seqdist: Distances (dissimilarities) between sequences
In TraMineR: Trajectory Miner: a Sequence Analysis Toolkit
seqdist R Documentation
Distances (dissimilarities) between sequences
Description
Computes pairwise dissimilarities between sequences or dissimilarity from
a reference sequence. Several dissimilarity measures can be chosen, including
optimal matching (OM) and many of its variants, distance based on the count
of common attributes, and distances between state distributions within sequences.
Usage
seqdist(seqdata, method, refseq = NULL, norm = "none", indel = "auto", sm = NULL,
with.missing = FALSE, full.matrix = TRUE, kweights = rep(1.0, ncol(seqdata)),
tpow = 1.0, expcost = 0.5, context, link = "mean", h = 0.5, nu,
transindel = "constant", otto, previous = FALSE, add.column = TRUE,
breaks = NULL, step = 1, overlap = FALSE, weighted = TRUE,
global.pdotj = NULL, prox = NULL, check.max.size=TRUE,
opt.args = list())
Arguments
seqdata
State sequence object of class stslist.
The sequence data to use.
Use seqdef to create such an object.
method
String.
The dissimilarity measure to use.
It can be "OM", "OMloc", "OMslen", "OMspell",
"OMstran", "HAM", "DHD", "CHI2", "EUCLID",
"LCS", "LCP", "RLCP", "NMS", "NMSMST",
"SVRspell", or "TWED". See the Details section.
refseq
NULL, Integer, State Sequence Object, or List.
Default: NULL.
The baseline sequence to compute the distances from.
When an integer, the index of a sequence in seqdata or 0 for the most frequent sequence.
When a state sequence object, it must contain a single sequence and have the same
alphabet as seqdata.
When a list, it must be a list of two sets of indexes of seqdata rows.
norm
String.
Default: "none".
The normalization to use when method is one of "OM",
"OMloc", "OMslen", "OMspell",
"OMstran", "TWED", "HAM", "DHD", "LCS",
"LCP", "RLCP", "CHI2", "EUCLID".
It can be "none", "auto", or, except for
"CHI2" and "EUCLID", "maxlength",
"gmean", "maxdist", or "YujianBo". "auto" is
equivalent to "maxlength" when method is one of "OM",
"HAM", or "DHD", to "gmean" when method is one
of "LCS", "LCP", or "RLCP", to YujianBo when
method is one of "OMloc", "OMslen", "OMspell",
"OMstran", "TWED". See the Details section.
indel
Double, Vector of Doubles, or String.
Default: "auto".
Insertion/deletion cost(s). Applies when method is one of "OM", "OMslen", "OMspell",
or "OMstran".
The single state-independent insertion/deletion cost when a double.
The state-dependent insertion/deletion costs when a vector of doubles.
The vector should contain an indel cost by state in the order of the alphabet.
When "auto", the indel is set as max(sm)/2 when sm is
a matrix and is computed by means of seqcost when sm is
a string specifying a cost method.
sm
NULL, Matrix, Array, or String. Substitution costs.
Default: NULL.
The substitution-cost matrix when a matrix and method is one of
"OM", "OMloc", "OMslen", "OMspell",
"OMstran", "HAM", or "TWED".
The series of the substitution-cost matrices when an array and
method = "DHD". They are grouped in a 3-dimensional array with the
third index referring to the position in the sequence.
One of the strings "CONSTANT", "INDELS", "INDELSLOG",
or "TRATE". Designates a seqcost method
to build sm. "CONSTANT" is not relevant for "DHD".
sm is mandatory when method is one of "OM",
"OMloc", "OMslen", "OMspell", "OMstran",
or "TWED".
sm is autogenerated when method is one of "HAM" or
"DHD" and sm = NULL. See the Details section.
Note: With method = "NMS" or method = "SVRspell", use
prox instead.
with.missing
Logical.
Default: FALSE.
Should the non-deleted missing value be added to the alphabet as an additional
state? If FALSE and seqdata or refseq contains such
gaps, an error is raised.
full.matrix
Logical.
Default: TRUE.
When refseq = NULL, if TRUE, the full distance matrix is
returned, if FALSE, an object of class dist is returned,
that is, a vector containing only values from the lower triangle of the
distance matrix. Objects of class dist are smaller and can be passed
directly as arguments to most clustering functions.
kweights
Double or vector of doubles.
