This function finds an interval in the sequence where their underlying distribution differs from the rest of the sequence.
gseg2(n, E, l0=0.05*n, l1=0.95*n, pval.appr=TRUE, skew.corr=TRUE, pval.perm=FALSE, B=100)The number of observations in the sequence.
The edge matrix (a "number of edges" by 2 matrix) for the similarity graph. Each row contains the node indices of an edge.
The minimum length of the interval to be considered as a changed interval.
The maximum length of the interval to be considered as a changed interval.
If it is TRUE, the function outputs p-value approximation based on asymptotic properties.
This argument is useful only when pval.appr=TURE. If skew.corr is TRUE, the p-value approximation would incorporate skewness correction.
If it is TRUE, the function outputs p-value from doing B permutations, where B is another argument that you can specify. Use this argument with caution that it may take long time to finish the permutation.
This argument is useful only when pval.perm=TRUE. The default value for B is 100.
An estimate of the two ends of the changed interval.
The test statistic (maximum of the scan statistics).
A matrix of scan statistics (standardized counts).
A matrix of raw counts.
This output exists only when the argument pval.appr is TRUE. The approximated p-value.
This output exists only when the argument pval.perm is TRUE (same for perm.curve, perm.maxZs, and perm.Z). p-value calculated from B permutations.
A B by 2 matrix with the first column being critical values corresponding to the p-values in the second column.
A sorted vector recording the test statistics in the B permutaitons.
A B by n-squared matrix with each row being the vectorized scan statistics from each permutaiton run.
# NOT RUN {
data(Example) # this example data has: n (the number of observations) and E (an edge matrix)
r2 = gseg2(n,E)
# }
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