| durationsOnt | R Documentation |
Durational measurements on the Dutch prefix ont- in the Spoken Dutch Corpus.
data(durationsOnt)
A data frame with 102 observations on the following 11 variables.
Worda factor with the words as levels.
Frequencya numeric vector with the word's logarithmically transformed frequency in the Spoken Dutch Corpus.
Speakera factor with speakers as levels.
Sexa factor with levels female and male.
YearOfBirtha numeric vector coding year of birth of the speaker - 1900.
DurationOfPrefixa numeric vector for the duration of ont- in seconds
DurationPrefixVowela numeric vector for the duration of the vowel in the prefix in seconds.
DurationPrefixNasala numeric vector for the duration of the nasal in the prefix in seconds.
DurationPrefixPlosivea numeric vector for the duration of the plosive in the prefix in seconds.
NumberOfSegmentsOnseta numeric vector for the number of segments in the onset of the stem.
PlosivePresenta factor with levels no and yes
for whether the plosive is realized in the signal.
SpeechRatea numeric vector coding speech rate in number of syllables per second.
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.
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)
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