Default: vector of 1s.
The weights applied to subsequences when method is one of "NMS",
"NMSMST", or "SVRspell". It contains at position k the
weight applied to the subsequences of length k. It must be positive.
Its length should be equal to the number of columns of seqdata. If shorter,
longer subsequences are ignored. If a scalar, it is transformed into
rep(kweights,ncol(sedata)).
tpow
Double.
Default: 1.0.
The exponential weight of spell length when method is one of
"OMspell", "NMSMST", or "SVRspell".
expcost
Double.
Default: 0.5.
The cost of spell length transformation when method = "OMloc" or
method = "OMspell". It must be positive. The exact interpretation is
distance-dependent.
context
Double.
Default: 1-2*expcost.
The cost of local insertion when method = "OMloc". It must be positive.
link
String.
Default: "mean".
The function used to compute substitution costs when method = "OMslen".
One of "mean" (arithmetic average) or "gmean" (geometric mean
as in the original proposition of Halpin 2010).
h
Double.
Default: 0.5.
It must be greater than or equal to 0.
The exponential weight of spell length when method = "OMslen".
The gap penalty when method = "TWED". It corresponds to the lambda
in Halpin (2014), p 88. It is usually chosen in the range [0,1]
nu
Double.
Stiffness when method = "TWED". It must be strictly greater than 0
and is usually less than 1.
See Halpin (2014), p 88.
transindel
String.
Default: "constant".
Method for computing transition indel costs when method = "OMstran".
One of "constant" (single indel of 1.0), "subcost" (based on
substitution costs), or "prob" (based on transition probabilities).
otto
Double.
The origin-transition trade-off weight when method = "OMstran". It
must be in [0, 1].
previous
Logical.
Default: FALSE.
When method = "OMstran", should we also account for the transition
from the previous state?
add.column
Logical.
Default: TRUE.
When method = "OMstran", should the last column (and also the first
column when previous = TRUE) be duplicated? When sequences have different
lengths, should the last (first) valid state be duplicated.
breaks
List of ordered pairs of integers.
Default: NULL.
The list of the possibly overlapping intervals when method = "CHI2"
or method = "EUCLID". Each interval is defined by the pair c(t1,t2) of the start t1 and end t2 positions of the interval.
step
Integer.
Default: 1.
The length of the intervals when method = "CHI2" or
method = "EUCLID" and breaks = NULL. It must be positive.
It must also be even when overlap = TRUE.
overlap
Logical.
Default: FALSE.
When method = "CHI2" or method = "EUCLID" and
breaks = NULL, should the intervals overlap?
weighted
Logical.
Default: TRUE.
When method is "CHI2" or when sm is a string (method),
should the distributions of the states account for the sequence weights
in seqdata? See seqdef.
global.pdotj
Numerical vector, "obs", or NULL.
Default: NULL.
Only for method = "CHI2".
The vector of state proportions to be used as marginal distribution. When NULL, the state distribution on the corresponding interval is used. When "obs", the overall state distribution in seqdata is used for all intervals. When a vector of proportions, it is used as marginal distribution for all intervals.
prox
NULL or Matrix.
Default: NULL.
The matrix of state proximities when method = "NMS" or
method = "SVRspell".
check.max.size
Logical. Should seqdist stop when maximum allowed number of unique sequences is exceeded? Caution, setting FALSE may produce unexpected results or even crash R.
opt.args
List. List of additional non-documented arguments for development usage.
Details
The seqdist function returns a matrix of distances between sequences
or a vector of distances from the reference sequence when refseq is set.
The available metrics (see method option) include:
-
Edit distances: optimal matching ("OM"), localized OM
("OMloc"), spell-length-sensitive OM ("OMslen"), OM of spell
sequences ("OMspell"), OM of transition sequences ("OMstran"),
Hamming ("HAM"), dynamic Hamming ("DHD"), and the time warp edit
distance ("TWED").
-
Metrics based on counts of common attributes: distance based on
the longest common subsequence ("LCS"), on the longest common prefix
("LCP"), on the longest common suffix ("RLCP"), on the number
of matching subsequences ("NMS"), on the number of matching
subsequences weighted by the minimum shared time ("NMSMST") and,
the subsequence vectorial representation distance ("SVRspell").
