##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:ELViS.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings ELViS_1.2.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.22-bioc/meat/ELViS.Rcheck’
* using R version 4.5.2 Patched (2025-11-04 r88984)
* using platform: aarch64-apple-darwin20
* R was compiled by
    Apple clang version 16.0.0 (clang-1600.0.26.6)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Ventura 13.7.8
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘ELViS/DESCRIPTION’ ... OK
* this is package ‘ELViS’ version ‘1.2.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... INFO
Imports includes 21 non-default packages.
Importing from so many packages makes the package vulnerable to any of
them becoming unavailable.  Move as many as possible to Suggests and
use conditionally.
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘ELViS’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                      user system elapsed
run_ELViS           22.148  0.255  24.737
integrative_heatmap 16.707  0.448  18.549
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘testthat.R’
 ERROR
Running the tests in ‘tests/testthat.R’ failed.
Last 13 lines of output:
  [ FAIL 1 | WARN 2 | SKIP 0 | PASS 83 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-Process_Bam_Test.R:210:9'): (code run outside of `test_that()`) ──
  Error in `eval(code, test_env)`: object 'samtools_to_install' not found
  Backtrace:
      ▆
   1. └─reticulate::conda_create(...) at test-Process_Bam_Test.R:210:9
   2.   └─base::grepl("^python", packages)
   3.     └─base::is.factor(x)
  
  [ FAIL 1 | WARN 2 | SKIP 0 | PASS 83 ]
  Error:
  ! Test failures.
  Execution halted
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 1 ERROR
See
  ‘/Users/biocbuild/bbs-3.22-bioc/meat/ELViS.Rcheck/00check.log’
for details.

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL ELViS
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library’
* installing *source* package ‘ELViS’ ...
** this is package ‘ELViS’ version ‘1.2.0’
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (ELViS)

R version 4.5.2 Patched (2025-11-04 r88984) -- "[Not] Part in a Rumble"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
> 
> library(testthat)
> library(ELViS)
> 
> test_check("ELViS")
ELViS run starts.
1

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
1| done
2

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
2| done
3

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
3| done
4

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
4| done
5

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
5| done
6

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
6| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
! Argument ncluster was not provided. Selecting values with BIC
i BIC-selected number of class : ncluster = 2
BIC-selected number of segment : nseg = 2
1| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
! Argument ncluster was not provided. Selecting values with BIC
i BIC-selected number of class : ncluster = 2
BIC-selected number of segment : nseg = 2
2| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
! Argument ncluster was not provided. Selecting values with BIC
i BIC-selected number of class : ncluster = 2
BIC-selected number of segment : nseg = 2
3| done
4| done
5| done
6| done
1

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
1| done
2

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
2| done
3

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
3| done
4

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
4| done
5

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
5| done
6

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
6| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
! Argument ncluster was not provided. Selecting values with BIC
i BIC-selected number of class : ncluster = 2
BIC-selected number of segment : nseg = 2
1| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
! Argument ncluster was not provided. Selecting values with BIC
i BIC-selected number of class : ncluster = 2
BIC-selected number of segment : nseg = 2
2| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
! Argument ncluster was not provided. Selecting values with BIC
i BIC-selected number of class : ncluster = 2
BIC-selected number of segment : nseg = 2
3| done
4| done
5| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
! Argument ncluster was not provided. Selecting values with BIC
i BIC-selected number of class : ncluster = 2
BIC-selected number of segment : nseg = 2
6| done
Normalization done.

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
1| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
2| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
3| done
4| done
5| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
6| done
1
1
2
2
3
3
4
4
5
5
6
6
Segmentation done.
ELViS run starts.
1

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
1| done
2

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
2| done
3

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
3| done
4

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
4| done
5

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
5| done
6

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
6| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
! Argument ncluster was not provided. Selecting values with BIC
i BIC-selected number of class : ncluster = 2
BIC-selected number of segment : nseg = 2
1| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
! Argument ncluster was not provided. Selecting values with BIC
i BIC-selected number of class : ncluster = 2
BIC-selected number of segment : nseg = 2
2| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
! Argument ncluster was not provided. Selecting values with BIC
i BIC-selected number of class : ncluster = 2
BIC-selected number of segment : nseg = 2
3| done
4| done
5| done
6| done
1

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
1| done
2

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
2| done
3

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
3| done
4

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
4| done
5

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
5| done
6

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
6| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
! Argument ncluster was not provided. Selecting values with BIC
i BIC-selected number of class : ncluster = 2
BIC-selected number of segment : nseg = 2
1| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
! Argument ncluster was not provided. Selecting values with BIC
i BIC-selected number of class : ncluster = 2
BIC-selected number of segment : nseg = 2
2| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
! Argument ncluster was not provided. Selecting values with BIC
i BIC-selected number of class : ncluster = 2
BIC-selected number of segment : nseg = 2
3| done
4| done
5| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
! Argument ncluster was not provided. Selecting values with BIC
i BIC-selected number of class : ncluster = 2
BIC-selected number of segment : nseg = 2
6| done
Normalization done.

