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--- re-building 'v01_preparing_data.Rmd' using rmarkdown
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--- re-building 'v02_run_segclust2d.Rmd' using rmarkdown
Warning: The closing backticks on line 271 ("```") in v02_run_segclust2d.Rmd do not match the opening backticks " ```" on line 261. You are recommended to fix either the opening or closing delimiter of the code chunk to use exactly the same numbers of backticks and same level of indentation (or blockquote).

── Checking arguments ──────────────────────────────────────────────────────────
! Argument seg.var missing
taking default value seg.var = c("x","y")
✔ Segmentation with seg.var = c("x", "y")
✔ Using lmin = 240
✔ Using Kmax = 25
! 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
✔ 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

── 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 = 25
→ Calculating cost matrix
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 25
→ Dynamic Programming
→ Calculating segment statistics
✔ Best segmentation estimated with 3 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("speed", "abs_spatial_angle")
✔ Using lmin = 10
✔ Using Kmax = 20
✔ Using scale.variable = TRUE
ℹ Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("speed", "abs_spatial_angle")
ℹ Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "speed"

── 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 ──

✔ Rescaling variables.
To deactivate, use scale.variable = FALSE
✔ Data have no repetition of nearly-identical values larger than lmin

── Running segmentation algorithm ──────────────────────────────────────────────
ℹ Running segmentation with lmin = 10 and Kmax = 20
→ Calculating cost matrix
✔ Cost matrix calculated
→ Calculating cost matrix
→ Dynamic Programming
✔ Optimal segmentation calculated for all number of segments <= 20
→ 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()

── Checking arguments ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("speed", "abs_spatial_angle")
✔ Using lmin = 10
✔ Using Kmax = 20
✔ Using ncluster = c(2, 3)
! Argument scale.variable missing
Taking default value scale.variable = TRUE for segclust().
ℹ Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("speed", "abs_spatial_angle")
ℹ Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "speed"

── 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 ──

✔ Rescaling variables.
To deactivate, use scale.variable = FALSE
✔ Data have no repetition of nearly-identical values larger than lmin

── Running Segmentation/Clustering algorithm ───────────────────────────────────
ℹ Running Segmentation/Clustering with lmin = 10, Kmax = 20 and ncluster = c(2, 3)
→ 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 = 2/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 3/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 4/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 5/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 6/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 7/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 8/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 9/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 10/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 11/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 12/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 13/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 14/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 15/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 16/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 17/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 18/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 19/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 20/20
✔ Segmentation-Clustering successful for ncluster = 2 and nseg = 2:20
→ Segmentation-Clustering for ncluster = 2 and nseg = 20/20
→ 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 = 10
→ Calculating initial segmentation without clustering
→ Calculating initial segmentation without clustering
── Segmentation/Clustering with ncluster = 3 
→ Calculating initial segmentation without clustering
→ Calculating initial segmentation without clustering
→ Segmentation-Clustering for ncluster = 3 and nseg = 3/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 4/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 5/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 6/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 7/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 8/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 9/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 10/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 11/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 12/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 13/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 14/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 15/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 16/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 17/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 18/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 19/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 20/20
✔ Segmentation-Clustering successful for ncluster = 3 and nseg = 3:20
→ Segmentation-Clustering for ncluster = 3 and nseg = 20/20
→ Smoothing likelihood for ncluster = 3. This step can be lengthy.
✔ Smoothing successful for ncluster = 3
→ Smoothing likelihood for ncluster = 3. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 3 successfully calculated.
BIC selected : nseg = 15
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ Best segmentation/clustering estimated with 3 clusters and 15 segments according to BIC
Quitting from lines 348-352 (v02_run_segclust2d.Rmd) 
Error: processing vignette 'v02_run_segclust2d.Rmd' failed with diagnostics:
number of characters is not computable in "bytes" encoding, element 1
--- failed re-building 'v02_run_segclust2d.Rmd'

--- re-building 'v03_explore_outputs.Rmd' using rmarkdown

── Checking arguments ──────────────────────────────────────────────────────────
✔ Segmentation with seg.var = c("speed", "abs_spatial_angle")
✔ Using lmin = 10
✔ Using Kmax = 20
✔ Using ncluster = c(2, 3)
✔ Using scale.variable = TRUE
ℹ Argument diag.var was not provided
Taking default seg.var as diagnostic variables diag.var.
Setting diag.var = c("speed", "abs_spatial_angle")
ℹ Argument order.var was not provided
Taking default diag.var[1] as ordering variable order.var.
Setting order.var = "speed"

── 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 ──

✔ Rescaling variables.
To deactivate, use scale.variable = FALSE
✔ Data have no repetition of nearly-identical values larger than lmin

── Running Segmentation/Clustering algorithm ───────────────────────────────────
ℹ Running Segmentation/Clustering with lmin = 10, Kmax = 20 and ncluster = c(2, 3)
→ 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 = 2/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 3/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 4/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 5/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 6/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 7/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 8/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 9/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 10/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 11/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 12/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 13/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 14/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 15/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 16/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 17/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 18/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 19/20
→ Segmentation-Clustering for ncluster = 2 and nseg = 20/20
✔ Segmentation-Clustering successful for ncluster = 2 and nseg = 2:20
→ Segmentation-Clustering for ncluster = 2 and nseg = 20/20
→ 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 = 10
→ Calculating initial segmentation without clustering
→ Calculating initial segmentation without clustering
── Segmentation/Clustering with ncluster = 3 
→ Calculating initial segmentation without clustering
→ Calculating initial segmentation without clustering
→ Segmentation-Clustering for ncluster = 3 and nseg = 3/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 4/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 5/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 6/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 7/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 8/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 9/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 10/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 11/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 12/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 13/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 14/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 15/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 16/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 17/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 18/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 19/20
→ Segmentation-Clustering for ncluster = 3 and nseg = 20/20
✔ Segmentation-Clustering successful for ncluster = 3 and nseg = 3:20
→ Segmentation-Clustering for ncluster = 3 and nseg = 20/20
→ Smoothing likelihood for ncluster = 3. This step can be lengthy.
✔ Smoothing successful for ncluster = 3
→ Smoothing likelihood for ncluster = 3. This step can be lengthy.
→ Calculating initial segmentation without clustering
✔ Segmentation/Clustering with ncluster = 3 successfully calculated.
BIC selected : nseg = 15
→ Calculating initial segmentation without clustering
── Segmentation/Clustering results ─────────────────────────────────────────────
✔ Best segmentation/clustering estimated with 3 clusters and 15 segments according to BIC
Quitting from lines 109-116 (v03_explore_outputs.Rmd) 
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--- failed re-building 'v03_explore_outputs.Rmd'

SUMMARY: processing the following files failed:
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Error: Vignette re-building failed.
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