sparrpowR-package | R Documentation |

Computes the statistical power for the spatial relative risk function.

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.

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.

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. ian.buller@alumni.emory.edu.gov

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