~ BehavioralScore, data = irregulars), 
  col = 'darkgrey')

# model criticism

plot(imaging.lm)
outliers = c(1, 19) # given Cook's distance, or perhaps only
outliers = 1        # the outlier in the semantics subset
imaging.lm = lm(FilteredSignal ~ BehavioralScore * Condition, 
  data = imaging[-outliers, ])
summary(imaging.lm)



## End(Not run)

Description

given a model matrix with main effects only, add interactions

Usage

implementInteractions.fnc(m)

Arguments

Details

not intended for independent use

Value

an updated (model) matrix

Note

not intended for independent use

Author(s)

R. H. Baayen

See Also

plotLMER.fnc

Examples

## Not run: not intended for independent use

Description

This function carries out a by-item regression for the simulated data sets generated in simulate.regression.fnc. It is not designed to be used independently.

Usage

item.fnc(data)

Arguments

Value

A model fitted with lm().

Author(s)

R. H. Baayen

See Also

See Also simulateRegression.fnc and make.reg.fnc.

Examples

## Not run: 
  dat = make.reg.fnc()
  dat.lm = item.fnc(dat)
  summary(dat.lm)

## End(Not run)

Description

By-item anova for simulated data set as created within simulateQuasif.fnc. Not intended for independent use. Depends on the packages MASS, coda and lme4.

Usage

items.quasif.fnc(dat)

Arguments

Value

A list with components

Author(s)

R. H. Baayen

See Also

See also simulateQuasif.fnc.

Examples

## Not run: 
data(quasif)
items.quasif.fnc(quasif)

## End(Not run)

Description

This function calculates for a given dependent variable the value of that variable at lag timesteps earlier in the time series of an experiment.

Usage

lags.fnc(dat, time="Trial", group = "Subject", depvar = "RT", lag=1)

Arguments

Value

A vector with the values of the dependent variable at the specified lag. The by-group mean is substituted for the first lag timestep(s), for which there is/are no preceding value(s) for the dependent variable.

Author(s)

R. H. Baayen

See Also

acf.fnc

Examples

## Not run: 
  dfr = data.frame(Subject=c(rep("a", 5), rep("b", 5)),
                   Trial = c(rep(1:5,2)),
                   RT = rnorm(10, 500, 40))
  dfr$prevRT = lag.fnc(dfr, time="Trial", group="Subject", depvar="RT")
  dfr


## End(Not run)

Description

Simulated lexical decision latencies with SOA as treatment, using a Latin Square design with subjects and items, as available in Raaijmakers et al. (1999).

Usage

data(latinsquare)

Format

A data frame with 144 observations on the following 6 variables.

Group

a factor with levels G1, G2 and G3, for groups of subjects

Subject

a factor with subjects labelled S1, ... S12.

Word

a factor with words labelled W1 ... W12.

RT

a numeric vector for reaction times.

SOA

a factor with levels long, medium, and short.

List

a factor with levels L1, L2, and L3 for lists of words.

Source

Raaijmakers, J.G.W., Schrijnemakers, J.M.C. & Gremmen, F. (1999) How to deal with "The language as fixed effect fallacy": common misconceptions and alternative solutions, Journal of Memory and Language, 41, 416-426.

Examples

## Not run: 
data(latinsquare)
library(lme4)
latinsquare.with = 
   simulateLatinsquare.fnc(latinsquare, nruns = 1000, with = TRUE) 
latinsquare.without = 
   simulateLatinsquare.fnc(latinsquare, nruns = 1000, with = FALSE)
latinsquare.with$alpha05
latinsquare.without$alpha05

## End(Not run)

Description

Lexical decision latencies elicited from 21 subjects for 79 English concrete nouns, with variables linked to subject or word.

Usage

data(lexdec)

Format

A data frame with 1659 observations on the following 28 variables.

Subject

a factor for the subjects.

RT

a numeric vector for logarithmically transformed reaction times.

Trial

a numeric vector for the rank of the trial in the experimental list.

Sex

a factor with levels F (female) and M (male).

NativeLanguage

a factor with levels English and Other, distinguishing between native and nonnative speakers of English.

Correct

a factor with levels correct and incorrect coding whether the word was correctly responded to as a word rather than a nonword.

PrevType

a factor with levels nonword and word coding whether the item presented at the preceding trial was a word or a nonword.

PrevCorrect

a factor with levels correct and incorrect coding whether the preceding item elicited a correct response.

Word

a factor with 79 words as levels.

Frequency

a numeric vector with logarithmically transformed lemma frequencies as available in the CELEX lexical database.

FamilySize

a numeric vector with the log-transformed count of a word's morphological family members.

SynsetCount

a numeric vector with the log-transformed count of synonym sets in WordNet in which the word is listed.

Length

a numeric vector for the word's length in letters.

Class

a factor for the semantic category of the word's referent, with levels animal and plant.

FreqSingular

a numeric vector with the frequency in CELEX of the singular form.

