sparrpowR-package {sparrpowR}

The sparrpowR Package: Power Analysis to Detect Spatial Relative Risk Clusters

Description

Computes the statistical power for the spatial relative risk function.

Details

For a two-group comparison (e.g., cases v. controls) the 'sparrpowR' package calculates the statistical power to detect clusters using the kernel-based spatial relative risk function that is estimated using the 'sparr' package. Details about the 'sparr' package methods can be found in the tutorial: Davies et al. (2018) doi:10.1002/sim.7577. Details about kernel density estimation can be found in J. F. Bithell (1990) doi:10.1002/sim.4780090616. More information about relative risk functions using kernel density estimation can be found in J. F. Bithell (1991) doi:10.1002/sim.4780101112.

This package provides a function to compute the statistical power for the spatial relative risk function with various theoretical spatial sampling strategies. The 'sparrpowR' package also provides a function to compute the statistical power for the spatial relative risk function for scenarios where one group (e.g., cases) have been observed and a theoretical sampling strategy for the second group (e.g., controls) is desired. The 'sparrpowR' package also provides visualization of data and statistical power.

Key content of the 'sparrpowR' package include:

Theoretical Spatial Sampling

spatial_data Generates random two-group data for a spatial relative risk function.

Statistical Power

spatial_power Computes the statistical power of a spatial relative risk function using randomly generated data.

jitter_power Computes the statistical power of a spatial relative risk function using previously collected data.

Data Visualization

spatial_plots Visualizes multiple plots of output from spatial_data, spatial_power and jitter_power functions.

Dependencies

The 'sparrpowR' package relies heavily upon sparr, spatstat.random, spatstat.geom, and terra for computing the statistical power and visualizing the output. Computation can be performed in parallel using doFuture, multisession, doRNG, and foreach. Basic visualizations rely on the plot.ppp and image.plot functions.

Author(s)

Ian D. Buller
Social & Scientific Systems, Inc., a division of DLH Corporation, Silver Spring, Maryland, USA (current); Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA (original)

Maintainer: I.D.B. [e-mail]