## S3 method for class 'matrix':
ranking(x, y, kernel ="rbfdot", kpar = list(sigma = 1),
scale = FALSE, alpha = 0.99, iterations = 600,
edgegraph = FALSE, convergence = FALSE ,...)## S3 method for class 'kernelMatrix':
ranking(x, y, alpha = 0.99, iterations = 600,
convergence = FALSE,...)
## S3 method for class 'list':
ranking(x, y, kernel = "stringdot", kpar =
list(length = 4, lambda = 0.5), alpha = 0.99, iterations = 600, convergence = FALSE, ...)
sigma
inverse kernel width for the Radial Balpha
parameter takes values between 0 and 1
and is used to control the authoritative scores received from the
unlabeled points. For 0 no global structure is found the algorithm
ranks the points similarly to the original distaranking
which extends the matrix
class.
The first column of the returned matrix contains the original index of
the points in the data matrix the second column contains the final
score received by each point and the third column the ranking of the point.
The object contains the following slots :ranking-class
, specc
data(spirals)
## create data from spirals
ran <- spirals[rowSums(abs(spirals) < 0.55) == 2,]
## rank points according to similarity to the most upper left point
ranked <- ranking(ran, 54, kernel = "rbfdot", kpar = list(sigma = 100), edgegraph = TRUE)
ranked[54, 2] <- max(ranked[-54, 2])
c<-1:86
op <- par(mfrow = c(1, 2),pty="s")
plot(ran)
plot(ran, cex=c[ranked[,3]]/40)
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