graph.idw: Graph that describes the behavior of the optimized p smoothing parameter.

Description

Function for plotting the RMSPE for several values of the p smoothing parameter with the same dataset. A curve is fitted to the points, and then the optimal p that provides the smallest RMSPE is determined from the curve, by the optimize function from the stats package.

Usage

graph.idw(formula, data, locations, np, p.dmax, P.T=NULL, nmax=Inf, nmin=0, pleg, 
    progress=F, iter, ...)

Value

Returns a graph that describes the behavior of the optimized p parameter associated with the RMSPE, and a table of values associated with the graph including optimal smoothing p parameter, which generates the lowest RMSPE.

Arguments

formula: formula that defines the dependent variable as a linear model of independent variables; suppose the dependent variable has name z, for a idw detrended use z~1
data: SpatialPointsDataFrame: should contain the dependent variable, independent variables, and coordinates.
locations: object of class Spatial, or (deprecated) formula defines the spatial data locations (coordinates) such as ~x+y
np: number of points, where the idw is calculated
p.dmax: maximum value of the range of the p parameter that will be evaluated by the optimize function
P.T: logical. Print Table (TRUE) or not (FALSE). Default P.T=NULL.
nmax: maximum number of nearest observations that should be used for a idw prediction, where nearest is defined in terms of the spatial locations. By default, all observations are used
nmin: minimum number of nearest observations that should be used for a idw prediction, where nearest is defined in terms of the spatial locations. see krige
pleg: the x and y co-ordinates to be used to position the legend. They can be specified by keyword or in any way which is accepted by xy.coords, by default pleg="topright.
progress: logical. Use TRUE to see the percentage of progress of the process and FALSE otherwise). Default progress=FALSE.
iter: The maximum allowed number of function evaluations.
...: further parameters to be passed to the minimization functions optimize or bobyqa, typically arguments of the type control() which control the behavior of the minimization algorithm. See documentation about the selected minimization function for further details.

References

Johnston, K., Ver, J., Krivoruchko, K., Lucas, N. 2001. Using ArcGIS Geostatistical Analysis. ESRI.

Examples

Run this code

if (FALSE) {
data(ariari)
data(ariprec)
# p optimization
gp <- graph.idw(PRECI_TOT~ 1, ~x+y, data=ariprec, np=50, p.dmax=4, nmax=15, 
    nmin=15,pleg = "center", progress=T)
gp
gp$p

library(sp)
library(fields)
plot(ariari)
gridAri <- spsample(ariari,20000,"regular")
plot(gridAri)

idw.p <- idw(PRECI_TOT~ 1, ~ x+y, ariprec, gridAri, nmax=15, nmin=15, idp=2)
pal2 <- colorRampPalette(c("snow3","royalblue1", "blue4"))

# Inverse Distance Interpolations Precipitation Weighted (P = 2)
p1 <- spplot(idw.p[1], col.regions=pal2(100), cuts =60, scales = list(draw =T), 
    xlab ="East (m)", ylab = "North (m)",
       main = "", auto.key = F)

split.screen( rbind(c(0, 1,0,1), c(1,1,0,1)))
split.screen(c(1,2), screen=1)-> ind
screen( ind[1])
p1
screen( ind[2])
image.plot(legend.only=TRUE, legend.width=0.5, col=pal2(100), 
    smallplot=c(0.6,0.68, 0.5,0.75), 
    zlim=c(min(idw.p$var1.pred),max(idw.p$var1.pred)), 
    axis.args = list(cex.axis = 0.7))
close.screen( all=TRUE)

}