##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD check --install=check:ELViS.install-out.txt --library=/home/biocbuild/bbs-3.22-bioc/R/site-library --timings ELViS_1.1.10.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.22-bioc/meat/ELViS.Rcheck’
* using R version 4.5.1 (2025-06-13)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
    GNU Fortran (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
* running under: Ubuntu 24.04.3 LTS
* using session charset: UTF-8
* checking for file ‘ELViS/DESCRIPTION’ ... OK
* this is package ‘ELViS’ version ‘1.1.10’
* 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 loading without being on the library search path ... 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           81.786  0.719  88.983
integrative_heatmap 40.118  0.574  27.967
gene_cn_heatmaps    12.215  0.379  12.595
get_depth_matrix     1.591  0.244  44.739
* 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:
  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:116:1'): (code run outside of `test_that()`) ──
  Error: Error creating conda environment 'env_samtools_1.21' [exit code 1]
  Backtrace:
      ▆
   1. └─ELViS:::get_envs_samtools_reticulate(...) at test-Process_Bam_Test.R:116:1
   2.   └─reticulate::conda_create(...)
   3.     └─reticulate:::stopf(fmt, envname, result, call. = FALSE)
  
  [ 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 re-building of vignette outputs ... OK
* checking PDF version of manual ... OK
* DONE

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

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD INSTALL ELViS
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.22-bioc/R/site-library’
* installing *source* package ‘ELViS’ ...
** this is package ‘ELViS’ version ‘1.1.10’
** 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 4.5.1 (2025-06-13) -- "Great Square Root"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 3
→ Dynamic Programming
→ Calculating segment statistics
✔ 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 ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("z", "y")
✔ Using lmin = 300
✔ Using Kmax = 10
✔ Using ncluster = 2L
✔ 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
✔ Using subsample_by = 60
✔ subsampling by 60
✔ 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 ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ 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
✔ 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.
✔ Smoothing successful for ncluster = 2
→ Smoothing likelihood for ncluster = 2. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 2 successfully calculated.
BIC selected : nseg = 2
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ 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
+ /home/biocbuild/.local/share/r-miniconda/bin/conda create --yes --name env_samtools_auto 'python=3.10' samtools --quiet -c conda-forge -c bioconda
Channels:
 - conda-forge
 - bioconda
 - defaults
Platform: linux-64
Collecting package metadata (repodata.json): ...working... Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/main/linux-64/repodata.json.zst

Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /bioconda/noarch/repodata.json.zst

Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /conda-forge/noarch/repodata.json.zst

Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/r/linux-64/repodata.json.zst

Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/main/noarch/repodata.json.zst

Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /bioconda/linux-64/repodata.json.zst

Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/r/noarch/repodata.json.zst

Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /conda-forge/linux-64/repodata.json.zst

Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/main/linux-64/repodata.json.zst

Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /bioconda/noarch/repodata.json.zst

Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /conda-forge/noarch/repodata.json.zst

Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /bioconda/linux-64/repodata.json.zst

Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /conda-forge/linux-64/repodata.json.zst

Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/main/noarch/repodata.json.zst

Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/r/noarch/repodata.json.zst

Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/r/linux-64/repodata.json.zst

Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /bioconda/noarch/repodata.json.zst

Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/r/linux-64/repodata.json.zst

Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /conda-forge/noarch/repodata.json.zst

Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /bioconda/linux-64/repodata.json.zst

Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/main/noarch/repodata.json.zst

Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/main/linux-64/repodata.json.zst

Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /conda-forge/linux-64/repodata.json.zst

Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/r/noarch/repodata.json.zst

failed

CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://conda.anaconda.org/conda-forge/linux-64/repodata.json>
Elapsed: -

An HTTP error occurred when trying to retrieve this URL.
HTTP errors are often intermittent, and a simple retry will get you on your way.
'https//conda.anaconda.org/conda-forge/linux-64'


Failed to create conda environment: Error creating conda environment 'env_samtools_auto' [exit code 1]
Changing mode from samtools_reticulate to Rsamtools
+ /home/biocbuild/.local/share/r-miniconda/bin/conda create --yes --name env_samtools_1.21 'python=3.10' 'samtools=1.21' --quiet -c conda-forge -c bioconda
Channels:
 - conda-forge
 - bioconda
 - defaults
Platform: linux-64
Collecting package metadata (repodata.json): ...working... Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /conda-forge/noarch/repodata.json.zst

Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /bioconda/noarch/repodata.json.zst

Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /bioconda/linux-64/repodata.json.zst

Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /conda-forge/linux-64/repodata.json.zst

Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/r/noarch/repodata.json.zst

Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/r/linux-64/repodata.json.zst

Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/main/noarch/repodata.json.zst

Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/main/linux-64/repodata.json.zst

Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /conda-forge/noarch/repodata.json.zst

Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /bioconda/noarch/repodata.json.zst

Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /conda-forge/linux-64/repodata.json.zst

Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /bioconda/linux-64/repodata.json.zst

Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/r/noarch/repodata.json.zst

Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/main/linux-64/repodata.json.zst

Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/main/noarch/repodata.json.zst

Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/r/linux-64/repodata.json.zst

Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /conda-forge/linux-64/repodata.json.zst

Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /bioconda/linux-64/repodata.json.zst

Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /bioconda/noarch/repodata.json.zst

Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='conda.anaconda.org', port=443): Read timed out. (read timeout=9.15)")': /conda-forge/noarch/repodata.json.zst

Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/r/noarch/repodata.json.zst

Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/main/linux-64/repodata.json.zst

Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/main/noarch/repodata.json.zst

Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/r/linux-64/repodata.json.zst

failed

CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://conda.anaconda.org/conda-forge/linux-64/repodata.json>
Elapsed: -

An HTTP error occurred when trying to retrieve this URL.
HTTP errors are often intermittent, and a simple retry will get you on your way.
'https//conda.anaconda.org/conda-forge/linux-64'



── Checking arguments ──────────────────────────────────────────────────────────
! Argument seg.var missing
taking default value seg.var = c("x","y")
✔ Segmentation with seg.var = c("x", "y")
✔ Using lmin = 5
✔ 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
✔ nrow(x) < subsample_over, no subsample needed

── Scaling and final data check ──

✔ No variable rescaling.
To activate, use scale.variable = TRUE
✔ 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
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 2
→ Dynamic Programming
→ Calculating segment statistics
✔ 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:116:1'): (code run outside of `test_that()`) ──
Error: Error creating conda environment 'env_samtools_1.21' [exit code 1]
Backtrace:
    ▆
 1. └─ELViS:::get_envs_samtools_reticulate(...) at test-Process_Bam_Test.R:116:1
 2.   └─reticulate::conda_create(...)
 3.     └─reticulate:::stopf(fmt, envname, result, call. = FALSE)

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