morancr.sim {smerc}

Constant-risk Moran's I statistic

Description

morancr.stat computes the constant-risk version of the Moran's I statistic proposed by Walter (1992).

Usage

morancr.sim(nsim = 1, cases, w, ex)

Arguments

nsim

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

cases

The number of cases observed in 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.

Value

Returns a numeric value.

Author(s)

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.test

Examples

data(nydf)
data(nyw)
ex <- sum(nydf$cases) / sum(nydf$pop) * nydf$pop
morancr.sim(nsim = 10, cases = nydf$cases, w = nyw, ex = ex)