trls.influence {spatial} | R Documentation |
This function provides the basic quantities which are used in
forming a variety of diagnostics for checking the quality of
regression fits for trend surfaces calculated by surf.ls
.
trls.influence(object) plot(x, border = "red", col = NA, pch = 4, cex = 0.6, add = FALSE, div = 8, ...)
object, x |
Fitted trend surface model from surf.ls
|
div |
scaling factor for influence circle radii in plot.trls
|
add |
add influence plot to existing graphics if TRUE
|
border, col, pch, cex, ... |
additional graphical parameters |
r |
raw residuals as given by residuals.trls
|
hii |
diagonal elements of the Hat matrix |
stresid |
standardised residuals |
Di |
Cook's statistic |
trls.influence
returns a list with:
Unwin, D. J., Wrigley, N. (1987) Towards a general-theory of control point distribution effects in trend surface models. Computers and Geosciences, 13, 351355.
surf.ls
, influence.measures
, plot.lm
library(MASS) data(topo, package="MASS") topo2 <- surf.ls(2, topo) infl.topo2 <- trls.influence(topo2) cand <- as.data.frame(infl.topo2)[abs(infl.topo2$stresid) > 1.5,] cand cand.xy <- topo[as.integer(rownames(cand)), c("x", "y")] trsurf <- trmat(topo2, 0, 6.5, 0, 6.5, 50) eqscplot(trsurf, type="n") #under S need to choose appropriate colour numbers contour(trsurf, add=TRUE, col="grey") plot(topo2, add=TRUE, div=3) points(cand.xy, pch=16, col="orange") text(cand.xy, labels=rownames(cand.xy), pos=4, offset=0.5)