FreqPlural

a numeric vector with the frequency in CELEX of the plural form.

DerivEntropy

Shannon's entropy calculated over the frequency distribution of a word's family members.

Complex

a factor coding morphological complexity with levels complex and simplex.

rInfl

a numeric vector for the log of the ratio of the singular to the plural frequency.

meanRT

a numeric vector for the by-item mean reaction time averaged over subjects.

SubjFreq

a numeric vector for the by-item mean subjective frequency estimate averaged over subjects.

meanSize

a numeric vector for the by-item mean size rating averaged over subjects.

meanWeight

a numeric vector for the by-item mean weight rating averaged over subjects.

BNCw

a numeric vector with the logarithmically transformed frequency in the written part of the British National Corpus.

BNCc

a numeric vector with the logarithmically transformed frequency in the context-governed part of the British National Corpus.

BNCd

a numeric vector with the logarithmically transformed frequency in the demographic part of the British National Corpus.

BNCcRatio

a numeric vector with the log of the ratio of the (absolute) frequencies in the context-governed and written parts of the British National Corpus, normalized for the differences in corpus size.

BNCdRatio

a numeric vector with the log of the ratio of the (absolute) frequencies in the demographic and written parts of the British National Corpus, normalized for the differences in corpus size.

Source

Data collected with Jen Hay, University of Canterbury, Christchurch, New Zealand, 2004.

Examples

## Not run: 
data(lexdec)
require(lme4)
require(lmerTest)
require(optimx)

lexdec.lmer = lmer(RT ~ 1 + Correct + Trial + PrevType * meanWeight + 
  Frequency + NativeLanguage * Length + (1|Subject) + (1|Word), 
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
  data = lexdec)
summary(lexdec.lmer)

# random slopes

lexdec.lmerA = lmer(RT ~ 1 + Correct + Trial + PrevType * meanWeight + 
  Frequency + NativeLanguage * Length + (Trial|Subject) + (1|Word), 
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
  data = lexdec)
anova(lexdec.lmer, lexdec.lmerA)

lexdec.lmerB = lmer(RT ~ 1 + Correct + Trial + PrevType * meanWeight + 
  Frequency + NativeLanguage * Length + (Trial|Subject) + 
  (Length|Subject) + (1|Word), data = lexdec,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
anova(lexdec.lmerA, lexdec.lmerB)

# model criticism

qqnorm(resid(lexdec.lmerB))

lexdec.lmerC = lmer(RT ~ 1 + Correct + Trial + PrevType * meanWeight + 
  Frequency + NativeLanguage * Length + 
  (Trial|Subject) + (Length|Subject) + (1|Word), 
  data = lexdec[abs(scale(resid(lexdec.lmerB)))<2,],
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))

qqnorm(resid(lexdec.lmerC))

# p values
summary(lexdec.lmerC)


## End(Not run)

Description

Lexical distributional measures for 2233 English monomorphemic words. This dataset provides a subset of the data available in the dataset english.

Usage

data(lexicalMeasures)

Format

A data frame with 2233 observations on the following 24 variables.

Word

a factor with 2284 words.

CelS

numeric vector with log-transformed lemma frequency in the CELEX lexical database.

Fdif

numeric vector with the logged ratio of written frequency (CELEX) to spoken frequency (British National Corpus).

Vf

numeric vector with log morphological family size.

Dent

numeric vector with derivational entropy.

Ient

numeric vector with inflectional entropy.

NsyS

numeric vector with the log-transformed count of synonym sets in WordNet in which the word is listed.

NsyC

numeric vector with the log-transformed count of synonym sets in WordNet in which the word is listed as part of a compound.

Len

numeric vector with length of the word in letters.

Ncou

numeric vector with orthographic neighborhood density.

Bigr

numeric vector with mean log bigram frequency.

InBi

numeric vector with log frequency of initial diphone.

spelV

numeric vector with type count of orthographic neighbors.

spelN

numeric vector with token count of orthographic neighbors.

phonV

numeric vector with type count of phonological neighbors.

phonN

numeric vector with token count of phonological neighbors.

friendsV

numeric vector with type counts of consistent words.

friendsN

numeric vector with token counts of consistent words.

ffV

numeric vector with type count of forward inconsistent words.

ffN

numeric vector with token count of forward inconsistent words.

fbV

numeric vector with type count of backward inconsistent words.

fbN

numeric vector with token count of backward inconsistent words

ffNonzero

a numeric vector with the count of forward inconsistent words with nonzero frequency.

NVratio

a numeric vector with the logarithmically transformed ratio of the noun and verb frequencies.

References

Baayen, R.H., Feldman, L. and Schreuder, R. (2006) Morphological influences on the recognition of monosyllabic monomorphemic words, Journal of Memory and Language, 53, 496-512.

