Regression diagnostics for trend surfaces

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, ])
#>            r        hii   stresid         Di
#> 1   61.21889 0.35476783  2.585852 0.61275133
#> 4  -45.58507 0.13493260 -1.662930 0.07188916
#> 12  44.71663 0.21022336  1.707234 0.12930392
#> 31  52.05575 0.07154233  1.833006 0.04314966
#> 37  54.75944 0.06974770  1.926349 0.04637112
#> 48  97.75499 0.08574061  3.468809 0.18807312
#> 50 -63.25149 0.27530059 -2.520972 0.40237779
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)