sim2D_RandSet_HPPP {sim2Dpredictr} | R Documentation |
Generate a Random Set Using a Poisson Process and Random Radii About Events
Description
A random set is generated by using a Poisson process in 2D space to choose
'event' locations, about which a circle of random radius is 'drawn'. The
union of the circles defines ultimately defines the set.
Usage
sim2D_RandSet_HPPP(
N,
xlim = c(0, 1),
ylim = c(0, 1),
radius.bounds = c(0.02, 0.1),
lambda = 50,
lambda.sd = 10,
lambda.bound = NULL,
prior = "gamma",
random.lambda = FALSE,
sub.area = FALSE,
min.sa = c(0.1, 0.1),
max.sa = c(0.3, 0.3),
radius.bounds.min.sa = c(0.02, 0.05),
radius.bounds.max.sa = c(0.08, 0.15),
print.subj.sa = FALSE,
print.lambda = FALSE,
print.iter = FALSE
)
Arguments
N |
A scalar value determining the number of images to create.
|
xlim , ylim |
These are the 2D image limits. Defaults for both are
c(0, 1) . It is not recommended to alter these arguments unless
changing the limits has a specific practical utility.
|
radius.bounds |
A 2-element vector whose first and second entries
determine the minimum and maximum radius sizes, respectively; these will
be the bounds of the uniform distribution used to draw the radii. If
sub.area = TRUE , then use radius.bounds.min.sa and
radius.bounds.max.sa .
|
lambda |
A scalar value specifying the mean/intensity value of the
Poisson process. If random.lambda = FALSE then this is the parameter
used to generate the binary image for each subject. If
random.lambda = TRUE , then this is the mean parameter in the
distribution used to draw subject-specific lambda.
|
lambda.sd |
Only utilized when random.lambda = TRUE , and
specifies the standard deviation in the distribution used to draw
subject-specific lambda.
|
lambda.bound |
Only utilized when random.lambda = TRUE , and
allows the user to specify a lower and upper bound for the subject-specific
lambda; if the randomly selected value is outside of this range, then
another draw is taken. This continues until a value is selected within the
specified bounds. If no bounds are desired then specify
lambda.bound = NULL .
|
prior |
Only utilized when random.lambda = TRUE , and specifies
the distribution from which to draw the subject-specific lambda.
Options are c("gaussian", "gamma") .
|
random.lambda |
random.lambda = TRUE allows the lambda
(mean/intensity) parameter in the Poisson process to vary randomly by
subject.
|
sub.area |
When sub.area = TRUE , a random sub-section of the
image is chosen, within which the Poisson process is used to generate the
binary image.
|
min.sa , max.sa |
Only utilized when sub.area = TRUE , and
determines the width and height of the minimum and maximum sub-areas;
e.g., if min.sa = c(0.1, 0.1) , then the smallest possible random
sub-area is a 0.1 x 0.1 square.
|
radius.bounds.min.sa , radius.bounds.max.sa |
Only utilized when
sub.area = TRUE , and specifies radius.bounds for the minimum
and maximum sub-areas, respectively. This information is used to adaptively
alter the bounds in between the minimum and maximum sub-areas.
|
print.subj.sa , print.lambda , print.iter |
These arguments are either
TRUE or FALSE , and define print options for checking that the
function is working as the user intends. print.subj.sa = TRUE prints
the x-and y-limits for each subject's sub-area. print.lambda = TRUE
prints each subject's mean and realized events; the means will be the same
unless random.lambda = TRUE , but the number of realized events will
always vary. print.iter = TRUE is only used when
random.lambda = TRUE and is.null(lambda.bound) = FALSE , and
shows iterations for re-drawing when the randomly selected intensity is
outside the specified bounds.
|
Value
A dataframe with columns for subject ID, x-coordinates,
y-coordinates, and associated radii.
References
Cressie N, Wikle CK (2011).
Statistics for Spatio-Temporal Data, Wiley Series in Probability and Statistics.
John Wiley & Sons, Hoboken, NJ.
[Package
sim2Dpredictr version 0.1.1
Index]