aple.plot {spdep} | R Documentation |
A scatterplot decomposition of the approximate profile-likelihood estimator, and a local APLE based on the list of vectors returned by the scatterplot function.
aple.plot(x, listw, do.plot=TRUE, ...) localAple(x, listw)
x |
a zero-mean detrended continuous variable |
listw |
a listw object from for example nb2listw |
do.plot |
default TRUE: should a scatterplot be drawn |
... |
other arguments to be passed to plot |
The function solves a secondary eigenproblem of size n internally, so constructing the values for the scatterplot is quite compute and memory intensive, and is not suitable for very large n.
aple.plot
returns list with components:
X |
A vector as described in Li et al. (2007), p. 366. |
Y |
A vector as described in Li et al. (2007), p. 367. |
localAple
returns a vector of local APLE values.
Roger Bivand Roger.Bivand@nhh.no
Li, H, Calder, C. A. and Cressie N. A. C. (2007) Beyond Moran's I: testing for spatial dependence based on the spatial autoregressive model. Geographical Analysis 39, pp. 357-375.
example(aple) plt_out <- aple.plot(scale(wheat$yield_detrend, scale=FALSE), nb2listw(nbr12, style="W"), cex=0.6) crossprod(plt_out$Y, plt_out$X)/crossprod(plt_out$X) lm_obj <- lm(Y ~ X, plt_out) abline(lm_obj) abline(v=0, h=0, lty=2) zz <- summary(influence.measures(lm_obj)) infl <- as.integer(rownames(zz)) points(plt_out$X[infl], plt_out$Y[infl], pch=3, cex=0.6, col="red") wheat$localAple <- localAple(scale(wheat$yield_detrend, scale=FALSE), nb2listw(nbr12, style="W")) mean(wheat$localAple) hist(wheat$localAple) spl <- list("sp.text", coordinates(wheat)[infl,], rep("*", length(infl))) spplot(wheat, "localAple", sp.layout=spl)