-
Distances between state distributions: Euclidean ("EUCLID"),
Chi-squared ("CHI2").
See Studer and Ritschard (2014, 2016) for a description and the comparison
of the above dissimilarity measures except "TWED" for which we refer to
Marteau (2009) and Halpin (2014).
Each method can be controlled with the following parameters:
method parameters
------------------ ---------------------------------
OM sm, indel, norm
OMloc sm, expcost, context, norm
OMslen sm, indel, link, h, norm
OMspell sm, indel, norm, tpow, expcost, norm
OMstran sm, indel, transindel, otto, previous, add.column, norm
HAM, DHD sm, norm
CHI2 breaks, step, overlap, norm, weighted, global.pdotj, norm
EUCLID breaks, step, overlap, norm
LCS, LCP, RLCP norm
NMS prox, kweights
NMSMST kweights, tpow
SVRspell prox, kweights, tpow
TWED sm, (indel), h, nu, norm
------------------ ---------------------------------
"LCS" is "OM" with a substitution cost of 2 (sm = "CONSTANT",
cval = 2) and an indel of 1.0. "HAM" is "OM" without
indels. "DHD" is "HAM" with specific substitution costs at each
position.
"HAM" and "DHD" apply only to sequences of equal length.
For "TWED", the (single) indel serves only for empty sequences.
The distance to an empty sequence is set as n*indel, where n is
the length of the non empty sequence. By default (indel="auto"), indel is set
as 2 * max(sm) + nu + h.
When sm = NULL, the substitution-cost matrix is automatically created
for "HAM" with a single substitution cost of 1 and for "DHD" with
the costs derived from the transition rates at the successive positions, i.e. with
sm = "TRATE".
Some distances can optionally be normalized by means of the norm argument.
Let d be the distance, m the maximum possible of the distance
given the lengths p and q of the two sequences, and k the
length of the longer sequence. Normalization "maxlength" is d/k
(Abbott's normalization), "gmean" is 1-(m-d)/(p*q)^.5 (Elzinga's
normalization), "maxdist" is d/m, and "YujianBo" is 2*d/(m+d).
For more details, see Gabadinho et al. (2009, 2011).
Actually, to avoid negative outcomes, the length p, q, and k are
set as (max) indel times the corresponding length. For some distances, m is
only a possibly non-reachable upper bound.
When norm="auto", "gmean" is applied to "LCS",
"LCP" and "RLCP" distances, "maxlength" is applied to "OM", "HAM"
and "DHD", and the normalization "YujianBo" of Yujian and Bo (2007) that preserves the
triangle inequality is used in the other cases except "CHI2" and "EUCLID".
For the latter two, the square of the
distances are normalized by the number of intervals and the maximal distance
on each interval. Note that for 'CHI2' the maximal distance on each interval
depends on the state distribution on the interval.
When sequences contain gaps and the left = NA, gaps = NA, or right = NA
option was passed to
seqdef (i.e. when there are non deleted missing values), the
with.missing argument should be set as TRUE. If left as
FALSE the function stops when it encounters a gap. This is to make the
user aware that there are gaps in the sequences. For methods that need an
sm value, seqdist expects a substitution-cost matrix with a row
and a column entry for the missing state (symbol defined with the nr
option of seqdef). Substitution-cost matrices returned by
seqcost (and so seqsubm) include these additional
entries when the function is called with with.missing = TRUE. More
details on how to compute distances with sequences containing gaps can be
found in Gabadinho et al. (2009).
Value
When refseq is NULL (default), the whole matrix of pairwise
distances between sequences or, if full.matrix = FALSE,
the corresponding dist object of pairwise distances between sequences.
When refseq is a list of two sets of indexes, the matrix
of distances from the first set of sequences (rows) to the second set (columns).
Otherwise, a vector with distances from the sequences in the
state sequence object to the reference sequence specified with refseq.
Author(s)
Matthias Studer, Gilbert Ritschard, Pierre-Alexandre Fonta, Alexis Gabadinho, Nicolas S. Müller.
