Generate random two-group data for a spatial relative risk function.
spatial_data(
win = spatstat.geom::unit.square(),
sim_total = 2,
x_case,
y_case,
samp_case = c("uniform", "MVN", "CSR", "IPP"),
samp_control = c("uniform", "systematic", "MVN", "CSR", "IPP", "clustered"),
x_control = NULL,
y_control = NULL,
n_case = NULL,
n_control = NULL,
npc_control = NULL,
r_case = NULL,
r_control = NULL,
s_case = NULL,
s_control = NULL,
l_case = NULL,
l_control = NULL,
e_control = NULL,
...
)An object of class "ppplist". This is a list of marked point patterns that have a single mark with two levels: case and control.
Window in which to simulate the random data. An object of class "owin" or something acceptable to as.owin.
Integer, specifying the number of simulation iterations to perform.
Numeric value, or numeric vector, of x-coordinate(s) of case cluster(s).
Numeric value, or numeric vector, of y-coordinate(s) of case cluster(s).
Character string specifying whether to randomize the case locations uniformly (samp_control = "uniform"), multivariate normal (samp_control = "MVN"), with complete spatial randomness (samp_control = "CSR"), or using the inhomogeneous Poisson process (samp_control = "IPP") around each case centroid.
Character string specifying whether to randomize the control locations uniformly (samp_control = "uniform"), systematically (samp_control = "systematic"), multivariate normal (samp_control = "MVN"), with complete spatial randomness (samp_control = "CSR"), using the inhomogeneous Poisson process (samp_control = "IPP"), or a realization of the Neyman-Scott cluster process (samp_control = "clustered").
Numeric value, or numeric vector, of x-coordinate(s) of case cluster(s). Ignored if samp_control != "MVN".
Numeric value, or numeric vector, of y-coordinate(s) of case cluster(s). Ignored if samp_control != "MVN".
Numeric value, or numeric vector, of the sample size for case locations in each cluster.
Numeric value, or numeric vector, of the sample size for control locations in each cluster.
Optional. Numeric value of the number of clusters of control locations. Ignored if samp_control != "clustered".
Optional. Numeric value, or numeric vector, of radius (radii) of case cluster(s) in the units of win. Ignored if samp_case = "MVN".
Optional. Numeric value, or numeric vector, of radius (radii) of control cluster(s) in the units of win. Ignored if samp_control != "clustered".
Optional. Numeric value, or numeric vector, for the standard deviation(s) of the multivariate normal distribution for case locations in the units of win. Ignored if samp_control != "MVN".
Optional. Numeric value, or numeric vector, for the standard deviation(s) of the multivariate normal distribution for control locations in the units of win. Ignored if samp_control != "MVN".
Optional. A single positive number, a vector of positive numbers, a function(x,y, ...), or a pixel image. Intensity of the Poisson process for case clusters. Ignored if samp_control != "IPP".
Optional. A single positive number, a vector of positive numbers, a function(x,y, ...), or a pixel image. Intensity of the Poisson process for control clusters. Ignored if samp_control = "uniform", samp_control = "systematic", samp_control = "MVN", or samp_control = "CSR".
Optional. A single non-negative number for the size of the expansion of the simulation window for generating parent points. Ignored if samp_control != "clustered".
Arguments passed to runifdisc, disc, rpoispp, rsyst, or rNeymanScott depending on samp_control or samp_control.
This function generates random data for a spatial relative risk function (nonparametric estimate of relative risk by kernel smoothing) using various random point pattern generators from the spatstat.random package to generate data.
If samp_case = "uniform" the case locations are randomly generated uniformly within a disc of radius r_case (or discs of radii r_case) centered at coordinates (x_case, y_case).
If samp_case = "MVN" the case locations are randomly generated assuming a multivariate normal distribution centered at coordinates (x_case, y_case) with a standard deviation of s_case.
If samp_case = "CSR" the case locations are randomly generated assuming complete spatial randomness (homogeneous Poisson process) within a disc of radius r_case (or discs of radii r_case) centered at coordinates (x_case, y_case) with lambda = n_case / area of disc.
If samp_case = "IPP" the case locations are randomly generated assuming an inhomogeneous Poisson process with a disc of radius r_case (or discs of radii r_case) centered at coordinates (x_case, y_case) with lambda = l_case, a function.
If samp_control = "uniform" the control locations are randomly generated uniformly within the window win.
If samp_control = "systematic" the control locations are randomly generated systematically within the window win consisting of a grid of equally-spaced points with a random common displacement.
If samp_control = "MVN" the control locations are randomly generated assuming a multivariate normal distribution centered at coordinates (x_control, y_control) with a standard deviation of s_control.
If samp_control = "CSR" the control locations are randomly generated assuming complete spatial randomness (homogeneous Poisson process) within the window win with a lambda = n_control / [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 = "IPP" the control locations are randomly generated assuming an inhomogeneous Poisson process within the window win with a lambda = l_control, a function.
If samp_control = "clustered" the control locations are randomly generated with a realization of the Neyman-Scott process within the window win with the intensity of the Poisson process cluster centres (kappa = l_control), the size of the expansion of the simulation window for generative parent points (e_control), and the radius (or radii) of the disc for each cluster (r_control).
runifdisc, disc, rpoispp, rsyst, or rNeymanScott for additional arguments for random point pattern generation.
spatial_data(x_case = c(0.25, 0.5, 0.75),
y_case = c(0.75, 0.25, 0.75),
samp_case = "MVN",
samp_control = "MVN",
x_control = c(0.25, 0.5, 0.75),
y_control = c(0.75, 0.25, 0.75),
n_case = 100,
n_control = c(100,500,300),
s_case = c(0.05,0.01,0.05),
s_control = 0.05,
verbose = FALSE)
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