spatial_power
|
Main function. Compute the statistical power of a spatial relative risk
function using randomly generated data.
|
spatial_data
|
Generate random bivariate data for a spatial relative risk function.
|
jitter_power
|
Compute the statistical power of a spatial relative risk function using
previously collected data.
|
spatial_plots
|
Easily make multiple plots from spatial_power,
spatial_data, and jitter_power outputs.
|
pval_correct
|
Called within spatial_power and jitter_power,
calculates various multiple testing corrections for the alpha level.
|
Authors
Ian D. Buller - Occupational and
Environmental Epidemiology Branch, Division of Cancer Epidemiology and
Genetics, National Cancer Institute, National Institutes of Health,
Rockville, Maryland - GitHub
- ORCID
Derek W. Brown - Integrative Tumor
Epidemiology Branch, Division of Cancer Epidemiology and Genetics,
National Cancer Institute, National Institutes of Health, Rockville,
Maryland - GitHub -
ORCID
See also the list of contributors
who participated in this package, including:
- Tim A. Myers - Laboratory of Genetic
Susceptibility, Division of Cancer Epidemiology and Genetics, National
Cancer Institute, National Institutes of Health, Rockville,
Maryland - GitHub
Usage
set.seed(1234) # for reproducibility
# ------------------ #
# Necessary packages #
# ------------------ #
library(sparrpowR)
library(spatstat.geom)
library(stats)
# ----------------- #
# Run spatial_power #
# ----------------- #
# Circular window with radius 0.5
# Uniform case sampling within a disc of radius of 0.1 at the center of the window
# Complete Spatial Randomness control sampling
# 20% prevalence (n = 300 total locations)
# Statistical power to detect both case and control relative clustering
# 100 simulations (more recommended for power calculation)
unit.circle <- spatstat.geom::disc(radius = 0.5, centre = c(0.5,0.5))
foo <- spatial_power(win = unit.circle,
sim_total = 100,
x_case = 0.5,
y_case = 0.5,
samp_case = "uniform",
samp_control = "CSR",
r_case = 0.1,
n_case = 50,
n_control = 250)
# ----------------------- #
# Outputs from iterations #
# ----------------------- #
# Mean and standard deviation of simulated sample sizes and bandwidth
mean(foo$n_con); stats::sd(foo$n_con) # controls
mean(foo$n_cas); stats::sd(foo$n_cas) # cases
mean(foo$bandw); stats::sd(foo$bandw) # bandwidth of case density (if fixed, same for control density)
# Global Test Statistics
## Global maximum relative risk: Null hypothesis is mu = 1
stats::t.test(x = foo$s_obs, mu = 0, alternative = "two.sided")
## Integral of log relative risk: Null hypothesis is mu = 0
stats::t.test(x = foo$t_obs, mu = 1, alternative = "two.sided")
# ----------------- #
# Run spatial_plots #
# ----------------- #
# Statistical power for case-only clustering (one-tailed test)
spatial_plots(foo)
# Statistical power for case clustering and control
clustering (two-tailed test)
## Only showing second and third plot
spatial_plots(foo, cascon = TRUE)
# --------------------------- #
# Multiple Testing Correction #
# --------------------------- #
# Same parameters as above
# Apply a conservative Bonferroni correction
set.seed(1234) # reset RNG
# Run spatial_power()
foo <- spatial_power(win = unit.circle,
sim_total = 100,
x_case = 0.5,
y_case = 0.5,
samp_case = "uniform",
samp_control = "CSR",
r_case = 0.1,
n_case = 50,
n_control = 250,
alpha = 0.05,
p_correct = "FDR")
median(foo$alpha) # critical p-value of 3e-6
# Run spatial_plots() for case-only clustering
## Only showing third plot
spatial_plots(foo, cascon = TRUE)
Funding
This package was developed while the authors were postdoctoral
fellows supported by the Cancer
Prevention Fellowship Program at the National Cancer Institute.
Acknowledgments
When citing this package for publication, please follow:
citation("sparrpowR")
Questions? Feedback?
For questions about the package please contact the maintainer
Dr. Ian D. Buller
or submit a
new issue.