Examples

## Not run: 
data(lexicalMeasures)
data(lexicalMeasuresDist)

library(rms)
library(cluster)
plot(varclus(as.matrix(lexicalMeasures[,-1])))

lexicalMeasures.cor = cor(lexicalMeasures[,-1], method = "spearman")^2
lexicalMeasures.dist = dist(lexicalMeasures.cor)
pltree(diana(lexicalMeasures.dist))

data(lexicalMeasuresClasses)
x = data.frame(measure = rownames(lexicalMeasures.cor), 
cluster = cutree(diana(lexicalMeasures.dist), 5),
class = lexicalMeasuresClasses$Class)
x = x[order(x$cluster), ]
x

## End(Not run)

Description

A data frame labelling the lexical measures in the dataset lexicalMeasures as measures of form or meaning.

Usage

data(lexicalMeasuresClasses)

Format

A data frame with 23 observations on the following 3 variables.

Variable

a factor with as levels the measures:

Bigr

Mean Bigram Frequency.

CelS

CELEX Frequency.

Dent

Derivational Entropy.

fbN

Token Count of Backward Inconsistent Words.

fbV

Type Count of Backward Inconsistent Words.

Fdif

Ratio of Frequencies in Written and Spoken English.

ffN

Token Count of Forward Inconsistent Words.

ffNonzero

Type Count of Forward Inconsistent Words with Nonzero Frequency.

ffV

Type Count of Forward Inconsistent Words

friendsN

Token Count of Consistent Words.

friendsV

Type Count of Consistent Words.

Ient

Inflectional Entropy

InBi

Initial Bigram Frequency

Len

Length in Letters

Ncou

Orthographic Neighborhood Density

NsyC

Number of Complex Synsets

NsyS

Number of Simplex Synsets

NVratio

Ratio of Noun and Verb Frequencies

phonN

Token Count of Phonological Neighbors.

phonV

Type Count of Phonological Neighbors.

spelN

Token Count of Orthographic Neighbors.

spelV

Type Count of Orthographic Neighbors.

Vf

Morphological Family Size.

Class

a factor with levels Form and Meaning.

Explanation

a factor with glosses for the variables.

References

Baayen, R.H., Feldman, L. and Schreuder, R. (2006) Morphological influences on the recognition of monosyllabic monomorphemic words, Journal of Memory and Language, 53, 496-512.

Examples

## Not run: 
library(cluster)
data(lexicalMeasures)
data(lexicalMeasuresClasses)

lexicalMeasures.cor = cor(lexicalMeasures[,-1], method = "spearman")^2
x = data.frame(measure = rownames(lexicalMeasures.cor), 
cluster = cutree(diana(dist(lexicalMeasures.cor)), 5),
class = lexicalMeasuresClasses$Class)
x = x[order(x$cluster), ]
x

## End(Not run)

Description

Visualization of an interaction in a model fitted with lmer of two numeric predictors.

Usage

lmerPlotInt.fnc(lmermodel, xname, yname, intxyname, 
                qntls = seq(0, 1, by = 0.1), view = 30, 
                addStdError = FALSE, ndigits = 2, nlev = 30, 
                which = "matplot", shadow = 0.5, colour = "lightblue", 
                fun  = NA, ylabel = NA, ...)

Arguments

Value

A plot is shown on the graphics device.

Warning

This function should not be used to plot interactions when one of the predictors also has quadratic or higher terms in the model.

Author(s)

R. H. Baayen

Examples

## Not run: 
  require(lme4)
  require(optimx)
	lexdec.lmer = lmer(RT~BNCw*Frequency+(1|Subject)+(1|Word), data=lexdec,
    control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
	lmerPlotInt.fnc(lexdec.lmer, "BNCw", "Frequency", "BNCw:Frequency", 
    which="matplot")

## End(Not run)

Description

This convenience function creates a regression data set with subjects, items, and three numerical predictors, and optionally an effect of learning or fatigue. This function is called by simulateRegression.fnc, and is not intended for independent use.

Usage

make.reg.fnc(nsubj = 10, nitem = 20, beta = c(400, 2, 6, 4), 
learn = FALSE, learnRate = 10, stdevItem = 40, stdevSubj = 80, 
  stdevError = 50)

Arguments

Value

A data frame with intercept, predictors labelled X, Y and Z, Item, Subject, the simulated random effects for Item and Subject, the residual errors, and the simulated RTs.

Author(s)

R. H. Baayen

See Also

simulateRegression.fnc

Examples

## Not run: 
  simdat = make.reg.fnc()
  require(lme4)
  require(lmerTest)
  require(optimx)
  simdat.lmer = lmer(RT ~ X + Y + Z + (1|Subject) + (1|Item), 
    control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
    data = simdat)
  summary(simdat.lmer)

  simdat = make.reg.fnc(learn = TRUE)
  simdat.lmer = lmer(RT ~ X + Y + Z + Trial + (1|Subject) + (1|Item), 
    control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
    data = simdat)
  summary(simdat.lmer)

## End(Not run)

Description

Creates a model matrix with main effects only

Usage

makeDefaultMatrix.fnc(model, n = 100, conditioningPred = "", 
                      conditioningValue = NULL, control = NA)

Arguments

Details

not intended for independent use

Value

a (model) matrix

Note

not intended for independent use

Author(s)

R. H. Baayen

See Also

See Also as plotLMER.fnc

Examples

## Not run: not intended for independent use

Description

creates a data set with Y ~ 30+cos(X) for 10 subjects, to compare restricted cubic spline in lmer with the spline of ols.

