trls.influence {spatial}
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
Values
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)
Documentation reproduced from package spatial, version 7.3-6. License: GPL-2 | GPL-3