Data sets and functions for 'Analyzing Linguistic Data'

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

harald.baayen@gmail.com

Maintainer: harald.baayen@gmail.com

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)

Autocorrelation trellis graph

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)

Affix productivity

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)

Alice's Adventures in Wonderland

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]

Compute p-values for factors in mixed models

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)

Auxiliaries for regular and irregular verbs in Dutch

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)

Visual lexical decision with beginning readers

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)

Calculate condition number with intercept included

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)

Compare Lexical Richness of Two Texts

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. baayen@mpi.nl

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)

Class "corres"

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")

Correspondence Analysis

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: baayen@mpi.nl

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)

Initialize correspondence object

Description

Function initializing correspondence object.

Usage

corresInit(data)

Arguments

Value

A correspondence object.

Author(s)

R. Harald Baayen

Supplementary rows or columns in correspondence analysis

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: baayen@mpi.nl

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)

Danish auditory lexical decision

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)

Dative Alternation

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) 

Dative Alternation - simplified data set

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)

Extract degree of polynomial or knots for restricted cubic spline

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

Durational measurements on the Dutch prefix ge-

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)

Durational measurements on the Dutch prefix ont-

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)

Cross-entropy based distances between speakers

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)

Metadata for dutchSpeakersDist

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)

English visual lexical decision and naming latencies

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)

Etymological age and regularity in Dutch

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)

Frankfurter frequencies

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)

Final Devoicing in Dutch

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)

Extracts knots from variable name

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

calculate HPD prediction intervals

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

determine position for labels for interaction plots

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

Extracts range of predicted values from list of data frames

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)

extract simple name of predictor from expression with poly

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

Class "growth"

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")

Calculate vocabulary growth curve and vocabulary richness measures

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)

Conversion of growth object into a vgc object

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)

Initialize a vocabulary growth object.

Description

This function initializes a growth object.

Usage

growthInit(data)

Arguments

Value

A growth object, see growth.fnc for details.

Author(s)

R. H. Baayen

See Also

See Also as growth.fnc.

The determiner 'het' in the Dutch novel Max Havelaar

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)

Returns first rows of a growth object.

Description

Returns the first rows of a growth object.

Usage

	## S3 method for class 'growth'
head(x, n = 6, ...)

Arguments

Value

The first n rows of the growth object are printed.

Author(s)

R. H. Baayen

See Also

See Also growth.fnc.

Examples

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

## End(Not run)

Lexical decision latencies for words ending in -heid

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)

Herdan's C

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)

fMRI Filtered Signal and Priming Scores for Brain-Damaged Patients

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)

implement interactions in the model matrix

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

Function for by-item regression used by simulateRegression.fnc

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)

By-item anova for simulated data for quasi-F analysis

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)

Calculate vector at specified lag

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)

Simulated Latin Square data set with subjects and items

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)

Lexical decision latencies for 79 English nouns

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)

Lexical measures for 2233 English monomorphemic words

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)

Classification of lexical measures

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)

Plot the interaction of two linear numeric predictors in a model fitted with lmer

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)
  

Make a simulated data set with regression design

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)

Create model matrix with main effects only

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

generate simulated data set with nonlinear function

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)

Moby Dick

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)

Scatterplot of bivariate standard normal distribution

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)

Frequency spectrum for -ness in the demographic BNC

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)

Frequency spectrum for -ness in the context-governed BNC

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)

Frequency spectrum for -ness in the written BNC

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)

Frequencies of tag trigrams in Old French texts

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")

Meta data for the oldFrench data

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)

The Wonderful Wizard of Oz

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)

Scatterplot matrix with correlations

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)

parse character string specifying restricted cubic spline

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

The development of periphrastic do in English

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)

Phylogenetic relations between Papuan and Oceanic languages

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

Source

Dunn, M., Terrill, A., Reesink, G., Foley, R. A. and Levinson, S. C. (2005) Structural phylogenetics and the reconstruction of ancient language history, Science, 309, 2072-2075.

Examples

## Not run: 
data(phylogeny)
library(ape)

# joint analysis of both language families

phylogeny.dist = dist(phylogeny[ ,3:ncol(phylogeny)], method = "binary")
phylogeny.dist.tr = nj(phylogeny.dist)
families =
  as.character(phylogeny$Family[as.numeric(phylogeny.dist.tr$tip.label)])
languages =
  as.character(phylogeny$Language[as.numeric(phylogeny.dist.tr$tip.label)])
phylogeny.dist.tr$tip.label = languages
plot(phylogeny.dist.tr, type="u", font = as.numeric(as.factor(families)))

# analysis of subset of Papuan languages

papuan = phylogeny[phylogeny$Family == "Papuan",]
papuan$Language = as.factor(as.character(papuan$Language))
papuan.meta = papuan[ ,1:2]
papuan.mat = papuan[, 3:ncol(papuan)]
papuan.meta$Geography = c(
  "Bougainville", "Bismarck Archipelago", "Bougainville", 
  "Bismarck Archipelago", "Bismarck Archipelago", "Central Solomons",
  "Bougainville", "Louisiade Archipelago", "Bougainville", 
  "Bismarck Archipelago", "Bismarck Archipelago", 
  "Bismarck Archipelago", "Central Solomons", "Central Solomons", 
  "Central Solomons")
papuan.dist = dist(papuan.mat, method = "binary")
papuan.dist.tr = nj(papuan.dist)
fonts = 
  as.character(papuan.meta$Geography[as.numeric(
    papuan.dist.tr$tip.label)])
papuan.dist.tr$tip.label = 
  as.character(papuan.meta$Language[as.numeric(
    papuan.dist.tr$tip.label)])
plot(papuan.dist.tr, type = "u", font = as.numeric(as.factor(fonts)))

## End(Not run)

Plot method for correspondence objects

Description

This function defines a plot method for correspondence objects.

Usage

## S3 method for class 'corres'
plot(x, main = "", addcol = TRUE, extreme = 0, rcex = 1, rcol = 1, 
rlabels = "", stretch = 1.4, ccex = 1, ccol = 2, clabels = "", ...)

Arguments

Value

A plot on the graphics device.

Author(s)

R. H. Baayen

See Also

See Also corres.fnc, link{corsup.fnc}.

Examples

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

  oldFrench.ca = corres.fnc(oldFrench)

  plot(oldFrench.ca)

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

## End(Not run)

Plot method for growth objects

Description

This function defines the plot method for growth objects.

Usage

## S3 method for class 'growth'
plot(x, w = "all", ...)

Arguments

Value

A plot shown on the graphics device.

Author(s)

R. H. Baayen

See Also

See Also growth.fnc.

Examples

## Not run: 
  data(alice)
  alice.growth = growth.fnc(alice)
  plot(alice.growth)
  plot(alice.growth, w = "Yule")

## End(Not run)

create plot or plots for list with data frames for plot or subplots

Description

given a list with one or more data frames with values for a plot (or subplot), create the actual plots

Usage

plotAll.fnc(reslist, sameYrange = TRUE, ylabel, xlabel = NA, intrName = NA, 
  pos = "end", ylimit = NA, addlines=FALSE, cexsize = 0.6, conditioningVals=NA,
  conditioningColors=1, conditioningLines=1, lineColor=1, addToExistingPlot = FALSE, ...)