Usage

makeSplineData.fnc(intr=0)

Arguments

Details

Requires rms package to be attached.

Value

A data frame with as values:

Note

intended for illustration only

Author(s)

R. H. Baayen

See Also

See Also as plotLMER.fnc

Examples

## Not run: 
require("rms")
require("optimx")
require("lmerTest")
dfr = makeSplineData.fnc()
table(dfr$Subject)
xylowess.fnc(Y ~ X | Subject, data = dfr)

dfr.lmer = lmer(Y ~ rcs(X, 5) + (1|Subject), data = dfr,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
dfr$fittedLMER = as.vector(dfr.lmer@X %*% fixef(dfr.lmer))

dfr.dd = datadist(dfr)
options(datadist='dfr.dd')
dfr.ols = ols(Y~Subject+rcs(X), data=dfr, x=T, y=T)
dfr$fittedOLS = fitted(dfr.ols)

# we plot the lmer() fit in blue, the ols() fit in red (both adjusted for
# subject S1), and plot the underlying model in green
plot(dfr[dfr$Subject=="S1",]$X, dfr[dfr$Subject=="S1",]$fittedLMER +
  ranef(dfr.lmer)[[1]]["S1",], type="l", col="blue",
  ylim = range(dfr$y + ranef(dfr.lmer)[[1]]["S1",],
  dfr[dfr$Subject == "S1",]$fittedLMER,
  dfr[dfr$Subject == "S1",]$fittedOLS), xlab="X", ylab="Y")   
lines(dfr[dfr$Subject=="S1",]$X, dfr[dfr$Subject=="S1",]$fittedOLS, col="red")
lines(dfr[dfr$Subject=="S1",]$X, dfr[dfr$Subject=="S1",]$y+ranef(dfr.lmer)[[1]]["S1",], 
  col="green")
legend(2,29,c("30+cos(x)", "lmer (S1)", "ols (S1)"), lty=rep(1,3), 
col=c("green", "blue", "red"))

## End(Not run)

Description

The text of H. Melville's 'Moby Dick', with punctuation marks removed.

Usage

data(alice)

Format

A character vector with 215994 words.

Source

The project Gutenberg at http://www.gutenberg.org

Examples

## Not run: 
  data(moby)
  moby[1:2]

## End(Not run)

Description

This function produces a scatterplot for a bivariate standard normal distribution with least squares regression line.

Usage

mvrnormplot.fnc(r, n, limits)

Arguments

Value

A scatterplot with ordinary least squares regression line is shown on the graphics device, with sample estimate of r added at the top of the plot.

Author(s)

R. H. Baayen

See Also

mvrnorm (MASS package)

Examples

## Not run: 
	mvrnormplot.fnc(r=0.9, n=100)

## End(Not run)

Description

Frequency (m) and frequency of frequency (Vm) for string types with the suffix -ness in the context-governed subcorpus of the British National Corpus sampling spoken British English.

Usage

data(nesscg)

Format

A data frame with 37 observations on the following 2 variables.

m

a numeric vector with word frequencies.

Vm

a numeric vector with the frequencies of word frequencies.

Source

The British National Corpus, see http://www.natcorp.ox.ac.uk/

Examples

## Not run: 
data(nesscg)
library(zipfR)
nesscg.spc = spc(m=nesscg$m, Vm = nesscg$Vm)
plot(nesscg.spc)

## End(Not run)

Description

Frequency (m) and frequency of frequency (Vm) for string types with the suffix -ness in the demographic subcorpus of the British National Corpus sampling spoken British English.

Usage

data(nessdemog)

Format

A data frame with 15 observations on the following 2 variables.

m

a numeric vector with word frequencies.

Vm

a numeric vector with the frequencies of word frequencies.

Source

The British National Corpus, see http://www.natcorp.ox.ac.uk/

Examples

data(nessdemog)
library(zipfR)
nessdemog.spc = spc(m=nessdemog$m, Vm = nessdemog$Vm)
plot(nessdemog.spc)

Description

Frequency (m) and frequency of frequency (Vm) for string types with the suffix -ness in the subcorpus of the British National Corpus sampling written British English.

Usage

data(nessw)

Format

A data frame with 189 observations on the following 2 variables.

m

a numeric vector with word frequencies.

Vm

a numeric vector with the frequencies of word frequencies.

