Description Usage Format References Examples
Relative frequencies of the 120 most frequent tag trigrams in 15 texts contributed by 3 authors.
1 |
A data frame with 120 observations on 15 variables documented in
spanishMeta
.
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | ## 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)
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