Arguments

Details

Note that reslist may contain as elements lists of data frames, these then specify the separate points or lines to be plotted for a given interaction

Value

A plot is produced on the graphics device.

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

plot a mer object

Description

Plot partial effects of a (generalized) linear mixed-effects model fit with lmer. For gaussian models, 95% highest posterior density credible intervals can be added.

Usage

plotLMER.fnc(model, xlabel = NA, xlabs = NA, ylabel = NA, ylimit = NA, 
   ilabel = NA, fun = NA, pred = NA, control = NA, ranefs = NA, n = 100, 
   intr = NA,  lockYlim = TRUE, addlines = FALSE, 
   withList = FALSE, cexsize = 0.5, linecolor = 1, addToExistingPlot = FALSE, 
   verbose = TRUE, ...)

Arguments

Details

When no predictor is specified, a series of plots is produced for the partial effects of each predictor. The graphs are shown for the reference level for factors and are adjusted for the median value for the other numerical predicors in the model. Interactions are not shown. The user should set up the appropriate number of subplots on the graphics device before running plotLMER.fnc().

Instead of showing all predictors jointly, plotLMER.fnc() can also be used to plot the partial effect of a specific predictor. When a specific predictor is specified (with pred = ...), a single plot is produced for that predictor. In this case, the intr argument can be used to specify a single second predictor that enters into an interaction with the selected main predictor.

Polynomials have to be fitted with poly(..., degree, raw=TRUE) and restricted cubic splines with rcs() from the rms package.

Value

A plot is produced on the graphical device.

Note

This code needs much more work, including (i) extension to poly with raw=FALSE, and (ii) general clean-up of the code.

Author(s)

R. H. Baayen

References

The 'danish' dataset in the example section is contributed by Laura Winther-Balling, see Winther-Balling, L. and Baayen, R. H., Morphological effects in auditory word recognition: Evidence from Danish, Language and Cognitive Processes, in press.

See Also

See also other utilities in languageR for facilitating work with lmer

Examples

  ## Not run: 

###########################################################################
# we will stay as close to the older optimizer of lme4 as possible -
# this requires the optimx package and using the control option of lmer()
###########################################################################
require(optimx)

###########################################################################
# fitting a cosine with a spline (simulated data)
###########################################################################

require("rms", quietly=TRUE, character=TRUE)
require("lme4", quietly=TRUE, character=TRUE)
dfr = makeSplineData.fnc()
table(dfr$Subject)
xylowess.fnc(Y ~ X | Subject, data = dfr)
# the smoother doesn't recognize the cosine function implemented in makeSplineData.fnc()
dev.off()   

dfr.lmer = lmer(Y ~ rcs(X, 5) + (1|Subject), data = dfr,
  control = lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
plotLMER.fnc(dfr.lmer)

# comparison with ols from Design package
dfr.lm = lm(Y~Subject+rcs(X), data=dfr, x=T, y=T)
dfr$fittedOLS = fitted(dfr.lm)
dfr$fittedLMER = as.vector(dfr.lmer@pp$X %*% fixef(dfr.lmer))

# 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",], 
  col="blue", ylim = c(24,30), xlab="X", ylab="Y", type="n")   

lines(dfr[dfr$Subject=="S1",]$X, dfr[dfr$Subject=="S1",]$fittedOLS, col="red")
lines(dfr[dfr$Subject=="S1",]$X, dfr[dfr$Subject=="S1",]$fittedLMER, col="blue")
lines(dfr[dfr$Subject=="S1",]$X, dfr[dfr$Subject=="S1",]$y+
  ranef(dfr.lmer)[[1]]["S1",], col="green")
legend(2,30,c("30+cos(x)", "lmer (S1)", "ols (S1)"), lty=rep(1,3), 
  col=c("green", "blue", "red"))


#############################################################
# a model with a raw polynomial
#############################################################

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

pars = par()
par(mfrow=c(3,3), mar=c(5,5,1,1))
plotLMER.fnc(bg.lmer, fun=exp, ylabel = "RT (ms)")

#############################################################
# a model with an interaction involving numeric predictors
#############################################################

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,
  subset=abs(scale(resid(danish.lmer)))<2.5,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))

# plot for reference level of Sex
plotLMER.fnc(danish.lmerA, pred = "LogAffixFreq", 
  intr=list("LogWordFreq", round(quantile(danish$LogWordFreq),3), "beg",
  list(c("red", "green", "blue", "yellow", "purple"), rep(1,5))), 
  ylimit=c(6.5,7.0))

# this model has a significant three-way interaction
# for visualization, we can either relevel Sex and refit,
# or make use of the control option. First releveling:

danish$Sex=relevel(danish$Sex, "F")
danish.lmerF = 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$Sex=relevel(danish$Sex, "M")
danish.lmerM = 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")))

# Next preparing for using the control option:
#
# names(fixef(danish.lmer))[10]     # SexM
# unique(danish.lmer@pp$X[,10])     # 1 0

par(mfrow=c(2,2))

plotLMER.fnc(danish.lmer, pred="LogWordFreq", ylimit=c(6.5,7.0),
intr=list("LogAffixFreq", round(quantile(danish$LogAffixFreq),2), "end"),
control=list("SexM", 0))
mtext("females", line=1.5, cex=0.9)

plotLMER.fnc(danish.lmer, pred="LogWordFreq", ylimit=c(6.5,7.0),
intr=list("LogAffixFreq", round(quantile(danish$LogAffixFreq),2), "end"),
control=list("SexM", 1))
mtext("males", line=1.5, cex=0.9)

plotLMER.fnc(danish.lmerF, pred="LogWordFreq", ylimit=c(6.5,7.0), 
intr=list("LogAffixFreq", round(quantile(danish$LogAffixFreq),2), "end"))
mtext("females", line=1.5, cex=0.9)

plotLMER.fnc(danish.lmerM, pred="LogWordFreq", ylimit=c(6.5, 7.0),
intr=list("LogAffixFreq", round(quantile(danish$LogAffixFreq),2), "end"))
mtext("males", line=1.5, cex=0.9)

par(mfrow=c(1,1))

#############################################################
# calculating effect sizes, defined as max - min
#############################################################

# effect size for a covariate

dfr = plotLMER.fnc(danish.lmerA, pred = "LogCUP", withList=TRUE)
max(dfr$LogCUP$Y)-min(dfr$LogCUP$Y)

# effect size for a factor

dfr = plotLMER.fnc(danish.lmerA, pred = "PrevError", withList=TRUE)
max(dfr$PrevError$Y)-min(dfr$PrevError$Y)


# effect sizes for the quantiles in an interaction plot

dfr = plotLMER.fnc(danish.lmerA, pred = "LogAffixFreq", 
  withList=TRUE,
  intr=list("LogWordFreq", round(quantile(danish$LogWordFreq),3), "beg"))

unlist(lapply(dfr$LogAffixFreq, FUN=function(X)return(max(X$Y)-min(X$Y))))


