sigClassif: Signals clustering

In RclusTool: Graphical Toolbox for Clustering and Classification of Data Frames

Signals clustering

Description

Sort signals (if available) in different directories according to a clustering result.

Usage

sigClassif(data.sample, method, user.name = "")

Arguments

data.sample	list containing features, profiles and clustering results.
method	character vector specifying the clustering method (already performed) to use.
user.name	character vector specifying the user name.

Details

sigClassif sorts signals (if available) in different directories according to a clustering result

Value

signals plots images in the different directories.

See Also

imgClassif

Examples

dat <- rbind(matrix(rnorm(100, mean = 0, sd = 0.3), ncol = 2), 
             matrix(rnorm(100, mean = 2, sd = 0.3), ncol = 2), 
             matrix(rnorm(100, mean = 4, sd = 0.3), ncol = 2))
tf1 <- tempfile()
write.table(dat, tf1, sep=",", dec=".")

sig <- data.frame(ID=rep(1:150, each=30), SIGNAL=rep(dnorm(seq(-2,2,length=30)),150))
tf2 <- tempfile()
write.table(sig, tf2, sep=",", dec=".")

dir.results <- tempdir()
x <- importSample(file.features=tf1,file.profiles = tf2, dir.save=dir.results)
x <- computeUnSupervised(x, K=3, method.name="K-means")

sigClassif(x, method = "K-means_preprocessed")
 

RclusTool documentation built on May 29, 2024, 5:23 a.m.