Dear All,
I am running R on a debian testing machine and lately I have
experienced several segmentation faults (often when running Amelia on
some large data set).
However, please have a look at the script pasted at the end of the
email.
If I uncomment the line about the RJSDMX library (which does precisely
nothing in this script), the script causes a segmentation fault
killing my R session.
Anybody else experiences this?
Here is my session_info()
sessionInfo()
R version 3.2.3 (2015-12-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux stretch/sid
locale:
[1] LC_CTYPE=en_GB.utf8 LC_NUMERIC=C
[3] LC_TIME=en_GB.utf8 LC_COLLATE=en_GB.utf8
[5] LC_MONETARY=en_GB.utf8 LC_MESSAGES=en_GB.utf8
[7] LC_PAPER=en_GB.utf8 LC_NAME=en_GB.utf8
[9] LC_ADDRESS=en_GB.utf8 LC_TELEPHONE=en_GB.utf8
[11] LC_MEASUREMENT=en_GB.utf8 LC_IDENTIFICATION=en_GB.utf8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] RJSDMX_1.5 zoo_1.7-12 rJava_0.9-8
tempdisagg_0.24.0
loaded via a namespace (and not attached):
[1] grid_3.2.3 lattice_0.20-33
Regards
Lorenzo
#################################################
rm(list=ls())
library(tempdisagg)
## library(RJSDMX)
ts2 <- structure(c(339130, 356462, 363234, 378179, 367864, 378337,
392157,
402153, 376361, 392204, 403483, 414034, 391967, 406067, 419464,
434913, 410102, 424795, 437073, 448827, 415569, 430561, 444719,
455764, 419892, 444190, 454648, 466312, 439922, 448963, 465153,
475621, 445502, 457198, 473573, 485764, 463895, 470274, 484390,
490678, 478003, 483570, 499141, 509216, 481395, 492345, 511184,
513420, 483757, 490884, 514966, 515457, 497614, 510139, 523467,
526406, 499784, 519033, 532009, 531260, 521539, 532590, 553118,
557725, 548321, 556832, 578087, 578120, 566116, 580571, 587993,
569985, 534326, 539641, 564824, 568445, 558614, 570192, 594584,
598305, 593769, 598278, 620147, 615884, 611033, 609304, 630458,
624325, 614356, 627192, 649324, 645988, 642965, 645125, 669471,
665529, 664248, 669670, 694719), na.action = structure(1:64, class
"omit"), .Tsp = c(1991,
2015.5, 4), class = "ts")
tsq <- td(ts2 ~ 1, to = "monthly", method =
"denton-cholette")
tsm <- predict(tsq)