References
Studer, M. and G. Ritschard (2016), "What matters in differences between life
trajectories: A comparative review of sequence dissimilarity measures",
Journal of the Royal Statistical Society, Series A. 179(2),
481-511, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssa.12125")}
Studer, M. and G. Ritschard (2014). "A Comparative Review of Sequence
Dissimilarity Measures". LIVES Working Papers, 33. NCCR LIVES,
Switzerland, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.12682/lives.2296-1658.2014.33")}
Gabadinho, A., G. Ritschard, N. S. Müller and M. Studer (2011). Analyzing and
Visualizing State Sequences in R with TraMineR. Journal of Statistical
Software 40(4), 1–37.
Gabadinho, A., G. Ritschard, M. Studer and N. S. Müller (2009). Mining
Sequence Data in R with the TraMineR package: A user's guide.
Department of Econometrics and Laboratory of Demography, University of Geneva
Halpin, B. (2014). Three Narratives of Sequence Analysis, in Blanchard, P.,
Bühlmann, F. and Gauthier, J.-A. (Eds.) Advances in Sequence Analysis:
Theory, Method, Applications, Vol 2 of Series Life Course Research and
Social Policies, pages 75–103, Heidelberg: Springer. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-3-319-04969-4_5")}
Marteau, P.-F. (2009). Time Warp Edit Distances with Stiffness Adjustment for
Time Series Matching. IEEE Transactions on Pattern Analysis and Machine
Intelligence, 31(2), 306–318. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/TPAMI.2008.76")}
Yujian, L. and Bo, L. (2007). A normalized Levenshtein distance metric.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
29(6), 1091–1095. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/TPAMI.2007.1078")}
See also all references in Studer and Ritschard (2014, 2016)
See Also
seqcost, seqsubm, seqdef, and seqMD for
multidomain (multichannel) distances using the cost additive trick.
Examples
## =========================
## Examples without missings
## =========================
## Defining a sequence object with columns 10 to 25
## of a subset of the 'biofam' data set
data(biofam)
biofam.seq <- seqdef(biofam[501:600, 10:25])
## OM distances using the vector of indels and substitution
## costs derived from the estimated state frequencies
costs <- seqcost(biofam.seq, method = "INDELSLOG")
biofam.om <- seqdist(biofam.seq, method = "OM",
indel = costs$indel, sm = costs$sm)
## OM between sequences of transitions
biofam.omstran <- seqdist(biofam.seq, method = "OMstran",
indel = costs$indel, sm = costs$sm,
otto=.3, transindel="subcost")
## Normalized LCP distances
biofam.lcp.n <- seqdist(biofam.seq, method = "LCP",
norm = "auto")
## Normalized LCS distances to the most frequent sequence
biofam.dref1 <- seqdist(biofam.seq, method = "LCS",
refseq = 0, norm = "auto")
## LCS distances to an external sequence
ref <- seqdef(as.matrix("(0,5)-(3,5)-(4,6)"), informat = "SPS",
alphabet = alphabet(biofam.seq))
biofam.dref2 <- seqdist(biofam.seq, method = "LCS",
refseq = ref)
## LCS distances between two subsets of sequences
set1 <- 1:10
set2 <- 31:36
biofam.dref2 <- seqdist(biofam.seq, method = "LCS",
refseq = list(set1,set2))
## Chi-squared distance over the full observed timeframe
biofam.chi.full <- seqdist(biofam.seq, method = "CHI2",
step = max(seqlength(biofam.seq)))
## Chi-squared distance over successive overlapping
## intervals of length 4
biofam.chi.ostep <- seqdist(biofam.seq, method = "CHI2",
step = 4, overlap = TRUE)
## ======================
## Examples with missings
## ======================
data(ex1)
## Ignore empty row 7
ex1.seq <- seqdef(ex1[1:6, 1:13])
## OM with indel and substitution costs based on
## log of inverse state frequencies
costs.ex1 <- seqcost(ex1.seq, method = "INDELSLOG",
with.missing = TRUE)
ex1.om <- seqdist(ex1.seq, method = "OM",
indel = costs.ex1$indel, sm = costs.ex1$sm,
with.missing = TRUE)
## Localized OM
ex1.omloc <- seqdist(ex1.seq, method = "OMloc",
sm = costs.ex1$sm, expcost=.1, context = .4,
with.missing = TRUE)
## OMspell with a scalar indel
indel <- max(costs.ex1$indel)
## OM of spells
ex1.omspell <- seqdist(ex1.seq, method = "OMspell",
indel = indel, sm = costs.ex1$sm,
with.missing = TRUE)
## Distance based on number of matching subsequences
ex1.nms <- seqdist(ex1.seq, method = "NMS",
with.missing = TRUE)
## Using the sequence vectorial representation metric
costs.fut <- seqcost(ex1.seq, method = "FUTURE", lag = 4,
proximities = TRUE, with.missing = TRUE)
ex1.svr <- seqdist(ex1.seq, method = "SVRspell",
prox = costs.fut$prox, with.missing = TRUE)
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| seqdist | R Documentation |
Distances (dissimilarities) between sequences
Description
Computes pairwise dissimilarities between sequences or dissimilarity from a reference sequence. Several dissimilarity measures can be chosen, including optimal matching (OM) and many of its variants, distance based on the count of common attributes, and distances between state distributions within sequences.