#############################################################
# plotting an interaction between two factors
#############################################################

danish$WordFreqFac = danish$LogWordFreq > median(danish$LogWordFreq)
danish.lmer2 = lmer(LogRT ~ WordFreqFac*Sex +  
  (1|Subject) + (1|Word) + (1|Affix), data = danish,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))

plotLMER.fnc(danish.lmer2, pred = "Sex", 
  intr=list("WordFreqFac", c("TRUE", "FALSE"), "end", 
  list(c("red",  "blue"), rep(1,2))),
  ylimit=c(6.7,6.9), cexsize=1.0, addlines=TRUE)

#############################################################
# a generalized linear mixed-effects model
#############################################################

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

par(mfrow=c(3,4),mar=c(5,5,1,1))
plotLMER.fnc(dative.lmer, fun=plogis, addlines=TRUE)

# with user-specified labels for the x-axis
par(mfrow=c(3,4),mar=c(5,5,1,1))
plotLMER.fnc(dative.lmer, fun=plogis, addlines=TRUE,
  xlabs=unlist(strsplit("abcdefghij","")))

par(pars)


  
## End(Not run)

Plot for goodness of fit of logistic regression

Description

This function plots observed proportions against mean predicted probabilities. For a good fit, points should be approximately on a straight line.

Usage

plotlogistic.fit.fnc(x, data, method, where, scalesize, ...)

Arguments

.

Value

A plot is produced on the graphics device. The R-squared value shown above the plot represents the correlation between the X and Y values in the plot. It does NOT represent the R-squared of the lrm or lmer model.

Author(s)

R. H. Baayen

Examples

## Not run: 
data(dative)
require(lme4)
require(rms)
require(lmerTest)
require(optimx)

dative.lrm = lrm(RealizationOfRecipient ~ AccessOfTheme + 
   AccessOfRec + LengthOfRecipient + AnimacyOfRec +
   AnimacyOfTheme + PronomOfTheme + DefinOfTheme + LengthOfTheme +
   SemanticClass + Modality, 
   data = dative)

dative.glmm = 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")

par(mfrow=c(2,2))
plotlogistic.fit.fnc (dative.lrm, dative)
mtext("lrm", 3, 3)
plotlogistic.fit.fnc (dative.glmm, dative)
mtext("lmer", 3, 3)
plotlogistic.fit.fnc (dative.lrm, dative, scalesize=0.2)
mtext("lrm", 3, 3)
plotlogistic.fit.fnc (dative.glmm, dative, method="shingle")
mtext("lmer", 3, 3)
par(mfrow=c(1,1))



## End(Not run)

determine X and Y values for a given (sub)plot

Description

this function figures out the X and Y values for a given (sub)plot, including upper and lower 95% HPD intervals

Usage

preparePredictor.fnc(pred, model, m, ylabel, fun, val, xlabel, ranefs, ...)

Arguments

Value

A data frame with values to be plotted, with columns

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.

Primed lexical decision latencies for neologisms ending in -heid

Description

Primed lexical decision latencies for Dutch neologisms ending in the suffix -heid.

Usage

data(primingHeid)

Format

A data frame with 832 observations on the following 13 variables.

Subject

a factor with subjects as levels.

Word

a factor with words as levels.

Trial

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

RT

a numeric vector with log-transformed lexical decision latencies.

Condition

a factor coding the priming treatmen, with levels baseheid (prime is the base word) and heid (the prime is the neologism)

Rating

a numeric vector for subjective frequency estimates.

Frequency

a numeric vector for log-transformed frequencies of the whole word.

BaseFrequency

a numeric vector for the log-transformed frequencies of the base word.

LengthInLetters

a numeric vector coding orthographic length in letters.

FamilySize

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

NumberOfSynsets

a numeric vector for the number of synonym sets in WordNet in which the base is listed.

ResponseToPrime

a factor with levels correct and incorrect for the response to the prime.

RTtoPrime

a numeric vector for the log-transformed reaction time to the prime.

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(primingHeid)

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

primingHeid.lmer = lmer(RT ~ RTtoPrime * ResponseToPrime + Condition +
  (1|Subject) + (1|Word), 
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
  data = primingHeid)
summary(primingHeid.lmer)

# model criticism

primingHeid.lmer = lmer(RT ~ RTtoPrime * ResponseToPrime + Condition +
  (1|Subject) + (1|Word), 
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
  data = primingHeid[abs(scale(resid(primingHeid.lmer)))<2.5,])
summary(primingHeid.lmer)

## End(Not run)

Primed lexical decision latencies for neologisms ending in -heid

Description

Primed lexical decision latencies for Dutch neologisms ending in the suffix -heid, with information on RTs to preceding trials added to the data already in primingHeid.

Usage

data(primingHeidPrevRT)

Format

A data frame with 832 observations on the following 17 variables.

Subject

a factor with subjects as levels.

Word

a factor with words as levels.

Trial

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

RT

a numeric vector with log-transformed lexical decision latencies.

Condition

a factor coding the priming treatmen, with levels baseheid (prime is the base word) and heid (the prime is the neologism)

Rating

a numeric vector for subjective frequency estimates.

Frequency

a numeric vector for log-transformed frequencies of the whole word.

BaseFrequency

a numeric vector for the log-transformed frequencies of the base word.

LengthInLetters

a numeric vector coding orthographic length in letters.

FamilySize

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

NumberOfSynsets

a numeric vector for the number of synonym sets in WordNet in which the base is listed.

ResponseToPrime

a factor with levels correct and incorrect for the response to the prime.

RTtoPrime

a numeric vector for the log-transformed reaction time to the prime.

RTmin1

a numeric vector for reaction time in ms to the item preceding the target.

RTmin2

a numeric vector for reaction time in ms to the item preceding the target by two trials.

RTmin3

a numeric vector for reaction time in ms to the item preceding the target by three trials.

RTmin4

a numeric vector for reaction time in ms to the item preceding the target by four trials.

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(primingHeidPrevRT)

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

primingHeid.lmer = lmer(RT ~ RTtoPrime * ResponseToPrime + Condition + 
  log(RTmin1) + (1|Subject) + (1|Word), data = primingHeidPrevRT,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
summary(primingHeid.lmer)

## End(Not run)

Print method for correspondence object

Description

Prints eigenvalues and eigenvalue rates for a correspondence object.

Usage

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

Arguments

Value

Report of eigenvalues and eigenvalue rates.

Author(s)

R. H. Baayen

See Also

See also corres.fnc.

Examples

## Not run: 
  data(oldFrench)
  oldFrench.ca = corres.fnc(oldFrench)
  oldFrench.ca

## End(Not run)

Print method for growth objects.

Description

Print method for growth objects.

Usage

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

Arguments

Value

The data frame with chunk sizes and associated vocabulary statistics is printed. To access the data frame that is being shown, use <my.growth.object>@data$data.

Author(s)

R. H. Baayen

See Also

See also growth.fnc.

Examples

## Not run: 
  data(alice)
  alice.growth = growth.fnc(alice)
  alice.growth
  # for accessing the printed data frame:
  alice.growth@data$data[1:4,]

## End(Not run)

Compute p-values and MCMC confidence intervals for mixed models

Description

This function used to calculate p-values and HPD intervals for the parameters of models fitted with lmer.

