GrpString (version 0.3.2)

StrDif: Statistically compares the difference between two groups of strings

Description

StrDif tests whether the difference between two groups of strings is statistically significant or not. The difference is based on normalized Levenshtein distances (LDs) between strings. A permutation test is used as the statistical method.

Usage

StrDif(grp1_string, grp2_string, num_perm = 1000,
       o.x = 0.01, o.y = 0, p.x = 0.015, p.y = 0)

Arguments

grp1_string

String group (vector) 1.

grp2_string

String group (vector) 2.

num_perm

Number of permutations. The default is 1000.

o.x

x coordinate of the legend in the histogram, default is 0.01.

o.y

y coordinate of the legend in the histogram, default is 0.

p.x

x coordinate of the p value in the histogram, default is 0.015.

p.y

y coordinate of the legend in the histogram, default is 0.

Value

The function generates a histogram that demonstrates the distribution of the differences of LDs, the original difference, and the p value.

The function also returns a vector containing differences of normalized LDs. The total number of differences is num_perm (number of permutations).

Differences are calculated by subtracting within-group LD from between-group LD. They range from -1 to 1. The "observed" difference is the difference from the original data set.

Details

The default values of o.y and p.y are 0. They are actually related to num_perm: o.y is above 0.2 * num_perm, and p.y is below 0.2 * num_perm. If non-default values are used, the values become absolute y coordinates.

References

1. H. Tang; J. J. Topczewski; A. M. Topczewski; N. J. Pienta. Permutation Test for Groups of Scanpaths Using Normalized Levenshtein Distances and Application in NMR Questions. In Proceedings of the Symposium on Eye Tracking Research and Applications, Santa Barbara, CA, March 28-30, 2012; ACM Press: New York; pp 169-172.

2. M. Feusner; B. Lukoff. (2008). Testing for statistically significant differences between groups of scan patterns. In Proceedings of the Symposium on Eye-tracking Research & Applications, ACM Press, New York, 43-46.

See Also

HistDif

Examples

# NOT RUN {
# simple stings, non-default permutation number and p-value position
strs1.vec <- c("ABCDdefABCDa", "def123DC", "123aABCD", "ACD13", "AC1ABC", "3123fe")
strs2.vec <- c("xYZdkfAxDa", "ef1563xy", "BC9Dzy35X", "AkeC1fxz", "65CyAdC", "Dfy3f69k")
ld.dif.vec <- StrDif(strs1.vec, strs2.vec, num_perm = 500, p.x = 0.025)

# longer strings
data(str1)
data(str2)
s1 <- str1[1:6]
s2 <- str2[1:6]
ld.dif12.vec <- StrDif(s1, s2, num_perm = 500)
# }