R Under development (unstable) (2025-10-06 r88901 ucrt) -- "Unsuffered Consequences" Copyright (C) 2025 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 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. > library(testthat) > library(ggseqplot) Loading required package: TraMineR TraMineR stable version 2.2-12 (Built: 2025-10-07) Website: http://traminer.unige.ch Please type 'citation("TraMineR")' for citation information. Loading required package: ggplot2 > > test_check("ggseqplot") [>] 4 distinct states appear in the data: 1 = A 2 = B 3 = C 4 = D [>] state coding: [alphabet] [label] [long label] 1 A A > 37 hours 2 B B 19-36 hours 3 C C 1-18 hours 4 D D no work [>] 300 sequences in the data set [>] min/max sequence length: 12/12 [>] found missing values ('NA') in sequence data [>] preparing 7 sequences [>] coding void elements with '%' and missing values with '*' [!!] 1 empty sequence(s) with index: 7 may produce inconsistent results. [>] 4 distinct states appear in the data: 1 = A 2 = B 3 = C 4 = D [>] state coding: [alphabet] [label] [long label] 1 A A A 2 B B B 3 C C C 4 D D D [>] sum of weights: 60 - min/max: 0/29.3 [>] 7 sequences in the data set [>] min/max sequence length: 0/13 [>] 8 distinct states appear in the data: 1 = 0 2 = 1 3 = 2 4 = 3 5 = 4 6 = 5 7 = 6 8 = 7 [>] state coding: [alphabet] [label] [long label] 1 0 0 Parent 2 1 1 Left 3 2 2 Married 4 3 3 Left+Marr 5 4 4 Child 6 5 5 Left+Child 7 6 6 Left+Marr+Child 8 7 7 Divorced [>] sum of weights: 330.07 - min/max: 0/6.02881860733032 [>] 300 sequences in the data set [>] min/max sequence length: 16/16 [>] 8 distinct states appear in the data: 1 = 0 2 = 1 3 = 2 4 = 3 5 = 4 6 = 5 7 = 6 8 = 7 [>] state coding: [alphabet] [label] [long label] 1 0 0 Parent 2 1 1 Left 3 2 2 Married 4 3 3 Left+Marr 5 4 4 Child 6 5 5 Left+Child 7 6 6 Left+Marr+Child 8 7 7 Divorced [>] 300 sequences in the data set [>] min/max sequence length: 16/16 [>] found missing values ('NA') in sequence data [>] preparing 7 sequences [>] coding void elements with '%' and missing values with '*' [!!] 1 empty sequence(s) with index: 7 may produce inconsistent results. [>] 4 distinct states appear in the data: 1 = A 2 = B 3 = C 4 = D [>] state coding: [alphabet] [label] [long label] 1 A A A 2 B B B 3 C C C 4 D D D [>] sum of weights: 60 - min/max: 0/29.3 [>] 7 sequences in the data set [>] min/max sequence length: 0/13 [>] 4 distinct states appear in the data: 1 = A 2 = B 3 = C 4 = D [>] state coding: [alphabet] [label] [long label] 1 A A > 37 hours 2 B B 19-36 hours 3 C C 1-18 hours 4 D D no work [>] 300 sequences in the data set [>] min/max sequence length: 12/12 [>] found missing values ('NA') in sequence data [>] preparing 7 sequences [>] coding void elements with '%' and missing values with '*' [!!] 1 empty sequence(s) with index: 7 may produce inconsistent results. [>] 4 distinct states appear in the data: 1 = A 2 = B 3 = C 4 = D [>] state coding: [alphabet] [label] [long label] 1 A A A 2 B B B 3 C C C 4 D D D [>] sum of weights: 60 - min/max: 0/29.3 [>] 7 sequences in the data set [>] min/max sequence length: 0/13 [>] 4 distinct states appear in the data: 1 = A 2 = B 3 = C 4 = D [>] state coding: [alphabet] [label] [long label] 1 A A > 37 hours 2 B B 19-36 hours 3 C C 1-18 hours 4 D D no work [>] 300 sequences in the data set [>] min/max sequence length: 12/12 [>] found missing values ('NA') in sequence data [>] preparing 7 sequences [>] coding void elements with '%' and missing values with '*' [!!] 