As MCMC is no longer supported by lme4, this function is now obsolete and does no longer produce any output, other than a warning.

See the lme4 function pvalues() for alternatives.

Usage

pvals.fnc(object, ...)

Arguments

Value

A warning.

Author(s)

R. H. Baayen

See Also

pvalues

Examples

  ## Not run: 
  data(primingHeid) 
  library(lme4)

  # remove extreme outliers
  primingHeid = primingHeid[primingHeid$RT < 7.1,]

  # fit mixed-effects model

  # we will stay as close to the older optimizer of lme4 as possible -
  # this requires the optimx package and using the control option of lmer()

  require(optimx)
  require(lmerTest)

  primingHeid.lmer = lmer(RT ~ RTtoPrime * ResponseToPrime + 
    Condition + (1|Subject) + (1|Word), data = primingHeid,
    control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
  summary(primingHeid.lmer)
  anova(primingHeid.lmer)
  
## End(Not run)

Quasi-F test

Description

The textbook Quasi-F test for a design with subjects, items, and a single factorial predictor. Included for educational purposes for this specific design only.

Usage

quasiF.fnc(ms1, ms2, ms3, ms4, df1, df2, df3, df4)

Arguments

Value

A list with components

Author(s)

R. H. Baayen

See Also

See Also as quasiFsim.fnc.

Examples

  data(quasif)

  quasif.lm = lm(RT ~ SOA + Item + Subject + 
     SOA:Subject + Item:Subject, data = quasif)
  quasif.aov = anova(quasif.lm)

  quasiF.fnc(quasif.aov["SOA","Mean Sq"], 
     quasif.aov["Item:Subject", "Mean Sq"],
     quasif.aov["SOA:Subject", "Mean Sq"], 
     quasif.aov["Item", "Mean Sq"],
     quasif.aov["SOA","Df"], 
     quasif.aov["Item:Subject", "Df"],
     quasif.aov["SOA:Subject", "Df"], 
     quasif.aov["Item", "Df"])

  # much simpler is
  quasiFsim.fnc(quasif)$quasiF

Quasi-F test for specific simple design

Description

This function carries out a Quasi-F test for data with columns labelled SOA, Subject, Item. This function is called by simulate.quasif.fnc, and is not intended for general use.

Usage

quasiFsim.fnc(dat)

Arguments

Value

A list with components

Author(s)

R. H. Baayen

See Also

See Also quasiF.fnc.

Examples

    data(quasif)
    quasiFsim.fnc(quasif)$quasiF

Simulated data set with subjects and items requiring quasi-F ratios

Description

Simulated lexical decision latencies with SOA as treatment, traditionally requiring an analysis using quasi-F ratios, as available in Raaijmakers et al. (1999).

Usage

data(quasif)

Format

A data frame with 64 observations on the following 4 variables.

Subject

a factor coding subjects.

RT

a numeric vector for simulated reaction times in lexical decision.

Item

a factor coding items.

SOA

a factor coding SOA treatment with levels long and short.

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(quasif)
items.quasif.fnc(quasif)

## End(Not run)

Ratings for 81 English nouns

Description

Subjective frequency ratings, ratings of estimated weight, and ratings of estimated size, averaged over subjects, for 81 concrete English nouns.

Usage

data(ratings)

Format

A data frame with 81 observations on the following 14 variables.

Word

a factor with words as levels.

Frequency

a numeric vector of logarithmically transformed frequencies

FamilySize

a numeric vector of logarithmically transformed morphological family sizes.

SynsetCount

a numeric vector with logarithmically transformed counts of the number of synonym sets in WordNet in which the word is listed.

Length

a numeric vector for the length of the word in letters.

Class

a factor with levels animal and plant.

FreqSingular

a numeric vector for the frequency of the word in the singular.

FreqPlural

a numeric vector with the frequency of the word in the plural.

DerivEntropy

a numeric vector with the derivational entropies of the words.

Complex

a factor coding morphological complexity with levels complex and simplex.

rInfl

a numeric vector coding the log of ratio of singular to plural frequencies.

meanWeightRating

a numeric vector for the estimated weight of the word's referent, averaged over subjects.

meanSizeRating

a numeric vector for the estimated size of the word's referent, averaged over subjects.

meanFamiliarity

a numeric vector with subjective frequency estimates, averaged over subjects.

Source

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

Examples

## Not run: 
data(ratings)

ratings.lm = lm(meanSizeRating ~ meanFamiliarity * Class + 
I(meanFamiliarity^2), data = ratings)

ratings$fitted = fitted(ratings.lm)

plot(ratings$meanFamiliarity, ratings$meanSizeRating,       
xlab = "mean familiarity", ylab = "mean size rating", type = "n")
text(ratings$meanFamiliarity, ratings$meanSizeRating, 
substr(as.character(ratings$Class), 1, 1), col = 'darkgrey')

plants = ratings[ratings$Class == "plant", ]    
animals = ratings[ratings$Class == "animal", ]  
plants = plants[order(plants$meanFamiliarity),]
animals = animals[order(animals$meanFamiliarity),]

lines(plants$meanFamiliarity, plants$fitted)
lines(animals$meanFamiliarity, animals$fitted)

## End(Not run)

Regular and irregular Dutch verbs

Description

Regular and irregular Dutch verbs and selected lexical and distributional properties.

Usage

data(regularity)

Format

A data frame with 700 observations on the following 13 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.

VerbalSynsets

a numeric vector for the number of verbal synsets in WordNet.

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.

FamilySize

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

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(regularity)

# ---- predicting regularity with a logistic regression model

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

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

anova(regularity.lrm)

# ---- model validation

validate(regularity.lrm, bw = TRUE, B = 200)
pentrace(regularity.lrm, seq(0, 0.8, by = 0.05))
regularity.lrm.pen = update(regularity.lrm, penalty = 0.6)
regularity.lrm.pen

# ---- a plot of the partial effects

plot(Predict(regularity.lrm.pen))

# predicting regularity with a support vector machine

library(e1071)
regularity$AuxNum = as.numeric(regularity$Auxiliary)
regularity.svm = svm(regularity[, -c(1,8,10)], regularity$Regularity, cross=10)
summary(regularity.svm)

## End(Not run)

Self-paced reading latencies for Dutch neologisms

Description

Self-paced reading latencies for Dutch neologisms ending in the suffix -heid.

Usage

data(selfPacedReadingHeid)

Format

A data frame with 1280 observations on the following 18 variables.

Subject

a factor with subjects as levels.

Word

a factor with words as levels.

RT

a numeric vector with logarithmically transformed reading latencies.

RootFrequency

a numeric vector for the logarithmically transformed frequency of the lowest-level base of the neologism (e.g., lob in [[[lob]+ig]+heid].

Condition

a factor for the priming conditions with levels baseheid (neologism is preceded 40 trials back by its base word) and heidheid (the neologism is preceded 40 trials back by itself).

Rating

a numeric vector for the word's subjective frequency estimate.

Frequency

a numeric vector for the neologism's frequency (all zero).

BaseFrequency

a numeric vector for the base adjective underlying the neologism (e.g., lobbig in [[[lob]+ig]+heid]).

LengthInLetters

a numeric vector coding word length in letters.

