| Title: | Detect and Test Regular Sequences and Subsequences |
|---|---|
| Description: | For a sequence of event occurence times, we are interested in finding subsequences in it that are too "regular". We define regular as being significantly different from a homogeneous Poisson process. The departure from the Poisson process is measured using a L1 distance. See Di and Perlman 2007 for more details. |
| Authors: | Yanming Di |
| Maintainer: | Yanming Di<[email protected]> |
| License: | GPL-2 |
| Version: | 0.12 |
| Built: | 2025-02-18 03:39:40 UTC |
| Source: | https://github.com/cran/regsubseq |
The data set provide quantile tables for the linearity/gap-linearity test statistics for N=2, ..., 50 and k= 2, ..., N, for each N. These tables will be used to compute p-values corresponding to test statistics.
qtablesqtables
R rda files. Within each quantile table, the first row indicates at which probability values the quantiles are computed.
test.gaplin.t find the most almost gap-linear length k+1 subsequence
of a given sequence and compute the almost gap-linearity test statistic for
this subsequence. test.gaplin.p compute the p-value corresponding to
a computed test statistic. test.gaplin compute the test statistics
and the p-values for subsequences of all lengths.
test.gaplin(Tn); test.gaplin.t(Tn, k); test.gaplin.p(t, n, k);test.gaplin(Tn); test.gaplin.t(Tn, k); test.gaplin.p(t, n, k);
Tn |
A sequence of numbers. Currently, only support sequence of length less than 50. |
k |
The length of the subsequences for which we want to test for almost gap-linearity. |
n |
The length of the sequence for which we want to test for subsequence almost gap-linearity. |
t |
Test statistic computed for a length k+1 subsequence of a length n+1 sequence. |
Almost gap-linear means the spacings of a subsequence are almost in proportion to the spacings of the corresponding indicies. For example, for Tn=c(11, 14, (.), 20), the subs sequence (11, 14, 20) is gap-linear, since the spacings (3, 6) is in proportion with the spacings of hte corresponding indicies (1, 2). Equivalently, almost gap-linearity can measured by the distance between the standardized spacings of the subsequnce and the standardized spacings of the corresponding indicies. See Di and Perlman (2007) for more details.
test.gaplin.t returns the most gap-linear length k+1 subsequence of the
input sequence and corresponding almost gap-linearity test statistic.
test.gaplin.p returns the p-value corresponding to the input test
statistic t. test.lin has no return value, instead, a table
containing the most almost gap-linear subsequences, corresponding test
staistics and p-values will be outputed.
Yanming Di
Di and Perlman, 2007
## A sequence representing arrival times of events. Tn = c(13, 21, 24, 33, 40, 55, 59, 63, 72, 85, 87); ## Test for almost linearity. t = test.gaplin.t(Tn, 4); print(t$sub); p = test.gaplin.p(t$t, 10, 4); print(p); test.gaplin(Tn);## A sequence representing arrival times of events. Tn = c(13, 21, 24, 33, 40, 55, 59, 63, 72, 85, 87); ## Test for almost linearity. t = test.gaplin.t(Tn, 4); print(t$sub); p = test.gaplin.p(t$t, 10, 4); print(p); test.gaplin(Tn);
test.lin.t find the most almost-linear length k+1 subsequence
of a given sequence and compute the almost-linearity test statistic for
this subsequence. test.lin.p compute the p-value corresponding to
a computed test statistic. test.lin compute the test statistics
and the p-values for subsequences of all lengths.
test.lin(Tn); test.lin.t(Tn, k); test.lin.p(t, n, k);test.lin(Tn); test.lin.t(Tn, k); test.lin.p(t, n, k);
Tn |
A sequence of numbers. Currently, only support sequences of length less than 50. |
k |
The length of the subsequences for which we want to test for almost-linearity. |
n |
The length of the sequence for which we want to test for subsequence almost-linearity. |
t |
Test statistic computed for a length k+1 subsequence of a length n+1 sequence. |
Almost-linear means the spacings of the sequence are almost equal, or the distance between the standardized spacings as a vector and (1/k, ..., 1/k) is too small. The p-value is computed by comparing the test statistic to a procomputed test statistic quantile table. See Di and Perlman (2007) for more details.
test.lin.t returns the most linear length k+1 subsequence of
the input sequence and corresponding almost-linearity test
statistic. test.lin.p returns the p-value corresponding to the
input test statistic t. test.lin has no return value,
instead, a table containing the most almost linear subsequences,
corresponding test staistics and p-values will be outputed.
Yanming Di
Di and Perlman, 2007
## A sequence representing arrival times of events. Tn = c(13, 21, 24, 33, 40, 55, 59, 63, 72, 85, 87); ## Test for almost linearity. t = test.lin.t(Tn, 4); print(t$sub); p = test.lin.p(t$t, 10, 4); print(p); test.lin(Tn);## A sequence representing arrival times of events. Tn = c(13, 21, 24, 33, 40, 55, 59, 63, 72, 85, 87); ## Test for almost linearity. t = test.lin.t(Tn, 4); print(t$sub); p = test.lin.p(t$t, 10, 4); print(p); test.lin(Tn);