Description Usage Arguments Details Value Author(s) References See Also Examples
Determine the significance of spatial dependence at different scales using pointwise variogram envelope based on permutation test.
1 |
envlist |
output from |
index |
integer. Indicating the index which the permutation test for spatial dependence is performed up to. For example, |
method |
string. One of p-value combination methods. |
The default and preferred method for computing overall p-value is "eb" (empirical Brown's method), which has good power and close to nominal type I error rate. "fisher" (Fisher's method assumes independent pointwise p-values and requires higher sample size to achieve good power. "min" has the highest power but also much higher type I error rate.
A list contains:
p.pointwise |
Adjusted pointwise p-values. |
p.overall |
Overall p-value of the permutation test. |
Craig Wang
Walker, D. D., J. C. Loftis, and J. P. W. Mielke (1997). Permutation methods for determining the significance of spatial dependence. Mathematical Geology 29(8), 1011–1024.
Fisher R. A. (1932). Statistical methods for research workers, 4th ed. Oliver & Boyd.
Poole, W., D. L. Gibbs, I. Shmulevich, B. Bernard, and T. A. Knijnenburg (2016). Combining dependent P-values with an empirical adaptation of Brown’s method. Bioinformatics 32(17), 430–436.
Wang, C., Furrer, R. (2018) Monte Carlo Permutation Tests for Assessing Spatial Dependence at Difference Scales. Nonparametric Statistics. (Submitted)
envelope
to use Monte Carlo permutations for generating variogram envelope.
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