Usage
seqdist(seqdata, method, refseq = NULL, norm = "none", indel = "auto", sm = NULL,
with.missing = FALSE, full.matrix = TRUE, kweights = rep(1.0, ncol(seqdata)),
tpow = 1.0, expcost = 0.5, context, link = "mean", h = 0.5, nu,
transindel = "constant", otto, previous = FALSE, add.column = TRUE,
breaks = NULL, step = 1, overlap = FALSE, weighted = TRUE,
global.pdotj = NULL, prox = NULL, check.max.size=TRUE,
opt.args = list())
Arguments
seqdata |
State sequence object of class |
method |
String.
The dissimilarity measure to use.
It can be |
refseq |
When an integer, the index of a sequence in When a state sequence object, it must contain a single sequence and have the same
alphabet as When a list, it must be a list of two sets of indexes of |
norm |
String.
Default: |
indel |
Double, Vector of Doubles, or String.
Default: The single state-independent insertion/deletion cost when a double. The state-dependent insertion/deletion costs when a vector of doubles. The vector should contain an indel cost by state in the order of the alphabet. When |
sm |
The substitution-cost matrix when a matrix and The series of the substitution-cost matrices when an array and
One of the strings
Note: With |
with.missing |
Logical.
Default: |
full.matrix |
Logical.
Default: |
kweights |
Double or vector of doubles.
Default: vector of |
tpow |
Double.
Default: |
expcost |
Double.
Default: |
context |
Double.
Default: |
link |
String.
Default: |
h |
Double.
Default: The exponential weight of spell length when The gap penalty when |
nu |
Double.
Stiffness when |
transindel |
String.
Default: |
otto |
Double.
The origin-transition trade-off weight when |
previous |
Logical.
Default: |
add.column |
Logical.
Default: |
breaks |
List of ordered pairs of integers.
Default: |
step |
Integer.
Default: |
overlap |
Logical.
Default: |
weighted |
Logical.
Default: |
global.pdotj |
Numerical vector, |
prox |
|
check.max.size |
Logical. Should |
opt.args |
List. List of additional non-documented arguments for development usage. |
Details
The seqdist function returns a matrix of distances between sequences
or a vector of distances from the reference sequence when refseq is set.
The available metrics (see method option) include:
-
Edit distances: optimal matching (
"OM"), localized OM ("OMloc"), spell-length-sensitive OM ("OMslen"), OM of spell sequences ("OMspell"), OM of transition sequences ("OMstran"), Hamming ("HAM"), dynamic Hamming ("DHD"), and the time warp edit distance ("TWED"). -
Metrics based on counts of common attributes: distance based on the longest common subsequence (
"LCS"), on the longest common prefix ("LCP"), on the longest common suffix ("RLCP"), on the number of matching subsequences ("NMS"), on the number of matching subsequences weighted by the minimum shared time ("NMSMST") and, the subsequence vectorial representation distance ("SVRspell"). -
Distances between state distributions: Euclidean (
"EUCLID"), Chi-squared ("CHI2").
See Studer and Ritschard (2014, 2016) for a description and the comparison
of the above dissimilarity measures except "TWED" for which we refer to
Marteau (2009) and Halpin (2014).
