trls.influence: Regression diagnostics for trend surfaces

In spatial: Functions for Kriging and Point Pattern Analysis

Regression diagnostics for trend surfaces

Description

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.

Usage

trls.influence(object)
## S3 method for class 'trls'
plot(x, border = "red", col = NA, pch = 4, cex = 0.6,
     add = FALSE, div = 8, ...)

Arguments

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

Value

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

References

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.

See Also

surf.ls, influence.measures, plot.lm

Examples

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)

spatial documentation built on July 26, 2023, 5:33 p.m.