segclust | R Documentation |
Joint Segmentation/Clustering of movement data. Method available for data.frame, move and ltraj objects. The algorithm finds the optimal segmentation for a given number of cluster and segments using an iterated alternation of a Dynamic Programming algorithm and an Expectation-Maximization algorithm. Among the different segmentation found, the best one can be chosen using the maximum of a BIC penalized likelihood.
segclust(x, ...) ## S3 method for class 'data.frame' segclust(x, ...) ## S3 method for class 'Move' segclust(x, ...) ## S3 method for class 'ltraj' segclust(x, ...)
x |
data.frame with observations |
... |
additional parameters given to |
a segmentation-class
object
#' @examples df <- test_data()$data #' # data is a data.frame with column 'x' and 'y' # Simple segmentation with automatic subsampling # if data has more than 1000 rows: res <- segclust(df, Kmax = 15, lmin = 10, ncluster = 2:4, seg.var = c("x","y")) # Plot results plot(res) segmap(res, coord.names = c("x","y")) # check penalized likelihood of # alternative number of segment possible. # There should be a clear break if the segmentation is good plot_BIC(res) ## Not run: # Advanced options: # Run with automatic subsampling if df has more than 500 rows: res <- segclust(df, Kmax = 30, lmin = 10, ncluster = 2:4, seg.var = c("x","y"), subsample_over = 500) # Run with subsampling by 2: res <- segclust(df, Kmax = 30, lmin = 10, ncluster = 2:4, seg.var = c("x","y"), subsample_by = 2) # Disable subsampling: res <- segclust(df, Kmax = 30, lmin = 10, ncluster = 2:4, seg.var = c("x","y"), subsample = FALSE) # Disabling automatic scaling of variables for segmentation (standardazing # the variables) : res <- segclust(df, Kmax = 30, lmin = 10, seg.var = c("dist","angle"), scale.variable = FALSE) ## End(Not run)
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