Each method can be controlled with the following parameters:
| method | parameters |
| ------------------ | --------------------------------- |
OM | sm, indel, norm |
OMloc | sm, expcost, context, norm |
OMslen | sm, indel, link, h, norm |
OMspell | sm, indel, norm, tpow, expcost, norm |
OMstran | sm, indel, transindel, otto, previous, add.column, norm |
HAM, DHD | sm, norm |
CHI2 | breaks, step, overlap, norm, weighted, global.pdotj, norm |
EUCLID | breaks, step, overlap, norm |
LCS, LCP, RLCP | norm |
NMS | prox, kweights |
NMSMST | kweights, tpow |
SVRspell | prox, kweights, tpow |
TWED | sm, (indel), h, nu, norm |
| ------------------ | --------------------------------- |
"LCS" is "OM" with a substitution cost of 2 (sm = "CONSTANT",
cval = 2) and an indel of 1.0. "HAM" is "OM" without
indels. "DHD" is "HAM" with specific substitution costs at each
position.
"HAM" and "DHD" apply only to sequences of equal length.
For "TWED", the (single) indel serves only for empty sequences.
The distance to an empty sequence is set as n*indel, where n is
the length of the non empty sequence. By default (indel="auto"), indel is set
as 2 * max(sm) + nu + h.
When sm = NULL, the substitution-cost matrix is automatically created
for "HAM" with a single substitution cost of 1 and for "DHD" with
the costs derived from the transition rates at the successive positions, i.e. with
sm = "TRATE".
Some distances can optionally be normalized by means of the norm argument.
Let d be the distance, m the maximum possible of the distance
given the lengths p and q of the two sequences, and k the
length of the longer sequence. Normalization "maxlength" is d/k
(Abbott's normalization), "gmean" is 1-(m-d)/(p*q)^.5 (Elzinga's
normalization), "maxdist" is d/m, and "YujianBo" is 2*d/(m+d).
For more details, see Gabadinho et al. (2009, 2011).
Actually, to avoid negative outcomes, the length p, q, and k are
set as (max) indel times the corresponding length. For some distances, m is
only a possibly non-reachable upper bound.
When norm="auto", "gmean" is applied to "LCS",
"LCP" and "RLCP" distances, "maxlength" is applied to "OM", "HAM"
and "DHD", and the normalization "YujianBo" of Yujian and Bo (2007) that preserves the
triangle inequality is used in the other cases except "CHI2" and "EUCLID".
For the latter two, the square of the
distances are normalized by the number of intervals and the maximal distance
on each interval. Note that for 'CHI2' the maximal distance on each interval
depends on the state distribution on the interval.
When sequences contain gaps and the left = NA, gaps = NA, or right = NA
option was passed to
seqdef (i.e. when there are non deleted missing values), the
with.missing argument should be set as TRUE. If left as
FALSE the function stops when it encounters a gap. This is to make the
user aware that there are gaps in the sequences. For methods that need an
sm value, seqdist expects a substitution-cost matrix with a row
and a column entry for the missing state (symbol defined with the nr
option of seqdef). Substitution-cost matrices returned by
seqcost (and so seqsubm) include these additional
entries when the function is called with with.missing = TRUE. More
details on how to compute distances with sequences containing gaps can be
found in Gabadinho et al. (2009).
Value
When refseq is NULL (default), the whole matrix of pairwise
distances between sequences or, if full.matrix = FALSE,
the corresponding dist object of pairwise distances between sequences.
When refseq is a list of two sets of indexes, the matrix
of distances from the first set of sequences (rows) to the second set (columns).
Otherwise, a vector with distances from the sequences in the
state sequence object to the reference sequence specified with refseq.
Author(s)
Matthias Studer, Gilbert Ritschard, Pierre-Alexandre Fonta, Alexis Gabadinho, Nicolas S. Müller.
References
Studer, M. and G. Ritschard (2016), "What matters in differences between life trajectories: A comparative review of sequence dissimilarity measures", Journal of the Royal Statistical Society, Series A. 179(2), 481-511, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssa.12125")}
Studer, M. and G. Ritschard (2014). "A Comparative Review of Sequence Dissimilarity Measures". LIVES Working Papers, 33. NCCR LIVES, Switzerland, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.12682/lives.2296-1658.2014.33")}
Gabadinho, A., G. Ritschard, N. S. Müller and M. Studer (2011). Analyzing and Visualizing State Sequences in R with TraMineR. Journal of Statistical Software 40(4), 1–37.