FamilySize

a numeric vector for the logaritmically transformed count of a word's morphological family members.

NumberOfSynsets

a numeric vector for the count of synonym sets in WordNet in which the word is listed.

RT4WordsBack

a numeric vector for the log-transformed reading latencies four trials back.

RT3WordsBack

a numeric vector for the log-transformed reading latencies three trials back.

RT2WordsBack

a numeric vector for the log-transformed reading latencies two trials back.

RT1WordBack

a numeric vector for the log-transformed reading latencies one trial back.

RT1WordLater

a numeric vector for the log-transformed reading latencies one trial later.

RT2WordsLater

a numeric vector for the log-transformed reading latencies two trials later.

RTtoPrime

a numeric vector for the log-transformed reading latency for the prime.

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(selfPacedReadingHeid)

# data validation
plot(sort(selfPacedReadingHeid$RT))   
selfPacedReadingHeid = selfPacedReadingHeid[selfPacedReadingHeid$RT > 5 & 
  selfPacedReadingHeid$RT < 7.2,]

# fitting a mixed-effects model

require(lme4)
require(lmerTest)
require(optimx)
x = selfPacedReadingHeid[,12:15]
x.pr = prcomp(x, center = TRUE, scale = TRUE)
selfPacedReadingHeid$PC1 = x.pr$x[,1]

selfPacedReadingHeid.lmer = lmer(RT ~ RTtoPrime + LengthInLetters + 
  PC1 * Condition + (1|Subject) + (1|Word), 
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
  data = selfPacedReadingHeid)  
summary(selfPacedReadingHeid.lmer)

# model criticism

selfPacedReadingHeid.lmerA = lmer(RT ~ RTtoPrime + LengthInLetters + 
  PC1 * Condition + (1|Subject) + (1|Word), 
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
  data = selfPacedReadingHeid[abs(scale(resid(selfPacedReadingHeid.lmer))) < 2.5, ])

qqnorm(resid(selfPacedReadingHeid.lmerA))
summary(selfPacedReadingHeid.lmerA)

## End(Not run)

Shade rejection region for normal probability density function

Description

This function plots the standord normal probability density function and shades the rejection region.

Usage

shadenormal.fnc(qnts = c(0.025, 0.975))

Arguments

Value

A plot on the graphics device.

Type shadenormal.fnc to see the code. The polygon() function used for the shaded areas takes a sequence of X and Y coordinates, connects the corresponding points, and fills the area(s) enclosed with a specified color. To understand the use of polygon(), one can best think of making a polygon with a set of pins, a thread, and a board. Outline the polygon by placing the pins on the board at the corners of the polygon. First fasten the thread to one of the pins, then connect the thread to the second pin, from there to the third pin, and so on, until the first pin has been reached. What polygon() requires as input is a vector of the X-coordinates of the pins, and a vector of their Y-coordinates. These coordinates should be in exactly the order in which the thread is to be connected from pin to pin.

For shading the left rejection area, we specify the vectors of X and Y coordinates, beginning at the leftmost point of the tail, proceding to the right edge of the shaded area, then up, and finally to the left and down to the starting point, thereby closing the polygon. The X-coordinates are therefore specified from left to right, and then from right to left. The corresponding Y-coordinates are all the zeros necessary to get from $-3$ to $1.96$ (the default, qnorm(0.025)), and then the Y-coordinates of the density in reverse order to return to where we began.

Author(s)

R. H. Baayen

Examples

## Not run: 
shadenormal.fnc()

## End(Not run)

Plot method for growth objects.

Description

A print method for growth objects created with growth.fnc.

Usage

## S3 method for class 'growth'
show(x)

Arguments

Value

Prints growth object. To access the data frame embedded in the growth object, use <my.growth.object>@data$data.

Author(s)

R. H. Baayen

See Also

See Also growth.fnc.

Examples

## Not run: 
  data(alice)
  alice.growth = growth.fnc(alice, chunks= c(5000, 10000, 15000))
  alice.growth

## End(Not run)

Data set illustrating shrinkage

Description

Simulated data set for illustrating shrinkage.

Usage

data(shrinkage)

Format

A data frame with 200 observations on the following 6 variables.

intercept

a numeric vector for the intercept.

frequency

a numeric vector for word frequency.

subject

a factor for subjects with levels S1, S2, ... , S10.

error

a numeric vector for residuals.

ranef

a numeric vector for random effect.

RT

a numeric vector for simulated RTs.

Examples

## Not run: 
data(shrinkage)

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

shrinkage.lmer = lmer(RT ~ frequency + (1|subject), 
  data = shrinkage,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb"))
shrinkage.lmList = lmList(RT ~ frequency | subject, data = shrinkage)

# and visualize the difference between random regression 
# and mixed-effects regression

mixed = coef(shrinkage.lmer)[[1]]
random = coef(shrinkage.lmList)
subj = unique(shrinkage[,c("subject", "ranef")])
subj = subj[order(subj$subject),]
subj$random = random[,1]
subj$mixed = mixed[,1]
subj = subj[order(subj$random),]
subj$rank = 1:nrow(subj)

par(mfrow=c(1,2))
plot(subj$rank, subj$random, xlab="rank", ylab="RT", ylim=c(200,550), type="n")
text(subj$rank, subj$random, as.character(subj$subject), cex=0.8, col="red")
mtext("random regression", 3, 1)
points(subj$rank, 400+subj$ranef, col="blue")
abline(h=400)
plot(subj$rank, subj$mixed, xlab="rank", ylab="RT", ylim=c(200,550), type="n")
text(subj$rank, subj$mixed, as.character(subj$subject), cex=0.8, col = "red")
mtext("mixed-effects regression", 3, 1)
points(subj$rank, 400+subj$ranef, col="blue")
abline(h=400)
par(mfrow=c(1,1))

## End(Not run)

Simulate simple Latin Square data and compare models

Description

This function creates a user-specified number of simulated datasets with a Latin Square design, and compares mixed-effects models with the by-subject anova.

Usage

simulateLatinsquare.fnc(dat, with = TRUE, trial = 0, nruns = 100, 
   nsub = NA, nitem = NA, ...)

Arguments

Value

A list with components

Author(s)

R. H. Baayen

Examples

## Not run: 
  data(latinsquare)
	\dontrun{
	library(lme4)
  simulateLatinsquare.fnc(latinsquare, nruns=100)
	}

## End(Not run)

Simulate data for quasi-F analysis and compare models

Description

This function creates a user-specified number of simulated datasets, and compares mixed-effects models with quasi-F and F1 and F2 analyses. It should be run with the version of R and the version of languageR used by Baayen, Davidson & Bates (2008, JML), as mcmcsamp no longer supports models with random correlation parameters.

Usage

simulateQuasif.fnc(dat, with = TRUE, nruns = 100, nsub = NA, nitem = NA, ...)

Arguments

Details

Model parameters are estimated from the input data set.

For each completed simulation run, a dot is added to the R console.

Value

A list with components

Author(s)

R. H. Baayen

See Also

See also subjects.quasif.fnc.