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
1| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
2| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
3| done
4| done
5| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
6| done
1
1
2
2
3
3
4
4
5
5
6
6
Segmentation done.
1

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
1| done
2

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
2| done
3

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
3| done
4

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
4| done
5

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
5| done
6

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
6| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
! Argument ncluster was not provided. Selecting values with BIC
i BIC-selected number of class : ncluster = 2
BIC-selected number of segment : nseg = 2
1| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
! Argument ncluster was not provided. Selecting values with BIC
i BIC-selected number of class : ncluster = 2
BIC-selected number of segment : nseg = 2
2| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
! Argument ncluster was not provided. Selecting values with BIC
i BIC-selected number of class : ncluster = 2
BIC-selected number of segment : nseg = 2
3| done
4| done
5| done
6| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
1| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
2| done

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using ncluster = 2L
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
! Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running Segmentation/Clustering algorithm -----------------------------------
i Running Segmentation/Clustering with lmin = 5, Kmax = 3 and ncluster = 2L
> Calculating initial segmentation without clustering
v Initial segmentation with no cluster calculated.
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
-- Segmentation/Clustering with ncluster = 2 
> Calculating initial segmentation without clustering
> Calculating initial segmentation without clustering
> Segmentation-Clustering for ncluster = 2 and nseg = 2/3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
v Segmentation-Clustering successful for ncluster = 2 and nseg = 2:3
> Segmentation-Clustering for ncluster = 2 and nseg = 3/3
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
v Smoothing successful for ncluster = 2
> Smoothing likelihood for ncluster = 2. This step can be lengthy.
> Calculating initial segmentation without clustering
v Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
> Calculating initial segmentation without clustering
-- Segmentation/Clustering results ---------------------------------------------
v Best segmentation/clustering estimated with 2 clusters and 2 segments according to BIC
> Number of cluster should preferentially be selected according to biological
knowledge. Exploring the BIC plot with plot_BIC() can also provide advice to
select the number of clusters.
> Once number of clusters is selected, the number of segment cab be selected
according to BIC.
> Results of the segmentation/clustering may further be explored with plot()
and segmap()
3| done
4| done
5| done
6| done
1
1
2
2
3
3
4
4
5
5
6
6

-- Checking arguments ----------------------------------------------------------
v Segmentation with seg.var = c("z", "y")
v Using lmin = 300
v Using Kmax = 10
v Using scale.variable = FALSE
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("z", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "z"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
v Using subsample_by = 60
v subsampling by 60
v Adjusting lmin to subsampling. 
Dividing lmin by 60, with a minimum of 5
> After subsampling, lmin = 5. 
Corresponding to lmin = 300 on the original time scale
v Adjusting Kmax so that lmin*Kmax < nrow(x). Kmax = 3

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 3
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 3
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 2 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
n_cycle : 1
N_alt_ori
n_cycle : 1
N_alt_ori
The path to samtools not provided.
Default samtools is used : /Users/biocbuild/Library/r-miniconda-arm64/envs/env_samtools_auto/bin/samtools
The path to samtools not provided.
Default samtools is used : /Users/biocbuild/Library/r-miniconda-arm64/envs/env_samtools_auto/bin/samtools
+ /Users/biocbuild/Library/r-miniconda-arm64/bin/conda create --yes --name env_samtools_1.21 'python=3.12' 'samtools=1.21' --quiet -c conda-forge -c bioconda
Collecting package metadata (current_repodata.json): ...working... done
Solving environment: ...working... unsuccessful attempt using repodata from current_repodata.json, retrying with next repodata source.
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done

## Package Plan ##

  environment location: /Users/biocbuild/Library/r-miniconda-arm64/envs/env_samtools_1.21

  added / updated specs:
    - python=3.12
    - samtools=1.21


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    htslib-1.21                |       hfcd771d_1         2.4 MB  bioconda
    samtools-1.21              |       h267f7b9_1         450 KB  bioconda
    ------------------------------------------------------------
                                           Total:         2.9 MB