Source

The British National Corpus, see http://www.natcorp.ox.ac.uk/

Examples

## Not run: 
data(nessw)
library(zipfR)
nessw.spc = spc(m=nessw$m, Vm = nessw$Vm)
plot(nessw.spc)

## End(Not run)

Description

Frequencies of 35 morphosyntactic tag trigrams in 343 Old French texts.

Usage

data(oldFrench)

Format

A data frame with the frequencies of 35 tag trigrams (columns) for 343 Old French texts (rows) in the Nouveau Corpus d'Amsterdam. See oldFrenchMeta for details on the texts (and manuscript versions).

Source

Data from Nouveau Corpus d'Amsterdam, https://www.ling.uni-stuttgart.de/institut/team/Stein-00011.

References

Ernestus, M., van Mulken, M. and Baayen, R. H. (2007) De syntax van Oud-Franse ridders en heiligen in ruimte en tijd To appear in Onze Taal.

Examples

data(oldFrench)
data(oldFrenchMeta)

oldFrench.ca = corres.fnc(oldFrench)

plot(oldFrench.ca, rlabels = oldFrenchMeta$Genre, 
rcol = as.numeric(oldFrenchMeta$Genre), rcex = 0.5, 
extreme = 0.1, ccol = "blue")

Description

Meta data for the oldFrench data, a matrix of frequencies for texts (rows) by tag trigrams (columns). The meta data provide information on the texts, manuscript variants, their authors, their region and approximate date of origin, their general topic, and their genre.

Usage

data(oldFrenchMeta)

Format

A data frame with 342 observations on the following 7 variables.

Textlabels

a factor with texts coded as follows:

Abe

J. de Meun, Traduction de la premiere epitre de P. Abelard, 1–821

Hyl1

Anon, La vie de saint Hylaire

Art

J. de Meun, L'art de chevalerie

Bar

Anon, L'histoire de Barlaam et Josaphat

Cathy

Anon, La vie de sainte Catherine d'Alexandrie

Hyl2

Anon, La vie de saint Hylaire

Ch1

Chretien de Troyes, Le Chevalier au lion

Ch2

Chretien de Troyes, Le chevalier au lion

Clari

Robert de Clari, La conquete de Constantinople

Marie

Rutebeuf, Sainte Marie l'Egyptienne

Fab4c

Anon, Fabliau nr 4 ms C

Fab4e

Anon, Fabliau nr 4 ms E

Fab4f

Anon, Fabliau 4f

Faba

Anon, Fabliaux nrs 1,2,4,23 et 29 du ms A

Fabb

Anon, Fabliaux nrs 2 et 4 du ms B

Fabd.COD

Anon, Fabliaux nrs 2 et 4 du ms D

Hyl3

Anon, La vie de saint Hylaire

Jacobi

Pierre de Beauvais, The Liber Sancti Jacobi

Louis

J. de Joinville, La vie de saint Louis

Cathy1

Anon, La passion saynte Katherine

Lancelot

Anon, Lancelot do Lac, p. 1.1–20.13

Merlin1

Merlin, Robert de Boron

Marga

Anon, La vie de Sainte Marguerite de Wace

Martin

Anon, Leben und Wunderthaten des heiligen Martin

Merlin2

Anon, Merlin, p.1–29 (ms. Huth)

RoseA

J. de Meun, Le Roman de la Rose

Arthur

Anon, La mort le roi Artu, par.1–35

NimAf

Anon, charroi de Nimes, ms. A, fragment

NimB1

Anon, Le charroi de Nimes, ms B1

NimB2

Anon, Le charroi de Nimes, ms B2

Nouvel

Jacquemart Gielee, Renart le Nouvel

Jehan

Anon, La vie de saint Jehan Bouche d'Or

Per0

Chretien de Troyes, Perceval

perL

Chretien de Troyes, Perceval

PerQ

Chretien de Troyes, Perceval

PerS

Chretien de Troyes, Perceval

PerU

Chretien de Troyes, Perceval

Queste

Anon, La queste del saint Graal, p.1.1–41.17

Rob

Anon, Robert le Diable, v.1–808

RomB

Anon, Le Roman de Renart, br.VI, ms B

RomD

Anon, Roman de Renard, br.VI, ms D

RomL

Anon, Roman de Renard, br. VI, ms L

RomO

Anon, Le Roman de Renart, br. VI, ms 0

RoseB

Guillaume de Lorris, Le roman de la rose

Sapi

Anon, Sermo de sapientia, dans: Li dialoge Gregoire lo pape

Troi

Anon, Le roman de Troie en prose, par.1–19

Conqueste

Josfroi de Vileharduyn, La conqueste de Costentinoble

YvA

Chretien de Troyes, Le chevalier au lion, v.1–1000

YvP

Chretien de Troyes, Le chevalier au lion, v.1–1000

YvS

Chretien de Troyes, Le chevalier au lion, v.1–1000

YvV

Chretien de Troyes, Le chevalier au lion, v 1–1000

Codes

a factor with manuscript variants, indicated by extensions to the text codes.