1 empty sequence(s) with index: 7 may produce inconsistent results. [>] 4 distinct states appear in the data: 1 = A 2 = B 3 = C 4 = D [>] state coding: [alphabet] [label] [long label] 1 A A A 2 B B B 3 C C C 4 D D D [>] sum of weights: 60 - min/max: 0/29.3 [>] 7 sequences in the data set [>] min/max sequence length: 0/13 [>] 4 distinct states appear in the data: 1 = A 2 = B 3 = C 4 = D [>] state coding: [alphabet] [label] [long label] 1 A A > 37 hours 2 B B 19-36 hours 3 C C 1-18 hours 4 D D no work [>] 300 sequences in the data set [>] min/max sequence length: 12/12 [>] found missing values ('NA') in sequence data [>] preparing 7 sequences [>] coding void elements with '%' and missing values with '*' [!!] 1 empty sequence(s) with index: 7 may produce inconsistent results. [>] 4 distinct states appear in the data: 1 = A 2 = B 3 = C 4 = D [>] state coding: [alphabet] [label] [long label] 1 A A A 2 B B B 3 C C C 4 D D D [>] sum of weights: 60 - min/max: 0/29.3 [>] 7 sequences in the data set [>] min/max sequence length: 0/13 [>] 4 distinct states appear in the data: 1 = A 2 = B 3 = C 4 = D [>] state coding: [alphabet] [label] [long label] 1 A A > 37 hours 2 B B 19-36 hours 3 C C 1-18 hours 4 D D no work [>] 300 sequences in the data set [>] min/max sequence length: 12/12 [>] found missing values ('NA') in sequence data [>] preparing 7 sequences [>] coding void elements with '%' and missing values with '*' [!!] 1 empty sequence(s) with index: 7 may produce inconsistent results. [>] 4 distinct states appear in the data: 1 = A 2 = B 3 = C 4 = D [>] state coding: [alphabet] [label] [long label] 1 A A A 2 B B B 3 C C C 4 D D D [>] sum of weights: 60 - min/max: 0/29.3 [>] 7 sequences in the data set [>] min/max sequence length: 0/13 [>] 8 distinct states appear in the data: 1 = 0 2 = 1 3 = 2 4 = 3 5 = 4 6 = 5 7 = 6 8 = 7 [>] state coding: [alphabet] [label] [long label] 1 0 0 Parent 2 1 1 Left 3 2 2 Married 4 3 3 Left+Marr 5 4 4 Child 6 5 5 Left+Child 7 6 6 Left+Marr+Child 8 7 7 Divorced [>] 100 sequences in the data set [>] min/max sequence length: 16/16 [>] 8 distinct states appear in the data: 1 = 0 2 = 1 3 = 2 4 = 3 5 = 4 6 = 5 7 = 6 8 = 7 [>] state coding: [alphabet] [label] [long label] 1 0 0 Parent 2 1 1 Left 3 2 2 Married 4 3 3 Left+Marr 5 4 4 Child 6 5 5 Left+Child 7 6 6 Left+Marr+Child 8 7 7 Divorced [>] sum of weights: 111.62 - min/max: 0/4.17260217666626 [>] 100 sequences in the data set [>] min/max sequence length: 16/16 [>] 100 sequences with 8 distinct states [>] creating a 'sm' with a substitution cost of 2 [>] creating 8x8 substitution-cost matrix using 2 as constant value [>] 76 distinct sequences [>] min/max sequence lengths: 16/16 [>] computing distances using the LCS metric [>] elapsed time: 0.03 secs [>] computing state distribution for 100 sequences ... [>] Using k=12 frequency groups with grp.meth='prop' [>] Pseudo/medoid-based-R2: 0.3064171 [>] Pseudo/medoid-based-F statistic: 4.001018, p-value: 7.736543e-05 [>] Using k=12 frequency groups with grp.meth='prop' [>] Pseudo/medoid-based-R2: 0.45323 [>] Pseudo/medoid-based-F statistic: 6.63138, p-value: 5.825085e-08 [>] Using k=12 frequency groups with grp.meth='prop' [>] Pseudo/medoid-based-R2: 0.45323 [>] Pseudo/medoid-based-F statistic: 6.63138, p-value: 5.825085e-08 [>] Using k=12 frequency groups with grp.meth='first' [>] Pseudo/medoid-based-R2: 0.4620155 [>] Pseudo/medoid-based-F statistic: 6.870317, p-value: 3.09994e-08 [>] Using k=11 