Bivariate Segmentation/Clustering Methods and Tools

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Documentation for package ‘segclust2d’ version 0.3.1

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segclust2d-package segclust2d: tools for segmentation of animal GPS movement data
add_covariates Covariate Calculations
add_covariates.data.frame Covariate Calculations
add_covariates.ltraj Covariate Calculations
add_covariates.Move Covariate Calculations
angular_speed Calculate angular speed along a path
apply_rowSums apply_rowSums
apply_subsampling Internal function for subsampling
argcheck_diag.var Check for argument 'diag.var'
argcheck_Kmax Check for argument 'Kmax'
argcheck_lmin Check for argument 'lmin'
argcheck_ncluster Check for argument 'ncluster'
argcheck_order.var Check for argument 'order.var'
argcheck_ordering Check for argument 'order'
argcheck_scale.variable Check for argument 'scale.variable'
argcheck_seg.var Check for argument 'seg.var'
argcheck_segclust Check for argument 'ncluster' and 'nseg'
argcheck_segmentation Check for argument 'nseg'
argcheck_type_coord Check for deprecated 'type' and 'coord.names' argument
arma_repmat arma_repmat
augment Generic function for augment
augment.segmentation segmentation class description
BIC.segmentation segmentation class description
bisig_plot bisig_plot draws the plots of the bivariate signal on the same plot (scale free)
calc_BIC Calculate BIC
calc_dist Calculate distance between locations
calc_speed Calculate speed along a path
calc_stat_states Calculate state statistics
check_repetition Check for repetition in the series
chooseseg_lavielle Internal Function for choosing optimal number of segment
choose_kmax Finding best segmentation with a different threshold S
colsums_sapply colsums_sapply
cumsum_cpp cumsum_cpp
DynProg DynProg computes the change points given a cost matrix matD and a maximum number of segments Kmax
DynProg_algo_cpp DynProg_algo_cpp
EM.algo_simultanee EM.algo_simultanee calculates the MLE of phi for given change-point instants
EM.algo_simultanee_Cpp EM.algo_simultanee calculates the MLE of phi for given change-point instants and for a fixed number of clusters
EM.init_simultanee EM.init_simultanee proposes an initial value for the EM algorithm based on a hierarchical clustering algorithm (ascending)
Estep_simultanee Estep_simultanee computes posterior probabilities and incomplete-data log-likelihood for mixture models
find_mu_sd Find mean and standard deviation of segments
get_likelihood segmentation class description
Gmean_simultanee Gmean_simultanee calculates the cost matrix for a segmentation model with changes in the mean and variance for all signals
Gmixt_algo_cpp Gmixt_algo_cpp
Gmixt_simultanee Gmixt_simultanee calculates the cost matrix for a segmentation/clustering model
Gmixt_simultanee_fullcpp Gmixt_simultanee_fullcpp
hybrid_simultanee 'hybrid_simultanee' performs a simultaneous seg - clustering for bivariate signals.
initialisePhi initialisePhi is the constructor for a set of parameters for a segclust model
likelihood Generic function for likelihood
likelihood.segmentation segmentation class description
logdens_simultanee logdens_simultanee_cpp
logdens_simultanee_cpp logdens_simultanee_cpp
logLik.segmentation segmentation class description
map_segm 'plot_segm' plot segmented movement data on a map.
matrixRupt matrixRupt transforms a vector of change point into a data.frame with start and end of every segment
Mstep_simultanee Mstep_simultanee computes the MLE within the EM framework
Mstep_simultanee_cpp Mstep_simultanee computes the MLE within the EM framework
neighborsbis neighbors tests whether neighbors of point k,P can be used to re-initialize the EM algorithm and to improve the log-likelihood.
plot.segmentation segmentation class description
plot_BIC segmentation class description
plot_likelihood segmentation class description
plot_segm Plot segmentation on time-serie
plot_states Plot states statistics
prepare_HMM Prepare HMM output for proper comparison plots
prepare_shiftfit Prepare shiftfit output for proper comparison plots
prep_segm Find segment and states for a Picard model
prep_segm_HMM Internal function for HMM
prep_segm_shiftfit Internal function for HMM
print.segmentation segmentation class description
relabel_states Relabel states of a segmentation/clustering output
repmat repmat repeats a matrix
ruptAsMat ruptAsMat is an internal function to transform a vector giving the change point to matrix 2 columns matrix in which each line gives the beginning and the end of a segment
segclust Segmentation/Clustering of movement data - Generic function
segclust.data.frame Segmentation/Clustering of movement data - Generic function
segclust.ltraj Segmentation/Clustering of movement data - Generic function
segclust.Move Segmentation/Clustering of movement data - Generic function
segclust2d segclust2d: tools for segmentation of animal GPS movement data
segclust_internal Internal segmentation/clustering function
segmap segmentation class description
segmap_list 'segmap_list' create maps with a list of object of 'segmentation' class
segment segmentation class description
segmentation Segmentation of movement data - Generic function
segmentation-class segmentation class description
segmentation.data.frame Segmentation of movement data - Generic function
segmentation.ltraj Segmentation of movement data - Generic function
segmentation.Move Segmentation of movement data - Generic function
segmentation_internal Segmentation of movement data - Generic function
simulmode Simulations of behavioural mode
simulshift Simulations of home-range shift
spatial_angle Calculate spatial angle along a path
stateplot segmentation class description
states segmentation class description
stat_segm Calculate statistics on a given segmentation
stat_segm_HMM Get segment statistic for HMM model
stat_segm_shiftfit Get segment statistic for shiftfit model
subsample_rename Internal function for subsampling
test_data Test function generating fake data
wrap_dynprog_cpp DynProg Rcpp DynProg computes the change points given a cost matrix matD and a maximum number of segments Kmax