Last updated on 2018-09-26 01:46:57 CEST.
| Package | ERROR | NOTE |
|---|---|---|
| dtwclust | 1 | 11 |
Current CRAN status: ERROR: 1, NOTE: 11
Version: 5.5.0
Check: whether the namespace can be unloaded cleanly
Result: WARN
---- unloading
Error in .mergeMethodsTable(generic, mtable, tt, attach) :
trying to get slot "defined" from an object of a basic class ("environment") with no slots
Calls: unloadNamespace ... <Anonymous> -> .updateMethodsInTable -> .mergeMethodsTable
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 5.5.0
Check: for GNU extensions in Makefiles
Result: NOTE
GNU make is a SystemRequirements.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-windows-ix86+x86_64, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64
Version: 5.5.0
Check: examples
Result: ERROR
Running examples in ‘dtwclust-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: tsclust
> ### Title: Time series clustering
> ### Aliases: tsclust
>
> ### ** Examples
>
> #' NOTE: More examples are available in the vignette. Here are just some miscellaneous
> #' examples that might come in handy. They should all work, but some don't run
> #' automatically.
>
> # Load data
> data(uciCT)
>
> # ====================================================================================
> # Simple partitional clustering with Euclidean distance and PAM centroids
> # ====================================================================================
>
> # Reinterpolate to same length
> series <- reinterpolate(CharTraj, new.length = max(lengths(CharTraj)))
>
> # Subset for speed
> series <- series[1:20]
> labels <- CharTrajLabels[1:20]
>
> # Making many repetitions
> pc.l2 <- tsclust(series, k = 4L,
+ distance = "L2", centroid = "pam",
+ seed = 3247, trace = TRUE,
+ control = partitional_control(nrep = 10L))
Precomputing distance matrix...
Repetition 1 for k = 4
Iteration 1: Changes / Distsum = 20 / 106.6446
Iteration 2: Changes / Distsum = 2 / 53.2297
Iteration 3: Changes / Distsum = 0 / 49.6027
Repetition 2 for k = 4
Iteration 1: Changes / Distsum = 20 / 130.5215
Iteration 2: Changes / Distsum = 0 / 116.7966
Repetition 3 for k = 4
Iteration 1: Changes / Distsum = 20 / 106.2209
Iteration 2: Changes / Distsum = 0 / 99.09456
Repetition 4 for k = 4
Iteration 1: Changes / Distsum = 20 / 82.09483
Iteration 2: Changes / Distsum = 1 / 68.2079
Iteration 3: Changes / Distsum = 0 / 68.2079
Repetition 5 for k = 4
Iteration 1: Changes / Distsum = 20 / 84.34789
Iteration 2: Changes / Distsum = 2 / 50.56039
Iteration 3: Changes / Distsum = 0 / 49.6027
Repetition 6 for k = 4
Iteration 1: Changes / Distsum = 20 / 76.41461
Iteration 2: Changes / Distsum = 2 / 49.6027
Iteration 3: Changes / Distsum = 0 / 49.6027
Repetition 7 for k = 4
Iteration 1: Changes / Distsum = 20 / 78.83074
Iteration 2: Changes / Distsum = 2 / 49.6027
Iteration 3: Changes / Distsum = 0 / 49.6027
Repetition 8 for k = 4
Iteration 1: Changes / Distsum = 20 / 74.16051
Iteration 2: Changes / Distsum = 1 / 49.6027
Iteration 3: Changes / Distsum = 0 / 49.6027
Repetition 9 for k = 4
Iteration 1: Changes / Distsum = 20 / 77.99961
Iteration 2: Changes / Distsum = 2 / 50.56039
Iteration 3: Changes / Distsum = 0 / 49.6027
Repetition 10 for k = 4
Iteration 1: Changes / Distsum = 20 / 101.6274
Iteration 2: Changes / Distsum = 0 / 101.1829
Elapsed time is 0.187 seconds.
