jitter_power | R Documentation |
Compute the statistical power of a spatial relative risk function using previously collected data.
jitter_power(
obs_data,
sim_total = 2,
samp_control = c("uniform", "CSR", "MVN"),
s_control = 1,
alpha = 0.05,
p_correct = "none",
parallel = FALSE,
n_core = 2,
verbose = TRUE,
...,
cascon = lifecycle::deprecated(),
lower_tail = lifecycle::deprecated(),
upper_tail = lifecycle::deprecated()
)
This function computes the statistical power of the spatial relative risk function (nonparametric estimate of relative risk by kernel smoothing) for previously collected studies with known case and control locations.
The function uses the risk
function to estimate the spatial relative risk function and forces the tolerate
argument to be TRUE in order to calculate asymptotic p-values.
If samp_control = "uniform"
the control locations are randomly generated uniformly within the dow of obs_data
. By default, the resolution is an integer value of 128 and can be specified using the resolution
argument in the internally called risk
function.
If samp_control = "CSR"
the control locations are randomly generated assuming complete spatial randomness (homogeneous Poisson process) within the dow of obs_data
with a lambda = number of controls / [resolution x resolution]
. By default, the resolution is an integer value of 128 and can be specified using the resolution
argument in the internally called risk
function.
If samp_control = "MVN"
the control locations are randomly generated assuming a multivariate normal distribution centered at each observed location. The optional argument s_control
specifies the standard deviation of the multivariate normal distribution (1 by default) in the units of the obs_data
.
The function computes a one-sided hypothesis test for case clustering (alpha = 0.05
by default). The function also computes a two-sided hypothesis test for case clustering and control clustering (lower tail = 0.025 and upper tail = 0.975).
The function has functionality for a correction for multiple testing. If p_correct = "FDR"
, calculates a False Discovery Rate by Benjamini and Hochberg. If p_correct = "Sidak"
, calculates a Sidak correction. If p_correct = "Bonferroni"
, calculates a Bonferroni correction. If p_correct = "none"
(the default), then the function does not account for multiple testing and uses the uncorrected alpha
level. See the internal pval_correct
function documentation for more details.
An object of class "list". This is a named list with the following components:
sim
An object of class 'rrs' for the first iteration of simulated data.
out
An object of class 'rrs' for the observed spatial relative risk function without randomization.
rr_mean
Vector of length [resolution x resolution]
of the mean relative risk values at each gridded knot.
pval_mean
Vector of length [resolution x resolution]
of the mean asymptotic p-value at each gridded knot.
rr_sd
Vector of length [resolution x resolution]
of the standard deviation of relative risk values at each gridded knot.
pval_prop_cascon
Vector of length [resolution x resolution]
of the proportion of asymptotic p-values that were significant for both case and control locations at each gridded knot.
pval_prop_cas
Vector of length [resolution x resolution]
of the proportion of asymptotic p-values that were significant for only case locations at each gridded knot.
rx
Vector of length [resolution x resolution]
of the x-coordinates of each gridded knot.
ry
Vector of length [resolution x resolution]
of the y-coordinates of each gridded knot.
n_cas
Vector of length sim_total
of the number of case locations simulated in each iteration.
n_con
Vector of length sim_total
of the number of control locations simulated in each iteration.
bandw
Vector of length sim_total
of the bandwidth (of numerator) used in each iteration.
s_obs
Vector of length sim_total
of the global s statistic.
t_obs
Vector of length sim_total
of the global t statistic.
alpha
Vector of length sim_total
of the (un)corrected critical p-values.
risk
for additional arguments for bandwidth selection, edge correction, and resolution.
# Using the 'chorley' data set from 'spatstat.data' package
data(chorley, package="spatstat.data")
f1 <- jitter_power(obs_data = unique(chorley),
samp_control = "CSR",
verbose = FALSE)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.