Description Usage Arguments Details Value See Also Examples
Function for the simulation framework in GAIL. Create the underlying rate of cases for each regular spatial unit which can follow a user-specified pattern. This can be used to generate the rate of cases, as well as the rate of individuals being in the irregular spatial unit.
For creating simulated data, this function can be bypassed if the user creates variables named 'case_rate' and 'rural_rate' in the set of regular spatial units.
1 2 | gail_sim_rate(units_reg, rate_base = c(0.03, 0.07), rate_spec = NULL,
seed = NULL)
|
units_reg |
Set of regular spatial units |
rate_base |
Vector of length 2 giving upper and lower bounds for uniform distribution.
The base rate is randomly allocated between these two values.
Default is |
rate_spec |
A |
seed |
If given, sets the seed for the RNG. |
... |
Space for additional arguments (e.g., for |
Each row of rate_spec
creates a 'hotspot' (or 'coldspot') in terms of the incidence
rate of cases. That is: areas in the spatial domain which have an increased or decreased rate. This
should be a data.frame
(or comprable object) with columns: mx
, my
, ax
, ay
, efc
. The
hotspot is centered at the point (mx
, my
), while ax
and ay
control the size in the x and y
directions, respectively, with larger values corresponding to larger range in that dimension. The efc
value is the effect size, which acts as a multiplier for the base rate of indidence.
The incidence rate is generated by first drawing a base rate for each spatial unit
from a uniform distribution with bounds given by rate_base
.
Then n=2000 points are drawn uniformly across the spatial domain (100x100 square). These points are
given a weight on the interval [0, 1]
, which decreases from 1 at the center of the hotspot down to 0.
The weight is multiplied by the hotspot effect size (efc
), and shifted so that each of the 2000
points have a value on the interval [1, efc]
. The mean effect is taken for all individuals contained
within a spatial region, and that mean is used as a multiplier for the base rate of that region.
Returns a vector of length nrow(units_reg)
which contains
gail_sim_regions, gail_sim_pop, gail_sim_index, gail_sim_assign
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ## Not run:
## Generate Regions
loca_reg <- gail_gen_regions( npoints=40, type="regular", nedge=10, suid="reg" )
## Generate incidence rate
rate_spec <- data.frame(
mx = c(25, 60),
my = c(25, 80),
ax = c(10, 40),
ay = c(25, 20),
efc = c( 0.15 , 4.0 )
)
loca_reg[["case_rate"]] <- gail_sim_rate( loca_reg, rate_base=c(0.03,0.07),
rate_spec=rate_spec, seed=42 )
ggplot( loca_reg ) +
geom_sf( aes(fill=case_rate) )
## Generate rate of being in irregular locations
irr_spec <- data.frame(
mx = c(85, 20, 25, 60),
my = c(15, 80, 25, 80),
ax = c(20, 10, 10, 40),
ay = c(20, 20, 25, 20),
efc = c(4.0, 4.0, 0.15 , 0.15 )
)
loca_reg[["rural_rate"]] <- gail_sim_rate( loca_reg, rate_base=c(0.03,0.07),
rate_spec=rate_spec, seed=42 )
ggplot( loca_reg ) +
geom_sf( aes(fill=rural_rate) )
## End(Not run)
|
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