Gabadinho, A., G. Ritschard, M. Studer and N. S. Müller (2009). Mining
Sequence Data in R with the TraMineR package: A user's guide.
Department of Econometrics and Laboratory of Demography, University of Geneva
Halpin, B. (2014). Three Narratives of Sequence Analysis, in Blanchard, P., Bühlmann, F. and Gauthier, J.-A. (Eds.) Advances in Sequence Analysis: Theory, Method, Applications, Vol 2 of Series Life Course Research and Social Policies, pages 75–103, Heidelberg: Springer. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-3-319-04969-4_5")}
Marteau, P.-F. (2009). Time Warp Edit Distances with Stiffness Adjustment for Time Series Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2), 306–318. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/TPAMI.2008.76")}
Yujian, L. and Bo, L. (2007). A normalized Levenshtein distance metric. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 1091–1095. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/TPAMI.2007.1078")}
See also all references in Studer and Ritschard (2014, 2016)
See Also
seqcost, seqsubm, seqdef, and seqMD for
multidomain (multichannel) distances using the cost additive trick.
Examples
## =========================
## Examples without missings
## =========================
## Defining a sequence object with columns 10 to 25
## of a subset of the 'biofam' data set
data(biofam)
biofam.seq <- seqdef(biofam[501:600, 10:25])
## OM distances using the vector of indels and substitution
## costs derived from the estimated state frequencies
costs <- seqcost(biofam.seq, method = "INDELSLOG")
biofam.om <- seqdist(biofam.seq, method = "OM",
indel = costs$indel, sm = costs$sm)
## OM between sequences of transitions
biofam.omstran <- seqdist(biofam.seq, method = "OMstran",
indel = costs$indel, sm = costs$sm,
otto=.3, transindel="subcost")
## Normalized LCP distances
biofam.lcp.n <- seqdist(biofam.seq, method = "LCP",
norm = "auto")
## Normalized LCS distances to the most frequent sequence
biofam.dref1 <- seqdist(biofam.seq, method = "LCS",
refseq = 0, norm = "auto")
## LCS distances to an external sequence
ref <- seqdef(as.matrix("(0,5)-(3,5)-(4,6)"), informat = "SPS",
alphabet = alphabet(biofam.seq))
biofam.dref2 <- seqdist(biofam.seq, method = "LCS",
refseq = ref)
## LCS distances between two subsets of sequences
set1 <- 1:10
set2 <- 31:36
biofam.dref2 <- seqdist(biofam.seq, method = "LCS",
refseq = list(set1,set2))
## Chi-squared distance over the full observed timeframe
biofam.chi.full <- seqdist(biofam.seq, method = "CHI2",
step = max(seqlength(biofam.seq)))
## Chi-squared distance over successive overlapping
## intervals of length 4
biofam.chi.ostep <- seqdist(biofam.seq, method = "CHI2",
step = 4, overlap = TRUE)
## ======================
## Examples with missings
## ======================
data(ex1)
## Ignore empty row 7
ex1.seq <- seqdef(ex1[1:6, 1:13])
## OM with indel and substitution costs based on
## log of inverse state frequencies
costs.ex1 <- seqcost(ex1.seq, method = "INDELSLOG",
with.missing = TRUE)
ex1.om <- seqdist(ex1.seq, method = "OM",
indel = costs.ex1$indel, sm = costs.ex1$sm,
with.missing = TRUE)
## Localized OM
ex1.omloc <- seqdist(ex1.seq, method = "OMloc",
sm = costs.ex1$sm, expcost=.1, context = .4,
with.missing = TRUE)
## OMspell with a scalar indel
indel <- max(costs.ex1$indel)
## OM of spells
ex1.omspell <- seqdist(ex1.seq, method = "OMspell",
indel = indel, sm = costs.ex1$sm,
with.missing = TRUE)
## Distance based on number of matching subsequences
ex1.nms <- seqdist(ex1.seq, method = "NMS",
with.missing = TRUE)
## Using the sequence vectorial representation metric
costs.fut <- seqcost(ex1.seq, method = "FUTURE", lag = 4,
proximities = TRUE, with.missing = TRUE)
ex1.svr <- seqdist(ex1.seq, method = "SVRspell",
prox = costs.fut$prox, with.missing = TRUE)
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