Examples

## Not run: 
data(quasif)
library(lme4)

quasif.sim = simulateQuasif.fnc(quasif, nruns = 1000, with = TRUE) 
quasif.sim$alpha05

## End(Not run)

Simulate regression data and compare models

Description

This function creates a user-specified number of simulated regression datasets, and compares mixed-effects regression with random regression, by-subject regression, by-item regression, and by-subject plus by-item regression. Optionally, an effect of learning or fatigue can be incorporated.

Usage

simulateRegression.fnc(beta = c(400, 2, 6, 4), nitem = 20, nsubj = 10, 
stdevItem = 40, stdevSubj = 80, stdevError = 50, nruns = 100, learn = FALSE, 
learnRate = 10, ...)

Arguments

Value

A list with components

As this may take some time, the index of each completed run is shown on the output device.

Author(s)

R. H. Baayen

See Also

See Also make.reg.fnc.

Examples

## Not run: 
	library(lme4)
  simulateRegression.fnc(beta = c(400, 2, 6, 4), nruns = 5)

  \dontrun{simulateRegression.fnc(beta = c(400, 2, 6, 0), nruns = 1000, learn = TRUE)}

## End(Not run)

Size ratings for 81 English concrete nouns

Description

Subjective estimates of the size of the referents of 81 English concrete nouns, collected from 38 subjects.

Usage

data(sizeRatings)

Format

A data frame with 3078 observations on the following 7 variables.

Rating

a numeric vector with subjective estimates of the size of the word's referent.

Subject

a factor with subjects as levels.

Word

a factor with words as levels.

Class

a factor with levels animal and plant.

Naive

a factor with levels naive and notNaive, coding whether the subject new about the purpose of the experiment.

Language

a factor with levels English and notEnglish coding whether the subject was a native speaker of English.

MeanFamiliarity

a numeric vector for the by-item mean familiarity ratings.

Details

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

Examples

## Not run: 
data(sizeRatings)
require(lme4)
require(lmerTest)
require(optimx)
sizeRatings.lmer = lmer(Rating ~ Class * Naive + 
  MeanFamiliarity * Language + (1|Subject) + (1|Word), 
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
  data = sizeRatings)
summary(sizeRatings.lmer)

## End(Not run)

Relative frequencies of tag trigrams is selected Spanish texts

Description

Relative frequencies of the 120 most frequent tag trigrams in 15 texts contributed by 3 authors.

Usage

data(spanish)

Format

A data frame with 120 observations on 15 variables documented in spanishMeta.

References

Spassova, M. S. (2006) Las marcas sintacticas de atribucion forense de autoria de textos escritos en espanol, Masters thesis, Institut Universitari de Linguistica Aplicada, Universitat Pompeu Fabra, Barcelona.

Examples

## Not run: 
data(spanish)
data(spanishMeta)

# principal components analysis

spanish.t = t(spanish)
spanish.pca = prcomp(spanish.t, center = TRUE, scale = TRUE)
spanish.x = data.frame(spanish.pca$x)
spanish.x = spanish.x[order(rownames(spanish.x)), ]

library(lattice)
splom(~spanish.x[ , 1:3], groups = spanishMeta$Author)

# linear discriminant analysis

library(MASS)
spanish.pca.lda = lda(spanish.x[ , 1:8], spanishMeta$Author)
plot(spanish.pca.lda)

# cross-validation

n = 8
spanish.t = spanish.t[order(rownames(spanish.t)), ]
predictedClasses = rep("", 15)
for (i in 1:15) {
  training = spanish.t[-i,]                     
  trainingAuthor = spanishMeta[-i,]$Author
  training.pca = prcomp(training, center=TRUE, scale=TRUE)
  training.x = data.frame(training.pca$x)
  training.x = training.x[order(rownames(training.x)), ]
  training.pca.lda = lda(training[ , 1:n], trainingAuthor)
  predictedClasses[i] = 
  as.character(predict(training.pca.lda, spanish.t[ , 1:n])$class[i])  
}

ncorrect = sum(predictedClasses==as.character(spanishMeta$Author))
ncorrect
sum(dbinom(ncorrect:15, 15, 1/3))

## End(Not run)

Relative frequencies of function words in selected Spanish texts

Description

Relative frequencies of the 120 most frequent function words in 15 texts contributed by 3 authors.

Usage

data(spanishFunctionWords)

Format

A data frame with 120 observations on 15 variables documented in spanishMeta.

References

Spassova, M. S. (2006) Las marcas sintacticas de atribucion forense de autoria de textos escritos en espanol, Masters thesis, Institut Universitari de Linguistica Aplicada, Universitat Pompeu Fabra, Barcelona.

Examples

## Not run: 
data(spanishFunctionWords)
data(spanishMeta)

# principal components analysis

spanishFunctionWords.t = t(spanishFunctionWords)
spanishFunctionWords.t = 
  spanishFunctionWords.t[order(rownames(spanishFunctionWords.t)), ]
spanishFunctionWords.pca = 
  prcomp(spanishFunctionWords.t, center = TRUE, scale = TRUE)

sdevs = spanishFunctionWords.pca$sdev^2
n = sum(sdevs/sum(sdevs)> 0.05) 

# linear discriminant analysis with cross-validation

library(MASS)

predictedClasses = rep("", 15)
for (i in 1:15) {
  training = spanishFunctionWords.t[-i,]                   
  trainingAuthor = spanishMeta[-i,]$Author
  training.pca = prcomp(training, center = TRUE, scale = TRUE)
  training.x = data.frame(training.pca$x)
  training.x = training.x[order(rownames(training.x)), ]
  training.pca.lda = lda(training[ , 1:n], trainingAuthor)
  cl = predict(training.pca.lda, spanishFunctionWords.t[,1:n])$class[i]
  predictedClasses[i] = as.character(cl)
}

ncorrect = sum(predictedClasses==spanishMeta$Author)
sum(dbinom(ncorrect:15, 15, 1/3))

## End(Not run)

Metadata for the spanish and spanishFunctionWords data sets

Description

By-text metadata for the spanish and spanishFunctionWords data sets.

Usage

data(spanishMeta)

Format

A data frame with 15 observations on the following 6 variables.

Author

a factor with levels C, M, and V.

YearOfBirth

a numeric vector with year of birth of the author.

TextName

a factor with codes for the texts as levels ( X14458gll ... X14476gll).

PubDate

a numeric vector with data of publication of the text.

Nwords

a numeric vector with text sizes in tokens.

FullName

a factor with author names: Cela, Mendoza and VargasLLosa.

References

Spassova, M. S. (2006) Las marcas sintacticas de atribucion forense de autoria de textos escritos en espanol, Masters thesis, Institut Universitari de Linguistica Aplicada, Universitat Pompeu Fabra, Barcelona.

Examples

## Not run: 
data(spanishMeta)

## End(Not run)

Frequency spectrum from text vector

Description

This function creates a frequency spectrum for a text in character vector form.

Usage

spectrum.fnc(text)

Arguments

Value

A data frame with as column variables

Author(s)

R. H. Baayen

References

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

See Also

See Also the zipfR package.

Examples

## Not run: 
data(alice)
alice.spectrum = spectrum.fnc(alice)
head(alice.spectrum)
tail(alice.spectrum)

## End(Not run)

Simulated data set with split plot design

Description

Simulated lexical decision latencies with priming as treatment and reaction time in lexical decision as dependent variable.

