Running examples in 'arulesSequences-Ex.R' failed
The error most likely occurred in:

> ### Name: c-methods
> ### Title: Combining Objects
> ### Aliases: c c,sequences-method c,timedsequences-method
> ###   c,sequencerules-method
> ### Keywords: manip attribute
> 
> ### ** Examples
> 
> ## continue example
> example(ruleInduction, package = "arulesSequences")

rlIndc> ## continue example
rlIndc> example(cspade)

cspade> ## use example data from paper
cspade> data(zaki)

cspade> ## get support bearings
cspade> s0 <- cspade(zaki, parameter = list(support = 0,
cspade+                                     maxsize = 1, maxlen = 1),
cspade+                    control   = list(verbose = TRUE))

parameter specification:
support : 0
maxsize : 1
maxlen  : 1

algorithmic control:
bfstype  : FALSE
verbose  :  TRUE
summary  : FALSE
tidLists : FALSE

preprocessing ... 1 partition(s), 0 MB [0.17s]
mining transactions ... 0 MB [0.19s]
reading sequences ... [0.03s]

total elapsed time: 0.39s

cspade> as(s0, "data.frame")
  sequence support
1    <{A}>    1.00
2    <{B}>    1.00
3    <{C}>    0.25
4    <{D}>    0.50
5    <{E}>    0.25
6    <{F}>    1.00
7    <{G}>    0.25
8    <{H}>    0.25

cspade> ## mine frequent sequences
cspade> s1 <- cspade(zaki, parameter = list(support = 0.4), 
cspade+ 		   control   = list(verbose = TRUE, tidLists = TRUE))

parameter specification:
support : 0.4
maxsize :  10
maxlen  :  10

algorithmic control:
bfstype  : FALSE
verbose  :  TRUE
summary  : FALSE
tidLists :  TRUE

preprocessing ... 1 partition(s), 0 MB [0.02s]
mining transactions ... 0 MB [0.03s]
reading sequences ... [0.05s]

total elapsed time: 0.1s

cspade> summary(s1)
set of 18 sequences with

most frequent items:
      A       B       F       D (Other) 
     11      10      10       8      28 

most frequent elements:
    {A}     {D}     {B}     {F}   {B,F} (Other) 
      8       8       4       4       4       3 

element (sequence) size distribution:
sizes
1 2 3 
8 7 3 

sequence length distribution:
lengths
1 2 3 4 
4 8 5 1 

summary of quality measures:
    support      
 Min.   :0.5000  
 1st Qu.:0.5000  
 Median :0.5000  
 Mean   :0.6528  
 3rd Qu.:0.7500  
 Max.   :1.0000  

includes transaction ID lists: TRUE 

mining info:
 data ntransactions nsequences support
 zaki            10          4     0.4

cspade> as(s1, "data.frame")
          sequence support
1            <{A}>    1.00
2            <{B}>    1.00
3            <{D}>    0.50
4            <{F}>    1.00
5          <{A,F}>    0.75
6          <{B,F}>    1.00
7        <{D},{F}>    0.50
8      <{D},{B,F}>    0.50
9        <{A,B,F}>    0.75
10         <{A,B}>    0.75
11       <{D},{B}>    0.50
12       <{B},{A}>    0.50
13       <{D},{A}>    0.50
14       <{F},{A}>    0.50
15   <{D},{F},{A}>    0.50
16     <{B,F},{A}>    0.50
17 <{D},{B,F},{A}>    0.50
18   <{D},{B},{A}>    0.50

cspade> ##
cspade> summary(tidLists(s1))
tidLists in sparse format with
 18 items/itemsets (rows) and
 4 transactions (columns)

most frequent transactions:
      1       2       4       6       5 (Other) 
      4       4       4       4       3      28 

item frequency distribution:
sizes
 2  3  4 
11  3  4 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.000   2.000   2.000   2.611   3.000   4.000 

includes extended item information - examples:
  labels
1  <{A}>
2  <{B}>
3  <{D}>

cspade> transactionInfo(tidLists(s1))
  sequenceID
1          1
2          2
3          3
4          4

cspade> ## use timing constraint
cspade> s2 <- cspade(zaki, parameter = list(support = 0.4, maxgap = 5))

cspade> as(s2, "data.frame")
      sequence support
1        <{A}>    1.00
2        <{B}>    1.00
3        <{D}>    0.50
4        <{F}>    1.00
5      <{A,F}>    0.75
6      <{B,F}>    1.00
7    <{A,B,F}>    0.75
8      <{A,B}>    0.75
9    <{B},{A}>    0.50
10   <{F},{A}>    0.50
11 <{B,F},{A}>    0.50

cspade> ## use classification
cspade> t <- zaki

cspade> transactionInfo(t)$classID <-
cspade+     as.integer(transactionInfo(t)$sequenceID) %% 2 + 1L

cspade> s3 <- cspade(t, parameter = list(support = 0.4, maxgap = 5))
Warning in Ops.factor(c(x), 1L) : '-' not meaningful for factors

