rflex.midp

Computes P(Y &gt; cases) + P(Y = cases)/2 when Y ~
Poisson(ex) or Y ~ Binomial(n = pop, p = ex/pop). This
is middle p-value computed by Tango and Takahashi (2012).

Implements statistical methods for analyzing the counts of areal data, with a focus on the detection of spatial clusters and clustering. The package has a heavy emphasis on spatial scan methods, which were first introduced by Kulldorff and Nagarwalla (1995) <doi:10.1002/sim.4780140809> and Kulldorff (1997) <doi:10.1080/03610929708831995>.

Joshua French

smerc

Statistical Methods for Regional Counts

Mohammad Meysami

rflex.midp function

<dl><dt>cases</dt>
<dd>The number of cases observed in each region.</dd>
<dt>ex</dt>
<dd>The expected number of cases for each region.
The default is calculated under the constant risk
hypothesis.</dd>
<dt>type</dt>
<dd>The type of scan statistic to compute. The
default is <code>"poisson"</code>. The other choice
is <code>"binomial"</code>.</dd>
<dt>pop</dt>
<dd>The population size associated with each
region.</dd></dl>

Arguments

Author

Compute middle p-value — rflex.midp

<dl>

<dt>cases</dt>
<dd>The number of cases observed in each region.</dd>


<dt>ex</dt>
<dd>The expected number of cases for each region.
The default is calculated under the constant risk
hypothesis.</dd>


<dt>type</dt>
<dd>The type of scan statistic to compute. The
default is <code>"poisson"</code>. The other choice
is <code>"binomial"</code>.</dd>


<dt>pop</dt>
<dd>The population size associated with each
region.</dd>

</dl>

rflex.midp: Compute middle p-value

Description

Usage

Value

Arguments

Author

References

Examples