Author

a factor with levels Anon, ChretienDeTroyes, GuillaumeDeLorris, Joinville, Meun, NouvelRenart, PierreDeBeauvais, RobertDeBoron, RobertDeClari, RobertLeDiable, Rutebeuf, and Villeharduyn.

Topic

a factor with levels Knight, Other, and Saint.

Genre

a factor with levels poetry and prose.

Region

a factor with levels R1 (Picardie), R2 (Champenois), and R3 (Nievre-Allier).

Year

a numeric vector indicating approximate year of origin.

Source

Data from Nouveau Corpus d'Amsterdam, https://www.ling.uni-stuttgart.de/institut/team/Stein-00011.

References

Ernestus, M., van Mulken, M. and Baayen, R. H. (2007) De syntax van Oud-Franse ridders en heiligen in ruimte en tijd To appear in Onze Taal.

Examples

## Not run: 
data(oldFrench)
data(oldFrenchMeta)

oldFrench.ca = corres.fnc(oldFrench)

plot(oldFrench.ca, rlabels = oldFrenchMeta$Genre, 
rcol = as.numeric(oldFrenchMeta$Genre), rcex = 0.5, 
extreme = 0.1, ccol = "blue")

## End(Not run)

Description

The text of L. F. Baum's 'The Wonderful Wizard of Oz', with punctuation marks removed.

Usage

data(alice)

Format

A character vector with 39513 words.

Source

The project Gutenberg at http://www.gutenberg.org/

Examples

## Not run: 
  data(oz)
  oz[1:5]

## End(Not run)

Description

A matrix of scatterplots is produced with Pearson and Spearman correlations in the lower triangle. By default, smoothers are added to panels in the upper triangle, and histograms are added to the panels on the diagonal.

Usage

pairscor.fnc(data, hist = TRUE, smooth = TRUE,
  cex.points = 1,  col.points = "darkgrey")

Arguments

Author(s)

R. Harald Baayen

See Also

See Also pairs and panel.smooth.

Examples

## Not run: 
  data(lexicalMeasures)
  pairscor.fnc(lexicalMeasures[,c("CelS", "Vf", "Ient", "NsyS", "Ncou")])

## End(Not run)

Description

parse character string specifying restricted cubic spline into simple predictor name and number of knots

Usage

parsePredName.fnc(name)

Arguments

Details

not intended for independent use

Value

a list with components

Note

not intended for independent use

Author(s)

R. H. Baayen

See Also

See Also as plotLMER.fnc

Examples

## Not run: not intended for independent use

Description

The development of periphrastic do in English: Ellegard's counts for the use of do across four sentence types in 11 consecutive time periods between 1390 and 1710.

Usage

data(periphrasticDo)

Format

A data frame with 44 observations on the following 5 variables.

begin

a numeric vector with beginnings of the time periods used by Ellegard.

end

a numeric vector with ends of these time periods.

type

a factor for sentence type, with levels affdecl (affirmative declarative), affquest (affirmative question), negdecl (negative declarative) and negquest (negative question).

do

a numeric vector with the count of sentences with do.

other

a numeric vector with the count of sentences without do.

Source

Ellegard, A. (1953) The auxiliary do: The establishment and regulation of its use in English, Stockholm: Almquist & Wiksell.

References

Vulanovic, R. and Baayen, R. H. (2006) Fitting the development of periphrastic do in all sentence types, in Grzybek, P. and Koehler, R. (eds.), Festschrift fuer Gabriel Altmann, Berlin: Walter de Gruyter, p. 679-688.

Examples

## Not run: 
data(periphrasticDo)

# add midpoints of time periods

periphrasticDo$year = periphrasticDo$begin +
  (periphrasticDo$end-periphrasticDo$begin)/2

# and ad an indicator variable distinguishing the first three time periods
# from the others

periphrasticDo$Indicator = rep(c(rep(0, 3), rep(1, 8)), 4)

# fit a logistic regression model

periphrasticDo.glm = glm(cbind(do, other) ~
(year + I(year^2) + I(year^3)) * type + Indicator * type + 
Indicator * year, data = periphrasticDo, family = "binomial")

anova(periphrasticDo.glm, test = "F")

# visualization of data and model predictions

periphrasticDo$predict = predict(periphrasticDo.glm, type = "response")
par(mfrow=c(2, 2))
for (i in 1:nlevels(periphrasticDo$type)) {
  subset = periphrasticDo[periphrasticDo$type == 
    levels(periphrasticDo$type)[i], ]
  plot(subset$year,
    subset$do/(subset$do + subset$other), 
    type = "p", ylab = "proportion", xlab = "year", 
    ylim = c(0, 1), xlim = c(1400, 1700))
  mtext(levels(periphrasticDo$type)[i], line = 2)
  lines(subset$year, subset$predict, lty = 1)
}
par(mfrow=c(1, 1))


## End(Not run)

Description

Phylogenetic relations between Papuan and Oceanic languages: 127 grammatical traits (absent/present) for 31 languages.

