{smcl}
{* 25march2006}{...}
{cmd:help sqom}{right:SJ6-4: st0111)}
{hline}
{title:Title}
{p2colset 5 11 13 2}{...}
{p2col :{hi:sqom} {hline 2} Optimal matching of sequences}{p_end}
{p2colreset}{...}
{title:Syntax}
{p 8 17 2}
{cmdab:sqom}
{ifin}
[{cmd:,} {it:options}]
{synoptset 38}{...}
{synopthdr}
{synoptline}
{synopt:{opt indel:cost(#)}}set indel costs to {it:#}{p_end}
{synopt:{cmdab:sub:cost(}{it:#}|{it:implied formula}|{it:matexp}|{it:matname}{cmd:)}}specify substitution costs{p_end}
{synopt:{opt name(varname)}}specify substitution costs{p_end}
{synopt:{opt ref:seqid(spec)}}select reference sequence{p_end}
{synopt:{opt full}}calculate full dissimilarity matrix between sequences{p_end}
{synopt:{opt k(#)}}restrict indels (to save calculation time){p_end}
{synopt:{cmdab:st:andard(}{it:#}|{cmd:cut}|{cmd:longer}|{cmd:longest}|{cmd:none)}}standardization of sequences of different length{p_end}
{synopt:{opt subseq:uence(a,b)}}use only subsequence between positions a and b{p_end}
{synopt:{opt ideal:type(spec)}}compare with a specified ideal typical sequence{p_end}
{synoptline}
{p2colreset}{...}
{title:Description}
{pstd} {cmd:sqom} performs optimal matching of sequences. The command
uses the Needleman-Wunsch algorithm to find the alignment between two
sequences that have the lowest Levenshtein distance. The Levenshtein
distances are then stored for further use.
{pstd}By default, all sequences are compared to the most frequent
sequence and the resulting distances are stored in a variable. It is,
however, possible to compare all sequences with a preselected
reference distance or to compare all sequences with every other
sequence. In the latter case, the resulting distances are stored in a
Stata variable.
{pstd}Comparing all sequences with any other sequence is computationally
intensive.
{title:Options}
{phang} {opt indelcost(#)} specifies the cost attached to an
insertion or deletion of an alignment. The default is {cmd:indelcost(1)}.
{phang}
{cmdab:sub:cost(}#|{it:implied formula}|{it:matexp}|{it:matname}}
specifies the cost attached to a substitution in an alignment. Substitution
costs may be specified as real number, as implied formula, or as full
matrix.
Specifying substitution cost as, for example,
{cmd:subcost(3)} will attach the cost of 3 to any substitution
necessary in an alignment, regardless of how similar the
substituted values may be. The default is two times the value specified as indel cost.
A full substitution cost matrix can be specified either by specifying
the name of a matrix containing the substitution cost or by typing
valid matrix syntax into the option itself. The matrix has to be a
symmetric n*n matrix, where n is the number of different
elements in all sequences.
{p 8 8 2}{it:implied formula} generates substitution costs based on the data. The implied formula is specified with a keyword. The following keywords are allowed
{synoptset 20}{...}
{it: implied formula}
{synoptline}
{synopt:{opt rawdistance}}use the
absolute value of the difference between the numeric values representing the respective elements{p_end}
{synopt:{opt meanprobdistance}}calculates symmetric substitiution cost matrix based on the mean of the transitions' probabilities {it:(p)} in the data
between every two neighboring elements in the sequences. The substitution costs between elements x and y are defined by: SC(x,y) = SC(y,x) = 2-p(x,y)-p(y,x) if x is not equal to y, otherwise 0.{p_end}
{synopt:{opt minprobdistance}}calculates symmetric substitiution cost matrix based on the transitions' probabilities {it:(p)} in the data
between every two neighboring elements in sequences. The substitution cost matrix contains the minimal substitution costs for
each pair of symmetric transitions: SC(x,y) = SC(y,x) = min(1-p(x,y),1-p(y,x))*2 if x is not equal to y, otherwise 0.{p_end}
{synopt:{opt maxprobdistance}}calculates symmetric substitiution cost matrix based on the transitions' probabilities {it:(p)} in the data
between every two neighboring elements in sequences. The substitution cost matrix contains the maximal substitution costs for
each pair of symmetric transitions: SC(x,y) = SC(y,x) = max(1-p(x,y),1-p(y,x))*2 if x is not equal to y, otherwise 0.{p_end}
{synoptline}
{p2colreset}{...}
{p 8 8 2}The substitution costs in last three cases have values between 0 and 2
{p 8 8 2}Specifying a full substitution cost matrix or generating a data based substitution
cost matrix can increase the running time of the program considerably.
