Using TraMineR I can identify frequent subsequences in a dataset of sequences. However, it only gives me a count of how often such a subsequence occur in the overall dataset, such as that it occurs in 21/22 sequences.
Is there any way of getting indices of exactly which sequences contain a specific frequent subsequence?
See function seqeapplysub.
According to help page: Checks occurrences of the subsequences subseq among the event sequences and returns the result according to the selected method.
Related
Find near duplicate string. Hi, I know there is a match, unique, duplicated function in R, but none of these does wha I'm really need. I've a unique column in my dataset that I need to go trough it to check if the number are nearly the same. For instance, the first element compared with the second has nearly equal pattern, except for the number '9'. The second compared with the third is nearly equal, except for the last number o the sequence, one is ending with 6 while other ending with 5. Lastly, the two last numbers are 100% equal. If I've used unique() function, only the last case would be correctly excluded.
I'm wondering if there is a function that I can flag nearly equal, maybe calculating the percentage of equality, so I can drive my attention to those cases with highly equality rate.
dat <- data.frame(text = c("87775956",
"987775956",
"987775955",
"987481732",
"987481732"))
I'm trying to analyse event of subsequences in TraMineR, but I really don't know what "support" and "minSupport" stand for!
I looked for the meaning, but I just found this sentences in details of seqefsub function.
There are two usages of this function. The first is for searching subsequences satisfying a support
condition. By default, the support is counted per sequence and not per occurrence, i.e. when a se-
quence contains twice a same subsequence it is counted only once.
The support is a basic concept in data mining. Here, the support of a subsequence is the number (or proportion) of sequences that contain the subsequence. We could also consider the number of time the subsequence occurs among the considered sequences and then there would be different ways of counting the number of occurrences when a same subsequence appears more than once in a same sequence.
In the seqefsub function of TraMineR you can control the counting method with the constraintargument that should be set using seqeconstraint. For the available counting methods, see the help page of this latter function and the references given therein.
Hope this helps.
I want to summarize certain patterns in an event sequence object. The reason I want to do this is that my sequences are too long (several hundred events) and this makes computation extremely difficult. I have identified frequent subsequences, and now I want to replace certain frequent subsequences with markers that denote a full subsequence (as if it were a single event).
For example, I may have a pattern that I want to replace, say FA-FA. In the sequence
FA-FA-EX-EX-FA (5 event markers)
this would now be:
FAFA_pattern-EX-EX-FA (4 event markers)
I tried something along the lines of:
library(TraMineR)
data(actcal.tse)
actcal.seqe <- seqecreate(id = actcal.tse$id,
timestamp = actcal.tse$time, event = actcal.tse$event)
actcal.seqe2 <- sub("(LowPartTime)-1-(Stop)", "replaced_pattern", actcal.seqe)
this seems to work fine, however, it converts the sequence into a text string, and it no longer functions as a sequence object. Is there a way to conduct such replacement operations while maintaining the sequence object's status as a sequence object?
I'm using R package TraMineR to make some academic research on sequence analysis.
I want to find a pattern defined as someone being in the target company, then going out, then coming back to the target company.
(simplified) I've define state A as target company; B as outside industry company and C as inside industry company.
So what I want to do is find sequences with the specific patterns A-B-A or A-C-A.
After looking at this question (Strange number of subsequences? ) and reading the user guide, specially the following passages:
4.3.3 Subsequences
A sequence u is a subsequence of x if all successive elements ui of u appear >in x in the same
order, which we simply denote by u x. According to this denition, unshared >states can appear
between those common to both sequences u and x. For example, u = S; M is a >subsequence of
x = S; U; M; MC.
and
7.3.2 Finding sequences with a given subsequence
The seqpm() function counts the number of sequences that contain a given subsequence and collects
their row index numbers. The function returns a list with two elements. The rst element, MTab,
is just a table with the number of occurrences of the given subsequence in the data. Note that
only one occurrence is counted per sequence, even when the sub-sequence appears more than one
time in the sequence. The second element of the list, MIndex, gives the row index numbers of
the sequences containing the subsequence. These index numbers may be useful for accessing the
concerned sequences (example below). Since it is easier to search a pattern in a character string,
the function rst translates the sequence data in this format when using the seqconc function with
the TRUE option.
I concluded that seqpm() was the function I needed to get the job done.
So I have sequences like:
A-A-A-A-A-B-B-B-B-B-A-A-A-A-A
And out of the definition of subsequences that i found on the mentiod sources, i figure I could find that kind of sequence by using:
seqpm(sequence,"ABA")
But that does not happen. In order to find that example sequence i need to input
seqpm(sequence,"ABBBBBA")
which is not very useful for what I need.
So do you guys see where I might've missed something ?
How can I retrieve all the sequences that do go from A to B and Back to A?
Is there a way for me to find go from A to anything else and then back to A ?
Thanks a lot !
The title of the seqpm help page is "Find substring patterns in sequences", and this is what the function actually does. It searches for sequences that contain a given substring (not a subsequence). Seems there is a formulation error in the user's guide.
A solution to find the sequences that contain given subsequences, is to convert the state sequences into event sequences with seqecreate , and then use the seqefsub and seqeapplysub function. I illustrate using the actcal data that ships with TraMineR.
library(TraMineR)
data(actcal)
actcal.seq <- seqdef(actcal[,13:24])
## displaying the first state sequences
head(actcal.seq)
## transforming into event sequences
actcal.seqe <- seqecreate(actcal.seq, tevent = "state", use.labels=FALSE)
## displaying the first event sequences
head(actcal.seqe)
## now searching for the subsequences
subs <- seqefsub(actcal.seqe, strsubseq=c("(A)-(D)","(D)-(B)"))
## and identifying the sequences that contain the subsequences
subs.pres <- seqeapplysub(subs, method="presence")
head(subs.pres)
## we can now, for example, count the sequences that contain (A)-(D)
sum(subs.pres[,1])
## or list the sequences that contain (A)-(D)
rownames(subs.pres)[subs.pres[,1]==1]
Hope this helps.
I'm using TraMineR to analyze sets of sequences. Each coherent set of sequences may contain 100 work processes from a single project for a single period of time. Using TraMineR I can easily calculate descriptive statistics for each sequence, however I'm more interested in descriptive statistics of the sequence object itself - subsuming all the smaller sequences within.
For example, to get state frequencies, I run:
seqstatd(sequences.sts)
However, this gives me the state frequencies for each sequence within my sequence object. I want to access the frequencies of states across all sequences inside of my sequence object. How can I accomplish this?
I am not sure to understand your question since seqstatd() returns the cross-sectional frequencies at each successive position, and NOT the state frequencies for each sequence. The latter is returned by seqistatd().
Assuming you refer to the outcome of seqistatd() you would get the mean time spent in each state with seqmeant(sequence.sts).
For other summaries you can use the apply function. For instance, you get the variance of the time spent in each state with
tab <- seqistatd(mvad.seq)
vart <- apply(tab,2,var)
head(vart)
Hope this helps.