Usage

data(phylogeny)

Format

A data frame with 31 observations on the following 127 variables.

Language

a factor for 31 languages: Anem, Ata, Bali, Banoni, Bilua, Buin, Gapapaiwa, Kairiru, Kaulong, Kilivila, Kokota, Kol, Kuot, Lavukaleve, Mali, Motuna, Nalik, Nasioi, Rotokas, Roviana, Savosavo, Siar, Sisiqa, Sudest, Sulka, Taiof, Takia, Touo, Tungag, Yabem and Yeli_Dnye.

Family

a factor with levels Oceanic and Papuan.

Frics

a numeric vector, 1: presence, 0: absence

PrenasalizedStops

a numeric vector, 1: presence, 0: absence

PhonDistBetweenLAndR

a numeric vector, 1: presence, 0: absence

PhonVelarFricOrGlide

a numeric vector, 1: presence, 0: absence

PhonVoicingContrAmongStops

a numeric vector, 1: presence, 0: absence

PhonConsLength

a numeric vector, 1: presence, 0: absence

PhonVowelLength

a numeric vector, 1: presence, 0: absence

ContrPhonTypesForVowels

a numeric vector, 1: presence, 0: absence

PhonStress

a numeric vector, 1: presence, 0: absence

WordFinalConss

a numeric vector, 1: presence, 0: absence

ConsClusters

a numeric vector, 1: presence, 0: absence

DefOrSpecArt

a numeric vector, 1: presence, 0: absence

IndefOrNonSpecArt

a numeric vector, 1: presence, 0: absence

ArticleNounOrder

a numeric vector, 1: presence, 0: absence

NounInitNps

a numeric vector, 1: presence, 0: absence

InclExclDist

a numeric vector, 1: presence, 0: absence

PronNum

a numeric vector, 1: presence, 0: absence

PronRelationship

a numeric vector, 1: presence, 0: absence

PronConflation

a numeric vector, 1: presence, 0: absence

MoreThan2DegreesDistDem

a numeric vector, 1: presence, 0: absence

NonSpkrAnchoredDem

a numeric vector, 1: presence, 0: absence

VerticalityDem

a numeric vector, 1: presence, 0: absence

ClassifiedDem

a numeric vector, 1: presence, 0: absence

NumDeterminedDecl

a numeric vector, 1: presence, 0: absence

GenderDeterminedDecl

a numeric vector, 1: presence, 0: absence

SuppletiveNouns

a numeric vector, 1: presence, 0: absence

SingMarkedNoun

a numeric vector, 1: presence, 0: absence

DualMarkedNoun

a numeric vector, 1: presence, 0: absence

PlMarkedNoun

a numeric vector, 1: presence, 0: absence

OtherNumMarkedNoun

a numeric vector, 1: presence, 0: absence

LimitedDistNumMarking

a numeric vector, 1: presence, 0: absence

NounClassesGenders

a numeric vector, 1: presence, 0: absence

ConcordBeyondNp

a numeric vector, 1: presence, 0: absence

NumeralClassifiers

a numeric vector, 1: presence, 0: absence

PossClassifiers

a numeric vector, 1: presence, 0: absence

PossClasses

a numeric vector, 1: presence, 0: absence

Inalienability

a numeric vector, 1: presence, 0: absence

MultiplePossConstr

a numeric vector, 1: presence, 0: absence

PrefixMarkedPoss

a numeric vector, 1: presence, 0: absence

SuffixMarkedPoss

a numeric vector, 1: presence, 0: absence

MarkedPossr

a numeric vector, 1: presence, 0: absence

MarkedPossessee

a numeric vector, 1: presence, 0: absence

PossrPossdOrder

a numeric vector, 1: presence, 0: absence

DecimalNumerals

a numeric vector, 1: presence, 0: absence

QuinaryNumerals

a numeric vector, 1: presence, 0: absence

CollectiveNouns

a numeric vector, 1: presence, 0: absence

AdjVerbLexOverlap

a numeric vector, 1: presence, 0: absence

AdjAttributionPred

a numeric vector, 1: presence, 0: absence

CoreCaseMarking

a numeric vector, 1: presence, 0: absence

ObliqueCaseMarking

a numeric vector, 1: presence, 0: absence

Prepositions

a numeric vector, 1: presence, 0: absence

Postpositions

a numeric vector, 1: presence, 0: absence

TamPerson

a numeric vector, 1: presence, 0: absence

VerbPrefixesProclitics

a numeric vector, 1: presence, 0: absence

VerbSuffixesEnclitics

a numeric vector, 1: presence, 0: absence

PunctualContinuous

a numeric vector, 1: presence, 0: absence

RealisIrrealis

a numeric vector, 1: presence, 0: absence

SSuffix

a numeric vector, 1: presence, 0: absence

SPrefix

a numeric vector, 1: presence, 0: absence

