smerc (version 1.8.3)

morancr.test: Constant-risk Moran's I-based test

Description

morancr.test performs a test of clustering using the constant-risk version of the Moran's I statistic proposed by Walter (1992) under the constant risk hypothesis.

Usage

morancr.test(
  cases,
  pop,
  w,
  ex = sum(cases)/sum(pop) * pop,
  nsim = 499,
  alternative = "greater"
)

Value

Returns a smerc_similarity_test.

Arguments

cases

The number of cases observed in each region.

pop

The population size associated with each region.

w

A binary spatial adjacency matrix for the regions.

ex

The expected number of cases for each region. The default is calculated under the constant risk hypothesis.

nsim

The number of simulations from which to compute the p-value.

alternative

a character string specifying the alternative hypothesis, must be one of "greater" (default), "two.sided", or "less". You can specify just the initial letter.

Author

Joshua French

References

Walter, S. D. (1992). The analysis of regional patterns in health data: I. Distributional considerations. American Journal of Epidemiology, 136(6), 730-741.

See Also

morancr.stat

Examples

data(nydf)
data(nyw)
morancr.test(cases = nydf$cases, pop = nydf$pop, w = nyw, nsim = 9)