| 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)
## S3 method for class 'trls':
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 |
trls.influence returns a list with components:
r |
raw residuals as given by residuals.trls
|
hii |
diagonal elements of the Hat matrix |
stresid |
standardised residuals |
Di |
Cook's statistic |
Unwin, D. J., Wrigley, N. (1987) Towards a general-theory of control point distribution effects in trend surface models. Computers and Geosciences, 13, 351–355.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
surf.ls, influence.measures, plot.lm
library(MASS) # for eqscplot
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.xy <- topo[as.integer(rownames(cand)), c("x", "y")]
trsurf <- trmat(topo2, 0, 6.5, 0, 6.5, 50)
eqscplot(trsurf, type = "n")
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)