frequency groups with grp.meth='prop' [>] Pseudo/medoid-based-R2: 0.4604651 [>] Pseudo/medoid-based-F statistic: 7.59569, p-value: 1.205403e-08 [>] Using k=10 frequency groups with grp.meth='prop' [>] Pseudo/medoid-based-R2: 0.448062 [>] Pseudo/medoid-based-F statistic: 8.117978, p-value: 1.001438e-08 [>] Using k=10 frequency groups with grp.meth='prop' [>] Pseudo/medoid-based-R2: 0.448062 [>] Pseudo/medoid-based-F statistic: 8.117978, p-value: 1.001438e-08 Scale for y is already present. Adding another scale for y, which will replace the existing scale. [>] Using k=10 frequency groups with grp.meth='prop' [>] Pseudo/medoid-based-R2: 0.448062 [>] Pseudo/medoid-based-F statistic: 8.117978, p-value: 1.001438e-08 [>] Using k=10 frequency groups with grp.meth='prop' [>] Pseudo/medoid-based-R2: 0.448062 [>] Pseudo/medoid-based-F statistic: 8.117978, p-value: 1.001438e-08 [>] 8 distinct states appear in the data: 1 = 0 2 = 1 3 = 2 4 = 3 5 = 4 6 = 5 7 = 6 8 = 7 [>] state coding: [alphabet] [label] [long label] 1 0 0 Parent 2 1 1 Left 3 2 2 Married 4 3 3 Left+Marr 5 4 4 Child 6 5 5 Left+Child 7 6 6 Left+Marr+Child 8 7 7 Divorced [>] 100 sequences in the data set [>] min/max sequence length: 16/16 [>] 100 sequences with 8 distinct states [>] creating a 'sm' with a substitution cost of 2 [>] creating 8x8 substitution-cost matrix using 2 as constant value [>] 76 distinct sequences [>] min/max sequence lengths: 16/16 [>] computing distances using the LCS metric [>] elapsed time: 0.01 secs [>] number of objects (sum of weights): 100 [>] max. distance: 32 [>] neighborhood radius: 3.2 [>] 3 representative(s) selected, coverage=27% (threshold=25%) [>] 76 distinct sequence(s) [>] number of objects (sum of weights): 100 [>] max. distance: 32 [>] neighborhood radius: 3.2 [>] 3 representative(s) selected, coverage=27% (threshold=25%) [>] 76 distinct sequence(s) [>] number of objects (sum of weights): 7 [>] max. distance: 30 [>] neighborhood radius: 3 [>] 1 representative(s) selected, coverage=29% (threshold=25%) [>] 6 distinct sequence(s) [>] number of objects (sum of weights): 3 [>] max. distance: 24 [>] neighborhood radius: 2.4 [>] 1 representative(s) selected, coverage=33% (threshold=25%) [>] 3 distinct sequence(s) [>] number of objects (sum of weights): 4 [>] max. distance: 28 [>] neighborhood radius: 2.8 [>] 1 representative(s) selected, coverage=25% (threshold=25%) [>] 4 distinct sequence(s) [>] number of objects (sum of weights): 11 [>] max. distance: 22 [>] neighborhood radius: 2.2 [>] 2 representative(s) selected, coverage=27% (threshold=25%) [>] 11 distinct sequence(s) [>] number of objects (sum of weights): 7 [>] max. distance: 28 [>] neighborhood radius: 2.8 [>] 2 representative(s) selected, coverage=29% (threshold=25%) [>] 7 distinct sequence(s) [>] number of objects (sum of weights): 7 [>] max. distance: 24 [>] neighborhood radius: 2.4 [>] 1 representative(s) selected, coverage=29% (threshold=25%) [>] 7 distinct sequence(s) [>] number of objects (sum of weights): 12 [>] max. distance: 30 [>] neighborhood radius: 3 [>] 2 representative(s) selected, coverage=25% (threshold=25%) [>] 12 distinct sequence(s) [>] number of objects (sum of weights): 11 [>] max. distance: 24 [>] neighborhood radius: 2.4 [>] 3 representative(s) selected, coverage=27% (threshold=25%) [>] 11 distinct sequence(s) [>] number of objects (sum of weights): 14 [>] max. distance: 30 [>] neighborhood radius: 3 [>] 2 representative(s) selected, coverage=29% (threshold=25%) [>] 