>
> # Cluster validity indices
> sapply(pc.l2, cvi, b = labels)
[,1] [,2] [,3] [,4] [,5]
ARI 1.000000e+00 0.2002053388 6.149546e-01 6.149546e-01 1.000000e+00
RI 1.000000e+00 0.5684210526 8.473684e-01 8.473684e-01 1.000000e+00
J 1.000000e+00 0.2869565217 5.538462e-01 5.538462e-01 1.000000e+00
FM 1.000000e+00 0.5020790110 7.287987e-01 7.287987e-01 1.000000e+00
VI 0.000000e+00 0.4610152539 2.048455e-01 2.048455e-01 0.000000e+00
Sil 6.331221e-01 0.3119630506 3.149087e-01 5.346690e-01 6.331221e-01
SF 4.606696e-04 0.0003004822 1.752593e-05 3.238172e-04 4.606696e-04
CH 1.925006e+01 3.1947649639 5.413882e+00 1.258269e+01 1.925006e+01
DB 4.615583e-01 0.5873421456 1.130403e+00 5.573207e-01 4.615583e-01
DBstar 6.271176e-01 1.7248522405 1.580351e+00 8.294109e-01 6.271176e-01
D 7.808069e-01 0.1689797902 2.655638e-01 5.926710e-01 7.808069e-01
COP 1.207865e-01 0.4285157845 3.098501e-01 1.886585e-01 1.207865e-01
[,6] [,7] [,8] [,9] [,10]
ARI 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 5.819730e-01
RI 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 8.368421e-01
J 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 5.230769e-01
FM 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 6.998789e-01
VI 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.235863e-01
Sil 6.331221e-01 6.331221e-01 6.331221e-01 6.331221e-01 2.417737e-01
SF 4.606696e-04 4.606696e-04 4.606696e-04 4.606696e-04 4.195698e-06
CH 1.925006e+01 1.925006e+01 1.925006e+01 1.925006e+01 5.270039e+00
DB 4.615583e-01 4.615583e-01 4.615583e-01 4.615583e-01 1.321109e+00
DBstar 6.271176e-01 6.271176e-01 6.271176e-01 6.271176e-01 2.252835e+00
D 7.808069e-01 7.808069e-01 7.808069e-01 7.808069e-01 1.623149e-01
COP 1.207865e-01 1.207865e-01 1.207865e-01 1.207865e-01 3.113853e-01
>
> # ====================================================================================
> # Hierarchical clustering with Euclidean distance
> # ====================================================================================
>
> # Re-use the distance matrix from the previous example (all matrices are the same)
> # Use all available linkage methods for function hclust
> hc.l2 <- tsclust(series, type = "hierarchical",
+ k = 4L, trace = TRUE,
+ control = hierarchical_control(method = "all",
+ distmat = pc.l2[[1L]]@distmat))
Distance matrix provided...
Performing hierarchical clustering...
Extracting centroids...
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Elapsed time is 0.21 seconds.
>
> # Plot the best dendrogram according to variation of information
> plot(hc.l2[[which.min(sapply(hc.l2, cvi, b = labels, type = "VI"))]])
Found more than one class "hclust" in cache; using the first, from namespace 'flexclust'
Also defined by ‘dtwclust’
Error in plot.hclust(x, ...) : invalid dendrogram
Calls: plot -> plot -> .local -> <Anonymous> -> plot.hclust
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 5.5.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [171s/123s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(dtwclust)
Loading required package: proxy
Attaching package: 'proxy'
The following objects are masked from 'package:stats':
as.dist, dist
The following object is masked from 'package:base':
as.matrix
Loading required package: dtw
Loaded dtw v1.20-1. See ?dtw for help, citation("dtw") for use in publication.
dtwclust:
Setting random number generator to L'Ecuyer-CMRG (see RNGkind()).
To read the included vignettes type: browseVignettes("dtwclust").
See news(package = "dtwclust") after package updates.
> library(foreach)
> library(testthat)
>
> # coverage for multi-threading might not be possible (?)
> if (nzchar(Sys.getenv("R_COVR"))) RcppParallel::setThreadOptions(1L)
> # old reporter for CMD checks
> options(testthat.default_reporter = "summary")
>
> #' To test in a local machine:
> #' Sys.setenv(NOT_CRAN = "true"); test_dir("tests/testthat/")
> #' OR
> #' devtools::test() # broken, can't figure out why
> #'
> #' To disable parallel tests, before calling test() run:
> #'
> #' options(dtwclust_skip_par_tests = TRUE)
> #'
> testthat::test_check("dtwclust")
── 1. Error: Methods for TSClusters objects are dispatched correctly. ─────────
invalid dendrogram
1: expect_silent(plot(hierarchical_object, type = "dendrogram"))
2: quasi_capture(enquo(object), evaluate_promise)
3: capture(act$val <- eval_bare(get_expr(quo), get_env(quo)))
4: withr::with_output_sink(temp, withCallingHandlers(withVisible(code), warning = handle_warning,
message = handle_message))
5: force(code)
6: withCallingHandlers(withVisible(code), warning = handle_warning, message = handle_message)
7: withVisible(code)
8: eval_bare(get_expr(quo), get_env(quo))
9: plot(hierarchical_object, type = "dendrogram")
10: plot(hierarchical_object, type = "dendrogram")
11: .local(x, y, ...)
12: graphics::plot(x, ...)
13: plot.hclust(x, ...)
14: stop(msg)
══ testthat results ═══════════════════════════════════════════════════════════
OK: 1886 SKIPPED: 3 FAILED: 1
1. Error: Methods for TSClusters objects are dispatched correctly.
Error: testthat unit tests failed
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 5.5.0
Check: re-building of vignette outputs
Result: WARN
Error in re-building vignettes:
...
Quitting from lines 1037-1040 (dtwclust.Rnw)
Error: processing vignette ‘dtwclust.Rnw’ failed with diagnostics:
invalid dendrogram
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 5.5.1
Check: for GNU extensions in Makefiles
Result: NOTE
GNU make is a SystemRequirements.
Flavors: r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-linux-x86_64, r-patched-solaris-x86
Version: 5.5.1
Check: installed package size
Result: NOTE
installed size is 9.0Mb
sub-directories of 1Mb or more:
doc 2.5Mb
libs 5.3Mb
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 5.5.0
Check: installed package size
Result: NOTE
installed size is 5.8Mb
sub-directories of 1Mb or more:
doc 2.5Mb
libs 2.1Mb
Flavors: r-devel-windows-ix86+x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64