segclust {segclust2d}

Segmentation/Clustering of movement data - Generic function

Description

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.

Usage

segclust(x, ...)

## S3 method for class 'data.frame'
segclust(x, ...)

## S3 method for class 'Move'
segclust(x, ...)

## S3 method for class 'ltraj'
segclust(x, ...)

Arguments

x

data.frame with observations

...

additional parameters given to segclust_internal.

Value

a segmentation-class object

Examples

#' @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)