14 distinct sequence(s) [>] number of objects (sum of weights): 15 [>] max. distance: 32 [>] neighborhood radius: 3.2 [>] 2 representative(s) selected, coverage=27% (threshold=25%) [>] 15 distinct sequence(s) [>] number of objects (sum of weights): 9 [>] max. distance: 22 [>] neighborhood radius: 2.2 [>] 2 representative(s) selected, coverage=33% (threshold=25%) [>] 9 distinct sequence(s) ! You are trying to render a representative sequence plot for many groups. The resulting output (if produced at all) might be hard to decipher. i Consider reducing the number of distinct groups. ! You are trying to render a representative sequence plot for many groups using just one column. The resulting output (if produced at all) might be hard to decipher. i Consider reducing the number of distinct groups or increase `facet_ncol`. [>] number of objects (sum of weights): 21 [>] max. distance: 32 [>] neighborhood radius: 3.2 [>] 3 representative(s) selected, coverage=29% (threshold=25%) [>] 21 distinct sequence(s) [>] number of objects (sum of weights): 20 [>] max. distance: 30 [>] neighborhood radius: 3 [>] 3 representative(s) selected, coverage=30% (threshold=25%) [>] 18 distinct sequence(s) [>] number of objects (sum of weights): 23 [>] max. distance: 30 [>] neighborhood radius: 3 [>] 2 representative(s) selected, coverage=30% (threshold=25%) [>] 19 distinct sequence(s) [>] number of objects (sum of weights): 17 [>] max. distance: 28 [>] neighborhood radius: 2.8 [>] 4 representative(s) selected, coverage=29% (threshold=25%) [>] 17 distinct sequence(s) [>] number of objects (sum of weights): 19 [>] max. distance: 28 [>] neighborhood radius: 2.8 [>] 1 representative(s) selected, coverage=26% (threshold=25%) [>] 16 distinct sequence(s) ! You are trying to render a representative sequence plot for many groups using just one column. The resulting output (if produced at all) might be hard to decipher. i Consider reducing the number of distinct groups or increase `facet_ncol`. [>] number of objects (sum of weights): 100 [>] max. distance: 32 [>] neighborhood radius: 3.2 [>] 3 representative(s) selected, coverage=27% (threshold=25%) [>] 76 distinct sequence(s) [>] number of objects (sum of weights): 100 [>] max. distance: 32 [>] neighborhood radius: 3.2 [>] 11 representative(s) selected [>] 76 distinct sequence(s) [>] number of objects (sum of weights): 100 [>] max. distance: 32 [>] neighborhood radius: 3.2 [>] 3 representative(s) selected, coverage=27% (threshold=25%) [>] 76 distinct sequence(s) [>] number of objects (sum of weights): 100 [>] max. distance: 32 [>] neighborhood radius: 3.2 [>] 3 representative(s) selected, coverage=27% (threshold=25%) [>] 76 distinct sequence(s) [>] number of objects (sum of weights): 21 [>] max. distance: 32 [>] neighborhood radius: 3.2 [>] 3 representative(s) selected, coverage=29% (threshold=25%) [>] 21 distinct sequence(s) [>] number of objects (sum of weights): 20 [>] max. distance: 30 [>] neighborhood radius: 3 [>] 3 representative(s) selected, coverage=30% (threshold=25%) [>] 18 distinct sequence(s) [>] number of objects (sum of weights): 23 [>] max. distance: 30 [>] neighborhood radius: 3 [>] 2 representative(s) selected, coverage=30% (threshold=25%) [>] 19 distinct sequence(s) [>] number of objects (sum of weights): 17 [>] max. distance: 28 [>] neighborhood radius: 2.8 [>] 4 representative(s) selected, coverage=29% (threshold=25%) [>] 17 distinct sequence(s) [>] number of objects (sum