Usage

data(splitplot)

Format

A data frame with 800 observations on the following 11 variables.

items

A factor with levels w1, w2, ..., w40, coding 40 word items.

ritems

The by-word random adjustments to the intercept.

list

A factor with levels listA and listB. The priming effect is counterbalanced for subjects across these two lists, compare table(splitplot$list, splitplot$subjects).

rlist

The by-list random adjustments to the intercept.

priming

A treatment factor with levels primed and unprimed.

fpriming

The priming effect, -30 for the primed and 0 for the unprimed condition.

subjects

A factor with levels s1, s2, ... s20 coding 20 subjects.

rsubject

The by-subject random adjustments to the intercept.

error

The by-observation noise.

int

The intercept.

RT

The reaction time.

Source

R. H. Baayen, D. J. Davidson and D. M. Bates. Mixed-effects modeling with crossed random effects for subjects and items. Manuscript under revision for Journal of Memory and Language.

Examples

## Not run: 
data(splitplot)
table(splitplot$list, splitplot$subjects)
dat=splitplot
require(lme4)
require(optimx)
require(lmerTest)
dat.lmer1 = lmer(RT ~ list*priming+(1+priming|subjects)+(1+list|items),data=dat,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
dat.lmer2 = lmer(RT ~ list*priming+(1+priming|subjects)+(1|items),data=dat,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
dat.lmer3 = lmer(RT ~ list*priming+(1|subjects)+(1|items),data=dat,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
dat.lmer4 = lmer(RT ~ list*priming+(1|subjects),data=dat,
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
anova(dat.lmer1, dat.lmer2, dat.lmer3, dat.lmer4)
summary(dat.lmer3)

## End(Not run)

By-subject analysis of simple Latin Square data sets

Description

This function is called by simulateLatinsquare.fnc for by-subject analysis of simulated Latin Square datasets. It is not intended for independent use.

Usage

subjects.latinsquare.fnc(dat)

Arguments

Value

A list with components

Author(s)

R. H. Baayen

See Also

See also simulateLatinsquare.fnc.

Examples

## Not run: 
data(latinsquare)
subjects.latinsquare.fnc(latinsquare)$p

## End(Not run)

By-subject analysis of data sets requiring quasi-F ratios

Description

This function is called by simulateQuasif.fnc for by-subject analysis of simulated datasets traditionally requiring quasi-F ratios. It is not intended for independent use.

Usage

subjects.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)
  subjects.quasif.fnc(quasif)

## End(Not run)

Summarize a correspondence object

Description

This function provides a concise summary of a correspondence object.

Usage

## S3 method for class 'corres'
summary(object, n = 2, returnList = FALSE, head = TRUE, ...)

Arguments

Value

A summary with eigenvalue rates, and coordinates, correlations, and contributions for the factors (by default, 2, unless n is set to a higher number).

Author(s)

R. H. Baayen

See Also

See also corres.fnc.

Examples

## Not run: 
  data(oldFrench)
  oldFrench.ca = corres.fnc(oldFrench)
  oldFrench.ca
  summary(oldFrench.ca)

## End(Not run)

Summary method for growth objects

Description

Summary method for vocabulary growth objects created with growth.fnc.

Usage

## S3 method for class 'growth'
summary(object, ...)

Arguments

Value

The growth object is printed. For access to the data frame inside the object, use <my.growth.object>@data$data.

Author(s)

R. H. Baayen

See Also

See also growth.fnc.

Show last rows of growth object.

Description

Prints last rows of growth object.

Usage

## S3 method for class 'growth'
tail(x, n = 6, ...)

Arguments

Author(s)

R. H. Baayen

See Also

See Also growth.fnc.

Examples

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

## End(Not run)

Create a frequency spectrum from a text vector

Description

This functions takes a text in the form of a character vector as input, and outputs a frequency spectrum object as defined in the zipfR package.

Usage

text2spc.fnc(text)

Arguments

Value

A spc spectrum object as defined in the zipfR package.

Author(s)

R. H. Baayen

See Also

See the documentation for zipfR for spc objects.

Examples

## Not run: 
  library(zipfR)
  data(alice)
  alice.spc = text2spc.fnc(alice)
  plot(alice.spc)

## End(Not run)

Through the Looking Glass

Description

The text of Lewis Carroll's 'Through the Looking Glass', with punctuation marks removed.

Usage

data(through)

Format

A character vector with 29560 words.

Source

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

Examples

## Not run: 
  data(through)
  through[1:3]

## End(Not run)

transform vector according to specified function

Description

Apply function fun to input vector y

Usage

transforming.fnc(y, fun)

Arguments

Details

exists only to make code more readable

Value

a numerical vector

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

Frequency spectrum for the Twente News Corpus

Description

Frequency (m) and frequency of frequency (Vm) for string types in the Twente News Corpus.

Usage

data(twente)

Format

A data frame with 4639 observations on the following 2 variables.

m

a numeric vector with word frequencies.

Vm

a numeric vector with the frequencies of word frequencies.

Source

Twente News Corpus.

Examples

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

## End(Not run)

Variation in spoken Dutch in the use of the suffix -lijk

Description

This dataset documents variation in the use of the suffix -lijk, as realized in 32 words, in spoken Dutch across region (Flanders versus The Netherlands), sex (females versus males) and education (high versus mid).

Usage

data(variationLijk)

Format

A data frame with 32 observations on the following 8 variables.

nlfemaleHigh

a numeric vector with counts for Dutch females with a mid education level.

nlfemaleMid

a numeric vector counts for Dutch females with a high education level.

nlmaleHigh

a numeric vector counts for Dutch males with a high education level.

nlmaleMid

a numeric vector counts for Dutch males with a mid education level.

vlfemaleHigh

a numeric vector counts for Flemish females with a high education level.

vlfemaleMid

a numeric vector counts for Flemish females with a mid education level.

vlmaleHigh

a numeric vector counts for Flemish males with a high education level.

vlmaleMid

a numeric vector counts for Flemish males with a mid education level.

References

Keune, K., Ernestus, M., Van Hout, R. and Baayen, R.H. (2005) Social, geographical, and register variation in Dutch: From written 'mogelijk' to spoken 'mok', Corpus Linguistics and Linguistic Theory, 1, 183-223.

Examples

## Not run: 
data(variationLijk)
variationLijk.ca = corres.fnc(variationLijk)
plot(variationLijk.ca, rcex=0.7, ccol="black",
  rcol = rep("blue", nrow(variationLijk))) 

## End(Not run)

The Dutch prefix ver-: semantic transparency and frequency

Description

Semantic transparency (dichotomous) and frequency for 985 words with the Dutch prefix ver-.

Usage

data(ver)

Format

A data frame with 985 observations on the following 2 variables.

Frequency

a numeric vector for the words' frequency.

SemanticClass

a factor with levels opaque and transparent coding semantic transparency.

References

Baayen, R. H. and Lieber, R. (1997) Word Frequency Distributions and Lexical Semantics, Computers and the Humanities, 30, 281-291.

