These functions execute a spatial interpolation of a variable of the slot rings
of
an object of class prevR
. The method krige
implements
the ordinary kriging technique. The method idw
executes an inverse distance weighting
interpolation.
# S4 method for ANY,prevR
krige(formula, locations, N = NULL, R = Inf,
model = NULL, nb.cells = 100, cell.size = NULL, fit = "auto",
keep.variance = FALSE, show.variogram = FALSE, ...)# S4 method for ANY,prevR
idw(formula, locations, N = NULL, R = Inf,
nb.cells = 100, cell.size = NULL, idp = 2, ...)
variable(s) to interpolate (see details).
object of class prevR
.
integer or list of integers corresponding to the rings to use.
integer or list of integers corresponding to the rings to use.
a variogram model returned by the function vgm
{gstat}.
number of cells on the longuest side of the studied area
(unused if cell.size
is defined).
size of each cell (in the unit of the projection).
"auto" for using a variogram automatically fitted from the data,
"manual" for using a variogram fitted through a graphic interface
(unused if model
is defined).
return variance of estimates?
plot the variogram?
inverse distance weighting power (see idw
{gstat}).
Object of class SpatialPixelsDataFrame
.
The name of estimated surfaces depends on the name of the interpolated variable, N and R
(for example: r.radius.N300.RInf). If you ask the function to return variance
(keep.variance=TRUE
), corresponding surfaces names will have the suffix .var.
NA
value is applied to points located outside of the studied area
(voir NA.outside.SpatialPolygons
).
formula
specifies the variable(s) to interpolate. Only variables available in the
slot rings
of locations
could be used. Possible values are "r.pos", "r.n", "r.prev",
"r.radius", "r.clusters", "r.wpos", "r.wn" ou "r.wprev". Variables could be specifed with a character
string or a formula (example: list(r.pos~1,r.prev~1
). Only formula like variable.name~1
are accepted. For more complexe interpolations, use directly functions krige
and
idw
from gstat.
N
and R
determine the rings to use for the interpolation. If they are not defined,
surfaces will be estimated for each available couples (N,R). Several interpolations could be
simultaneously calculated if several variables and/or several values of N and R are defined.
A suggested value of N could be computed with Noptim
.
In the case of an ordinary kriging, the method krige
from prevR will try to fit automatically
a exponantial variogram to the sample variogram (fit="auto"
). If you choose fit="manual"
,
the sample variogram will be plotted and a graphical dialog box (adapted from
eyefit
{geoR}) will appear for a manual and visual fitting. You can also specify
directly the variogram to use with the parameter model
. Packages geoR and tcltk
are required for manual fit.
Interpolations are calculated on spatial gridd obtained with
as.SpatialGrid
.
Larmarange Joseph, Vallo Roselyne, Yaro Seydou, Msellati Philippe and Meda Nicolas (2011) "Methods for mapping regional trends of HIV prevalence from Demographic and Health Surveys (DHS)", Cybergeo: European Journal of Geography, no 558, http://cybergeo.revues.org/24606, DOI: 10.4000/cybergeo.24606.
krige
{gstat}, idw
{gstat},
rings,prevR-method
, Noptim
.
# NOT RUN {
# }
# NOT RUN {
dhs <- rings(fdhs, N = c(100,200,300,400,500))
radius.N300 <- krige('r.radius', dhs, N = 300, nb.cells = 200)
prev.krige <- krige(r.wprev ~ 1, dhs, N = c(100, 300, 500))
library(sp)
spplot(prev.krige, c('r.wprev.N100.RInf', 'r.wprev.N300.RInf', 'r.wprev.N500.RInf'))
# }
# NOT RUN {
# }
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