Option {cmd:k()} might be considered for {cmd:sqom} with full
substitution cost matrix.
{phang} {opt name(varname)} is used to specify the name of the
variable in which the distances are stored. If not specified,
{cmd:_SQdist} is used. The automatically generated distance
variable will get overwritten without warning whenever a {cmd:sqom}
command without {cmd:name()} is invoked.
{phang} {opt refseqid(spec)} is used to select the
reference sequence against which all sequences in the dataset are
being tested. Within the parentheses, an existing value of the sequence
identifier has to be stated.
{phang} {cmd:full} is used to perform optimal matching for all
sequences in the dataset against any other. The results of these
comparisons are stored in the distance matrix "SQdist". Specifying
the option {cmd:full} will increase the running time of the program
considerably. Option {cmd:k()} might be used for {cmd:sqom} with
{cmd:full}.
{pmore}Two companion programs, {help sqclusterdat} and {help sqclustermat},
help to further analyze the distance matrix produced with {cmd: sqom, full}.
{phang}{opt k(#)} is used to speed up the calculation of the
optimal matching algorithm. Within the parentheses, an integer
positive number between 1 and the number of positions of the longest
sequence can be given. The speed up will be higher with small numbers.
Very small numbers can have the effect that the algorithm doesn't find
the best alignment between some sequences, and this problem tends to
increase if substitution costs are high relative to indel
costs.
{p 8 8 2}Note: The implementation of the {cmd:k()} is based partly on the
source code of TDA, written by Goetz Rohwer and Ulrich Poetter. TDA is
a very powerful program for transitory data analysis. It is programmed
in C and distributed as freeware under the terms of the General
Public License. It is downloadable from
{browse "http://www.stat.ruhr-uni-bochum.de/tda.html"}.
{phang}
{cmdab:st:andard(}#|{cmd:cut}|{cmd:longer}|{cmd:longest}|{cmd:none)}
is used to define the standardization of the resulting distances. With
{cmd:standard(#)} all sequences are cut to the length {it:#}. The keyword
{cmd:cut} automatically cuts all sequences to the length of the
shortest sequence in the dataset. {cmd:standard(longer)} divides all
distances by the length of the longer sequence of the respective
alignment. {cmd:standard(longest)} divides all distances by the length
of the longest sequence in the dataset; this is the
default. {cmd:none} is specified if no standardization is needed.
{phang}{cmd:subsequence(a,b)} is used to include only the part of
the sequence that is between position a and b, whereby a and b refer
to the position defined in the order variable. {p_end}
{phang}{cmd:idealtype(spec)} allows to specify an ideal typical
sequence against which all sequences are compared. To specify the
sequence use element[:repetitions] [element:repetitions]. For example,
with {cmd: idealtype(3:20 5 1:20 3:20)} you specifiy an ideal typical
sequence of length 61. The ideal typical sequence starts with element
3 over 20 positions, followed by one position of elment 5, 20 positions
of element 1 and finally again 20 positions of element 3. {p_end}
{title:Authors}
{pstd}Ulrich Kohler, WZB, kohler@wz-berlin.de{p_end}
{pstd}Magdalena Luniak, WZB, luniak@wz-berlin.de{p_end}
{title:Examples}
{phang}{cmd:. sqom, name(mydist)}{p_end}
{phang}{cmd:. sqindexplot, order(mydist)}{p_end}
{phang}{cmd:. sqom, full k(2)}{p_end}
{phang}{cmd:. sqclustermat ward, name(mydist2)}{p_end}
{phang}{cmd:. sqindexplot, order(mydist2)}{p_end}
{title:Also see}
{psee} Online: {helpb sq},
{helpb sqdemo}, {helpb sqset}, {helpb sqdes}, {helpb sqegen}, {helpb sqstat},
{helpb sqindexplot}, {helpb sqparcoord}, {helpb sqom},
{helpb sqclusterdat}, {helpb sqclustermat} {p_end}