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.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
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)