ASuffix

a numeric vector, 1: presence, 0: absence

APrefix

a numeric vector, 1: presence, 0: absence

OSuffix

a numeric vector, 1: presence, 0: absence

OPrefix

a numeric vector, 1: presence, 0: absence

VerbVarTam

a numeric vector, 1: presence, 0: absence

VerbVarVClass

a numeric vector, 1: presence, 0: absence

VerbVarClauseType

a numeric vector, 1: presence, 0: absence

VerbVarPerson

a numeric vector, 1: presence, 0: absence

NumStemAlt

a numeric vector, 1: presence, 0: absence

PersonStemAlt

a numeric vector, 1: presence, 0: absence

SepVerbNumPerson

a numeric vector, 1: presence, 0: absence

Portmanteau3Plus

a numeric vector, 1: presence, 0: absence

DistributedCategory

a numeric vector, 1: presence, 0: absence

NonCore

a numeric vector, 1: presence, 0: absence

RecipientObj

a numeric vector, 1: presence, 0: absence

X3PlacePreds

a numeric vector, 1: presence, 0: absence

VerbNeg

a numeric vector, 1: presence, 0: absence

VerbDirection

a numeric vector, 1: presence, 0: absence

VerbSuppletion

a numeric vector, 1: presence, 0: absence

ConjugationClasses

a numeric vector, 1: presence, 0: absence

TransIntransAlt

a numeric vector, 1: presence, 0: absence

TransitivizingMorph

a numeric vector, 1: presence, 0: absence

IntranstivizingMorph

a numeric vector, 1: presence, 0: absence

ReflexiveMorph

a numeric vector, 1: presence, 0: absence

ReciprocalMorph

a numeric vector, 1: presence, 0: absence

VerbClassifiers

a numeric vector, 1: presence, 0: absence

Copula

a numeric vector, 1: presence, 0: absence

NonVbPreds

a numeric vector, 1: presence, 0: absence

SerialVerbConstr

a numeric vector, 1: presence, 0: absence

Auxiliaries

a numeric vector, 1: presence, 0: absence

VerbCompounds

a numeric vector, 1: presence, 0: absence

VerbAdjunctConstr

a numeric vector, 1: presence, 0: absence

VbIncorporation

a numeric vector, 1: presence, 0: absence

ExistentialVerb

a numeric vector, 1: presence, 0: absence

IrregularGive

a numeric vector, 1: presence, 0: absence

ClosedClassOfVb

a numeric vector, 1: presence, 0: absence

SvIntransClauses

a numeric vector, 1: presence, 0: absence

VsIntransClauses

a numeric vector, 1: presence, 0: absence

VInitTransClauses

a numeric vector, 1: presence, 0: absence

VMedialTransClauses

a numeric vector, 1: presence, 0: absence

VFinalTransClauses

a numeric vector, 1: presence, 0: absence

FixedConstituentOrder

a numeric vector, 1: presence, 0: absence

ClauseFinalNeg

a numeric vector, 1: presence, 0: absence

ClauseInitNeg

a numeric vector, 1: presence, 0: absence

ImpVs.DeclNeg

a numeric vector, 1: presence, 0: absence

VbAndNonVbPredIdentity

a numeric vector, 1: presence, 0: absence

SOMorphInBasicConstr

a numeric vector, 1: presence, 0: absence

SAMorphInBasicConstr

a numeric vector, 1: presence, 0: absence

SOMorphInComplexConstr

a numeric vector, 1: presence, 0: absence

SAMorphInComplexConstr

a numeric vector, 1: presence, 0: absence

SynConflationOfSO

a numeric vector, 1: presence, 0: absence

ControlledUncontrolled

a numeric vector, 1: presence, 0: absence

ClauseChaining

a numeric vector, 1: presence, 0: absence

SimultaneousSequential

a numeric vector, 1: presence, 0: absence

SayInDesidConstr

a numeric vector, 1: presence, 0: absence

RelativeClauses

a numeric vector, 1: presence, 0: absence

PurpSubClauses

a numeric vector, 1: presence, 0: absence

TemporalSubClauses

a numeric vector, 1: presence, 0: absence

ComplementClauses

a numeric vector, 1: presence, 0: absence

CausBySerialVerbConstr

a numeric vector, 1: presence, 0: absence

CausByBoundAffClit

a numeric vector, 1: presence, 0: absence

CausByConstrInvolvingSay

a numeric vector, 1: presence, 0: absence

MorphTopicOrFocus

a numeric vector, 1: presence, 0: absence

TailHeadLinkage

a numeric vector, 1: presence, 0: absence

VerbRedup

a numeric vector, 1: presence, 0: absence

NounRedup

a numeric vector, 1: presence, 0: absence