Usage
sample_dots(shp, vars = NULL, convert2density = FALSE, nrow = NA, ncol = NA, N = 250000, npop = NA, n = 10000, w = NA, shp.id = NULL, var.name = "class", var.labels = vars, target = "metric", orig = NA, to = NA, randomize = TRUE, output = c("points", "grid"), ...)
Arguments
vars
Names of one or more variables that are contained in shp
. If vars
is not provided, the dots are sampled uniformly. If vars
consists of one variable name, the dots are sampled according to the distribution of the corresponding variable. If vars
consist of more than one variable names, then the dots are sampled according to the distributions of those variables. A categorical variable is added that contains the distrubtion classes (see var.name
).
convert2density
Should the variables be converted to density values? Density values are used for the sampling algorithm, so use TRUE
when the values are absolute counts.
ncol
Number of grid colums
npop
Population total. If NA
, it is recontructed from the data. If density values are specified, the population total is approximated using the polygon areas (see also target
, orig
and to
).
w
Number of population units per dot. It is the population total divided by n
. If specified, n
is calculated accordingly.
shp.id
Name of the variable of shp
that contains the polygon identifying numbers or names.
var.name
Name of the variable that will be created to store the classes. The classes are defined by vars
, and the labels can be configured with var.labels
.
var.labels
Labels of the classes (see var.name
).
randomize
should the order of sampled dots be randomized? The dots are sampled class-wise (specified by vars
). If this order is not randomized (so if randomize=FALSE
), then the dots from the last class will be drawn on top, which may introduce a perception bias. By default randomize=TRUE
, so the sampled dots are randomized to prevent this bias.
output
format of the output: use "points"
for spatial points, and "grid"
for a spatial grid.