Examples

## Not run: 
data(ver)
ver$Frequency = log(ver$Frequency)

plot(density(ver$Frequency))

# plot separate densities for opaque and transparent words

ver.transp = ver[ver$SemanticClass == "transparent",]$Frequency
ver.opaque = ver[ver$SemanticClass == "opaque", ]$Frequency

ver.transp.d = density(ver.transp)
ver.opaque.d = density(ver.opaque)
xlimit = range(ver.transp.d$x, ver.opaque.d$x)
ylimit = range(ver.transp.d$y, ver.opaque.d$y)
plot(ver.transp.d, lty = 1, col = "black", 
  xlab = "frequency", ylab = "density", 
  xlim = xlimit, ylim = ylimit, main = "")
lines(ver.opaque.d, col = "darkgrey")
legend(6,0.25, lty=rep(1,2), col=c("black", "darkgrey"), 
legend=c("transparent", "opaque"))

# test whether the difference is significant

ks.test(jitter(ver.transp), jitter(ver.opaque))

## End(Not run)

Dative Alternation - simplified data set

Description

A simplified version of the dative data set, used for expository purposes only.

Usage

data(verbs)

Format

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

RealizationOfRec

a factor with levels NP and PP.

Verb

a factor with the verbs as levels.

AnimacyOfRec

a factor with levels animate and inanimate.

AnimacyOfTheme

a factor with levels animate and inanimate.

LengthOfTheme

a numeric vector coding the length in words 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

data(verbs)
head(verbs)
xtabs( ~ RealizationOfRec + AnimacyOfRec, data = verbs)
barplot(xtabs( ~ RealizationOfRec + AnimacyOfRec, data = verbs),beside=TRUE)

Ergative case marking in Warlpiri

Description

This data set documents the use of ergative case marking in the narratives of native speakers of Lajamanu Warlpiri (8 children, 13 adults) describing events in picture books.

Usage

data(warlpiri)

Format

A data frame with 347 observations on the following 9 variables.

Speaker

a factor with speakers as levels.

Sentence

a factor with sentence as levels.

AgeGroup

a factor with levels adult and child.

CaseMarking

a factor with levels ergative and other.

WordOrder

a factor with levels subInitial (subject initial) and subNotInitial (subject not initial).

AnimacyOfSubject

a factor with levels animate and inanimate.

OvertnessOfObject

a factor with levels notOvert and overt.

AnimacyOfObject

a factor with levels animate and inanimate.

Text

a factor with levels texta, textb and textc.

References

O'Shannessy, C. (2006) Language contact and child bilingual acquisition: Learning a mixed language and Warlpiri in northern Australia, PhD Thesis, University of Sydney, Australia.

Examples

## Not run: 
data(warlpiri)
require(lme4)
require(lmerTest)
require(optimx)
warlpiri.lmer = glmer(CaseMarking ~ WordOrder * AgeGroup + 
  AnimacyOfSubject + (1|Text) + (1|Speaker), 
  control=glmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
  family = "binomial", data = warlpiri)
summary(warlpiri.lmer)

## End(Not run)

Subjective estimates of the weight of the referents of 81 English nouns

Description

Subjective estimates on a seven-point scale of the weight of the referents of 81 English nouns.

Usage

data(weightRatings)

Format

A data frame with 1620 observations on the following 7 variables.

Subject

a factor with subjects as levels.

Rating

a numeric vector.

Trial

a numeric vector with the weight ratings.

Sex

a factor with levels F and M.

Word

a factor with words as levels.

Frequency

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

Class

a factor with levels animal and plant.

References

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

Examples

## Not run: 
data(weightRatings)
xylowess.fnc(Rating ~ Frequency | Subject, data = weightRatings, 
  xlab = "log Frequency", ylab = "Weight Rating")

## End(Not run)

Variation in written Dutch in the use of the suffix -lijk

Description

This dataset documents variation in the use of the 80 most frequent words ending in the suffix -lijk in written Dutch.

Usage

data(writtenVariationLijk)

Format

A data frame with 560 observations on the following 5 variables.

Corpus

a factor with as levels the sampled newspapers: belang (Het Belang van Limburg), gazet (De Gazet van Antwerpen), laatnieu (Het Laatste Nieuws), limburg (De Limburger), nrc (NRC Handelsblad), stand (De Standaard), and tele (De Telegraaf).

Word

a factor with the 80 most frequent words ending in -lijk.

Count

a numeric vector with token counts in the CONDIV corpus.

Country

a factor with levels Flanders and Netherlands.

Register

a factor with levels National, Quality and Regional coding the type of newspaper.

References

Keune, K., Ernestus, M., Van Hout, R. and Baayen, R.H. (2005) Social, geographical, and register variation in Dutch: From written 'mogelijk' to spoken 'mok', Corpus Linguistics and Linguistic Theory, 1, 183-223.

Examples

## Not run: 
data(writtenVariationLijk)

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

writtenVariationLijk.lmer = glmer(Count ~ Country * Register + (1|Word), 
  control=glmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
  data = writtenVariationLijk, family = "poisson")

writtenVariationLijk.lmerA = glmer(Count ~ Country * Register + (Country|Word), 
  control=glmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
  data = writtenVariationLijk, family = "poisson")

anova(writtenVariationLijk.lmer, writtenVariationLijk.lmerA)

summary(writtenVariationLijk.lmerA)

## End(Not run)

Trellis scatterplot with smoothers

Description

Convenience function for trellis scatterplots with smoothers added.

Usage

xylowess.fnc(fmla, data, 
  span = 2/3, symbolcolor = "darkgrey", 
  linecolor = "blue", xlabel = "", ylabel = "", ...)

Arguments

Value

A trellis scatterplot matrix with smoothers is shown on the graphics device. Code builds on xyplot from the lattice package.

Author(s)

R. H. Baayen

Examples

## Not run: 
  data(weightRatings)
  xylowess.fnc(Rating ~ Frequency | Subject, data = weightRatings,
    xlab = "log Frequency", ylab = "Weight Rating")

## End(Not run)

Yule's characteristic constant K

Description

This function calculates Yule's characteristic constant K given a frequency spectrum.

Usage

yule.fnc(spect)

Arguments

Value

Yule's characteristic constant K

Author(s)

R. H. Baayen

References

Yule, G. U. (1944) The Statistical Study of Literary Vocabulary, Cambridge: Cambridge University Press.

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

See Also

See also spectrum.fnc and growth.fnc.

Examples

## Not run: 
  data(alice)
  yule.fnc(spectrum.fnc(alice))

## End(Not run)

Zipf's rank frequency distribution

Description

This function calculates Zipf's rank-frequency distribution for a text vector, and optionally produces the rank-frequency plot.

Usage

zipf.fnc(text, plot = FALSE)

Arguments

Value

A data frame with variables

Author(s)

R. H. Baayen

References

Zipf, G. K. (1935) The Psycho-Biology of Language, Boston: Houghton Mifflin.

Zipf, G. K. (1949) Human Behavior and the Principle of the Least Effort. An Introduction to Human Ecology, New York: Hafner.

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

See Also

See also growth.fnc.

Examples

## Not run: 
data(alice)
alice.zipf = zipf.fnc(alice, plot = TRUE) 
head(alice.zipf)

## End(Not run)