cspade> as(s3, "data.frame")
      sequence support   1 2
1        <{A}>    1.00 2.0 0
2        <{B}>    1.00 2.0 0
3        <{D}>    0.50 1.0 0
4        <{F}>    1.00 2.0 0
5      <{A,F}>    0.75 1.5 0
6      <{B,F}>    1.00 2.0 0
7    <{A,B,F}>    0.75 1.5 0
8      <{A,B}>    0.75 1.5 0
9    <{B},{A}>    0.50 1.0 0
10   <{F},{A}>    0.50 1.0 0
11 <{B,F},{A}>    0.50 1.0 0

cspade> ## replace timestamps
cspade> t <- zaki

cspade> transactionInfo(t)$eventID <-
cspade+     unlist(tapply(seq(t), transactionInfo(t)$sequenceID,
cspade+ 	function(x) x - min(x) + 1), use.names = FALSE)

cspade> as(t, "data.frame")
       items sequenceID eventID SIZE
1      {C,D}          1       1    2
2    {A,B,C}          1       2    3
3    {A,B,F}          1       3    3
4  {A,C,D,F}          1       4    4
5    {A,B,F}          2       1    3
6        {E}          2       2    1
7    {A,B,F}          3       1    3
8    {D,G,H}          4       1    3
9      {B,F}          4       2    2
10   {A,G,H}          4       3    3

cspade> s4 <- cspade(t, parameter = list(support = 0.4))

cspade> s4
set of 18 sequences 

cspade> identical(as(s1, "data.frame"), as(s4, "data.frame"))
[1] TRUE

cspade> ## work around
cspade> s5 <- cspade(zaki, parameter = list(support = .25, maxgap = 5))

cspade> length(s5)
[1] 3297

cspade> k <- support(s5, zaki, control   = list(verbose = TRUE,
cspade+                        parameter = list(maxwin = 5)))
using method: idlists 

parameter specification:
support : NA
maxsize : NA
maxlen  : NA
maxwin  :  5

preprocessing ... L1 [0s]

cspade> table(size(s5[k == 0]))

   3    4 
 873 2205 

cspade> ## Not run: 
cspade> ##D ## use generated data
cspade> ##D t <- read_baskets(con  = system.file("misc", "test.txt", package =
cspade> ##D 				      "arulesSequences"),
cspade> ##D 		  info = c("sequenceID", "eventID", "SIZE"))
cspade> ##D summary(t)
cspade> ##D ## use low support
cspade> ##D s6 <- cspade(t, parameter = list(support = 0.0133), 
cspade> ##D 		control   = list(verbose = TRUE))
cspade> ##D summary(s6)
cspade> ##D 
cspade> ##D ## check
cspade> ##D k <- support(s6, t, control = list(verbose = TRUE))
cspade> ##D table(size(s6), sign(quality(s6)$support -k))
cspade> ##D 
cspade> ##D ## use low confidence
cspade> ##D r6 <- ruleInduction(s6, confidence = .5,
cspade> ##D 			control    = list(verbose = TRUE))
cspade> ##D summary(r6)
cspade> ## End(Not run)
cspade> 
cspade> 
cspade> 

rlIndc> ## mine rules
rlIndc> r2 <- ruleInduction(s2, confidence = 0.5,
rlIndc+ 			control    = list(verbose = TRUE))

rlIndc> summary(r2)
set of 3 sequencerules with

rule size distribution (lhs + rhs)
sizes
2 
3 

rule length distribution (lhs + rhs)
lengths
2 3 
2 1 

summary of quality measures:
    support      confidence       lift    
 Min.   :0.5   Min.   :0.5   Min.   :0.5  
 1st Qu.:0.5   1st Qu.:0.5   1st Qu.:0.5  
 Median :0.5   Median :0.5   Median :0.5  
 Mean   :0.5   Mean   :0.5   Mean   :0.5  
 3rd Qu.:0.5   3rd Qu.:0.5   3rd Qu.:0.5  
 Max.   :0.5   Max.   :0.5   Max.   :0.5  

mining info:
 data ntransactions nsequences support confidence
 zaki            10          4     0.4        0.5

rlIndc> as(r2, "data.frame")
              rule support confidence lift
1   <{B}> => <{A}>     0.5        0.5  0.5
2   <{F}> => <{A}>     0.5        0.5  0.5
3 <{B,F}> => <{A}>     0.5        0.5  0.5
> s <- c(s1, s2)
> s
set of 29 sequences 
> match(unique(s), s1)
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18
> 
> ## combine rules
> r <- c(r2, r2[1:2])
> r
set of 5 sequencerules 
> match(unique(r), r2)
[1] 1 2 3
> 
> ## combine timed sequences
> z <- as(zaki, "timedsequences")
Warning in Ops.factor(c(t), 1L) : '-' not meaningful for factors
Error in validObject(.Object) : 
  invalid class "ngCMatrix" object: invalid object for slot "i" in class "ngCMatrix": got class "logical", should be or extend class "integer"
Calls: as ... initialize -> callNextMethod -> .nextMethod -> validObject
Execution halted