| finalDevoicing {languageR} | R Documentation |
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.
Worda factor with the words as levels.
Onset1Typea factor for the first consonant in the onset, with levels
None,ObstruentandSonorant.Onset2Typea factor for the second consonant in the onset, with levels
None,ObstruentandSonorant.VowelTypea factor describing the vowel with levels
iuy,longandshort.ConsonantTypea factor for the first consonant in the offset, with levels
None,ObstruentandSonorant.Obstruenta 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/) andX(/x,g/).Nsylla numeric vector for the number of syllables in the word.
Stressa factor with levels
A(antepenult),F(final) andP(penult).Voicea factor with levels
voicedandvoiceless.
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)