This function performs several analysis in one go:
(i) apply rings();
(ii) compute prevalence surface with kde();
(iii) compute the surface of rings radii with krige();
(iv) plot prevalence surface using prevR.colors.red() and add rings radii
as a contour plot.
Usage
quick.prevR(
object,
N = Noptim(object),
nb.cells = 100,
cell.size = NULL,
weighted = NULL,
plot.results = TRUE,
return.results = FALSE,
return.plot = FALSE,
legend.title = "%",
cex = 0.7,
progression = TRUE
)Arguments
- object
object of class prevR.
- N
integer or list of integers corresponding to the rings to use.
- nb.cells
number of cells on the longest side of the studied area (unused if
cell.sizeis defined).- cell.size
size of each cell (in the unit of the projection).
- weighted
use weighted data (TRUE, FALSE or "2")?
- plot.results
plot the results?
- return.results
return the results?
- return.plot
return the plot within the results?
- legend.title
title of the legend
- cex
to control the text size on the graph
- progression
show a progress bar?
Value
A list of one or several elements, depending on the arguments:
(i) prev is a SpatialPixelsDataFrame containing the prevalence
surface; (ii) radius a SpatialPixelsDataFrame containing the
kriged surface of the rings radii; (iii) plot a ggplot graph.
Details
N determine the rings to use for the estimation.
By default, a suggested value of N will be computed with Noptim().