LCS {qualV}

Algorithm for the Longest Common Subsequence Problem

Description

Determines the longest common subsequence of two strings.

Usage

LCS(a, b)

Arguments

a

vector (numeric or character), missing values are not accepted

b

vector (numeric or character), missing values are not accepted

Details

A longest common subsequence (LCS) is a common subsequence of two strings of maximum length. The LCS Problem consists of finding a LCS of two given strings and its length (LLCS). A qualitative similarity index QSI is computed by division of the LLCS over maximum length of 'a' and 'b'.

Value

a

vector 'a'

b

vector 'b'

LLCS

length of LCS

LCS

longest common subsequence

QSI

quality similarity index

va

one possible LCS of vector 'a'

vb

one possible LCS of vector 'b'

Note

LCS is now using a C version of the algorithm provided by Dominik Reusser.

References

Wagner, R. A. and Fischer, M. J. (1974) The String-to-String Correction Problem. Journal of the ACM, 21, 168-173.

Paterson, M. and Dancik, V. (1994) Longest Common Subsequences. Mathematical Foundations of Computer Science, 841, 127-142.

Gusfield, D. (1997) Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology. Cambridge University Press, England, ISBN 0-521-58519-8.

Examples

# direct use
a <- c("b", "c", "a", "b", "c", "b")
b <- c("a", "b", "c", "c", "b")
print(LCS(a, b))

# a constructed example
x <- seq(0, 2 * pi, 0.1)  # time
y <- 5 + sin(x)           # a process
o <- y + rnorm(x, sd=0.2) # observation with random error
p <- y + 0.1              # simulation with systematic bias
plot(x, o); lines(x, p)

lcs <- LCS(f.slope(x, o), f.slope(x, p))  # too much noise
lcs$LLCS
lcs$QSI

os <- ksmooth(x, o, kernel = "normal", bandwidth = dpill(x, o), x.points = x)$y
lcs <- LCS(f.slope(x, os), f.slope(x, p))
lcs$LLCS
lcs$QSI


# observed and measured data with non-matching time intervals
data(phyto)
bbobs    <- dpill(obs$t, obs$y)
n        <- tail(obs$t, n = 1) - obs$t[1] + 1
obsdpill <- ksmooth(obs$t, obs$y, kernel = "normal", bandwidth = bbobs,
                    n.points = n)
obss     <- data.frame(t = obsdpill$x, y = obsdpill$y)
obss     <- obss[match(sim$t, obss$t),]
obs_f1   <- f.slope(obss$t, obss$y)
sim_f1   <- f.slope(sim$t, sim$y)
lcs      <- LCS(obs_f1, sim_f1)
lcs$QSI