##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.23-bioc/R/bin/R CMD check --install=check:ELViS.install-out.txt --library=/home/biocbuild/bbs-3.23-bioc/R/site-library --timings ELViS_1.3.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.23-bioc/meat/ELViS.Rcheck’
* using R Under development (unstable) (2025-10-20 r88955)
* 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 ‘1.3.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... INFO
Imports includes 21 non-default packages.
Importing from so many packages makes the package vulnerable to any of
them becoming unavailable.  Move as many as possible to Suggests and
use conditionally.
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘ELViS’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking 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           74.097  0.433  77.932
integrative_heatmap 24.667  0.332  24.882
gene_cn_heatmaps    12.114  0.303  12.418
get_depth_matrix     6.374  0.406  72.942
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘testthat.R’
 OK
* 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: OK

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.23-bioc/R/bin/R CMD INSTALL ELViS
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.23-bioc/R/site-library’
* installing *source* package ‘ELViS’ ...
** this is package ‘ELViS’ version ‘1.3.0’
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (ELViS)

R Under development (unstable) (2025-10-20 r88955) -- "Unsuffered Consequences"
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='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/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/r/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/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='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/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/noarch/repodata.json.zst

failed

CondaHTTPError: HTTP 502 BAD GATEWAY for url <https://anaconda.mgb.org/conda-forge/linux-64/repodata.json>
Elapsed: 00:00.355567

A remote server error occurred when trying to retrieve this URL.

A 500-type error (e.g. 500, 501, 502, 503, etc.) indicates the server failed to
fulfill a valid request.  The problem may be spurious, and will resolve itself if you
try your request again.  If the problem persists, consider notifying the maintainer
of the remote server.


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='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/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/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/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/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/r/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=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/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='repo.anaconda.com', port=443): Read timed out. (read timeout=9.15)")': /pkgs/main/linux-64/repodata.json.zst

failed

CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://repo.anaconda.com/pkgs/main/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.

If your current network has https://repo.anaconda.com blocked, please file
a support request with your network engineering team.

'https//repo.anaconda.com/pkgs/main/linux-64'


Failed to get conda envs: Error creating conda environment 'env_samtools_1.21' [exit code 1]

── 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 0 | WARN 2 | SKIP 0 | PASS 96 ]

[ FAIL 0 | WARN 2 | SKIP 0 | PASS 96 ]
> 
> proc.time()
   user  system elapsed 
 58.293   4.232 142.346