of weights): 19 [>] max. distance: 28 [>] neighborhood radius: 2.8 [>] 1 representative(s) selected, coverage=26% (threshold=25%) [>] 16 distinct sequence(s) ! You are trying to render a representative sequence plot for many groups using just one column. The resulting output (if produced at all) might be hard to decipher. i Consider reducing the number of distinct groups or increase `facet_ncol`. [>] number of objects (sum of weights): 21 [>] max. distance: 32 [>] neighborhood radius: 3.2 [>] 3 representative(s) selected, coverage=29% (threshold=25%) [>] 21 distinct sequence(s) [>] number of objects (sum of weights): 20 [>] max. distance: 30 [>] neighborhood radius: 3 [>] 3 representative(s) selected, coverage=30% (threshold=25%) [>] 18 distinct sequence(s) [>] number of objects (sum of weights): 23 [>] max. distance: 30 [>] neighborhood radius: 3 [>] 2 representative(s) selected, coverage=30% (threshold=25%) [>] 19 distinct sequence(s) [>] number of objects (sum of weights): 17 [>] max. distance: 28 [>] neighborhood radius: 2.8 [>] 4 representative(s) selected, coverage=29% (threshold=25%) [>] 17 distinct sequence(s) [>] number of objects (sum of weights): 19 [>] max. distance: 28 [>] neighborhood radius: 2.8 [>] 1 representative(s) selected, coverage=26% (threshold=25%) [>] 16 distinct sequence(s) ! You are trying to render a representative sequence plot for many groups using just one column. The resulting output (if produced at all) might be hard to decipher. i Consider reducing the number of distinct groups or increase `facet_ncol`. [>] 8 distinct states appear in the data: 1 = 0 2 = 1 3 = 2 4 = 3 5 = 4 6 = 5 7 = 6 8 = 7 [>] state coding: [alphabet] [label] [long label] 1 0 0 Parent 2 1 1 Left 3 2 2 Married 4 3 3 Left+Marr 5 4 4 Child 6 5 5 Left+Child 7 6 6 Left+Marr+Child 8 7 7 Divorced [>] sum of weights: 330.07 - min/max: 0/6.02881860733032 [>] 300 sequences in the data set [>] min/max sequence length: 16/16 [>] 8 distinct states appear in the data: 1 = 0 2 = 1 3 = 2 4 = 3 5 = 4 6 = 5 7 = 6 8 = 7 [>] state coding: [alphabet] [label] [long label] 1 0 0 Parent 2 1 1 Left 3 2 2 Married 4 3 3 Left+Marr 5 4 4 Child 6 5 5 Left+Child 7 6 6 Left+Marr+Child 8 7 7 Divorced [>] 300 sequences in the data set [>] min/max sequence length: 16/16 [>] found missing values ('NA') in sequence data [>] preparing 7 sequences [>] coding void elements with '%' and missing values with '*' [!!] 1 empty sequence(s) with index: 7 may produce inconsistent results. [>] 4 distinct states appear in the data: 1 = A 2 = B 3 = C 4 = D [>] state coding: [alphabet] [label] [long label] 1 A A A 2 B B B 3 C C C 4 D D D [>] sum of weights: 60 - min/max: 0/29.3 [>] 7 sequences in the data set [>] min/max sequence length: 0/13 [>] computing transition probabilities for states 0/1/2/3/4/5/6/7 ... [>] computing transition probabilities for states 0/1/2/3/4/5/6/7 ... [>] computing transition probabilities for states 0/1/2/3/4/5/6/7 ... [>] computing transition probabilities for states 0/1/2/3/4/5/6/7 ... [>] computing transition probabilities for states 0/1/2/3/4/5/6/7 ... [>] computing transition probabilities for states 0/1/2/3/4/5/6/7 ... [>] computing transition probabilities for states A/B/C/D ... [>] computing transition probabilities for states A/B/C/D/* ... [>] computing transition probabilities for states A/B/C/D ... [ FAIL 0 | WARN 11 | SKIP 0 | PASS 137 ] [ FAIL 0 | WARN 11 | SKIP 0 | PASS 137 ] > > proc.time() user system elapsed 26.85 0.76 27.61