The following NEW packages will be INSTALLED:

  bzip2              conda-forge/osx-arm64::bzip2-1.0.8-hd037594_8 
  c-ares             conda-forge/osx-arm64::c-ares-1.34.5-h5505292_0 
  ca-certificates    conda-forge/noarch::ca-certificates-2025.11.12-hbd8a1cb_0 
  htslib             bioconda/osx-arm64::htslib-1.21-hfcd771d_1 
  krb5               conda-forge/osx-arm64::krb5-1.21.3-h237132a_0 
  libcurl            conda-forge/osx-arm64::libcurl-8.17.0-hdece5d2_0 
  libcxx             conda-forge/osx-arm64::libcxx-21.1.5-hf598326_0 
  libdeflate         conda-forge/osx-arm64::libdeflate-1.22-hd74edd7_0 
  libedit            conda-forge/osx-arm64::libedit-3.1.20250104-pl5321hafb1f1b_0 
  libev              conda-forge/osx-arm64::libev-4.33-h93a5062_2 
  libexpat           conda-forge/osx-arm64::libexpat-2.7.1-hec049ff_0 
  libffi             conda-forge/osx-arm64::libffi-3.5.2-he5f378a_0 
  liblzma            conda-forge/osx-arm64::liblzma-5.8.1-h39f12f2_2 
  libnghttp2         conda-forge/osx-arm64::libnghttp2-1.67.0-hc438710_0 
  libsqlite          conda-forge/osx-arm64::libsqlite-3.51.0-h9a5124b_0 
  libssh2            conda-forge/osx-arm64::libssh2-1.11.1-h1590b86_0 
  libzlib            conda-forge/osx-arm64::libzlib-1.3.1-h8359307_2 
  ncurses            conda-forge/osx-arm64::ncurses-6.5-h5e97a16_3 
  openssl            conda-forge/osx-arm64::openssl-3.6.0-h5503f6c_0 
  pip                conda-forge/noarch::pip-25.3-pyh8b19718_0 
  python             conda-forge/osx-arm64::python-3.12.12-h18782d2_1_cpython 
  readline           conda-forge/osx-arm64::readline-8.2-h1d1bf99_2 
  samtools           bioconda/osx-arm64::samtools-1.21-h267f7b9_1 
  setuptools         conda-forge/noarch::setuptools-80.9.0-pyhff2d567_0 
  tk                 conda-forge/osx-arm64::tk-8.6.13-h892fb3f_3 
  tzdata             conda-forge/noarch::tzdata-2025b-h78e105d_0 
  wheel              conda-forge/noarch::wheel-0.45.1-pyhd8ed1ab_1 
  zstd               conda-forge/osx-arm64::zstd-1.5.7-h6491c7d_2 


Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working... done
The path to samtools not provided.
Default samtools is used : /Users/biocbuild/Library/r-miniconda-arm64/envs/env_samtools_1.21/bin/samtools
The path to samtools not provided.
Default samtools is used : /Users/biocbuild/Library/r-miniconda-arm64/envs/env_samtools_1.21/bin/samtools
The path to samtools not provided.
Default samtools is used : /Users/biocbuild/Library/r-miniconda-arm64/envs/env_samtools_1.21/bin/samtools
The path to samtools not provided.
Default samtools is used : /Users/biocbuild/Library/r-miniconda-arm64/envs/env_samtools_1.21/bin/samtools
Saving _problems/test-Process_Bam_Test-214.R

-- Checking arguments ----------------------------------------------------------
! Argument seg.var missing
taking default value seg.var = c("x","y")
v Segmentation with seg.var = c("x", "y")
v Using lmin = 5
v Using Kmax = 2
! Argument scale.variable missing
Taking default value scale.variable = FALSE for segmentation().
i Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("x", "y")
i Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "x"

-- Preparing and checking data -------------------------------------------------

-- Subsampling --

! Subsampling automatically activated. To disable it, provide subsample = FALSE
i Argument subsample_over was not provided
Taking default value for segmentation()
Setting subsample_over = 10000
v nrow(x) < subsample_over, no subsample needed

-- Scaling and final data check --

v No variable rescaling.
To activate, use scale.variable = TRUE
v Data have no repetition of nearly-identical values larger than lmin

-- Running segmentation algorithm ----------------------------------------------
i Running segmentation with lmin = 5 and Kmax = 2
> Calculating cost matrix
v Cost matrix calculated
> Calculating cost matrix
> Dynamic Programming
v Optimal segmentation calculated for all number of segments <= 2
> Dynamic Programming
> Calculating segment statistics
v Best segmentation estimated with 1 segments, according to Lavielle's criterium
Other number of segments may be selected by looking for likelihood breaks with
plot_likelihood()
Results of the segmentation may be explored with plot() and segmap()
[ FAIL 1 | WARN 2 | SKIP 0 | PASS 83 ]

══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-Process_Bam_Test.R:210:9'): (code run outside of `test_that()`) ──
Error in `eval(code, test_env)`: object 'samtools_to_install' not found
Backtrace:
    ▆
 1. └─reticulate::conda_create(...) at test-Process_Bam_Test.R:210:9
 2.   └─base::grepl("^python", packages)
 3.     └─base::is.factor(x)

[ FAIL 1 | WARN 2 | SKIP 0 | PASS 83 ]
Error:
! Test failures.
Execution halted