compare.cv {automap} | R Documentation |
Allows comparison of the results from several outcomes of autoKrige.cv
in both statistics and spatial plots
(bubble plots).
compare.cv(..., col.names, bubbleplots = FALSE, zcol = "residual", layout, key.entries, reference = 1, plot.diff = FALSE)
... |
autoKrige.cv objects that are compared to each other |
col.names |
Names for the different objects in ... . This defaults to
A for the first object, B for the second, etc. |
bubbleplots |
logical, if TRUE then bubble plots of the objects in ... are
drawn using the same value for the color breaks. |
zcol |
Which column in the objects in ... is going to be drawn in the bubbleplots. Options
are: var1.pred , var1.var , observed , residual and zscore . |
layout |
layout of the bubbleplot, e.g. c(2,2). The argument gives the number of rows and columns
in which the set of bubbleplots is to be drawn. Useful defaults are selected. |
key.entries |
A list of numbers telling what the key entries in the bubbleplots are. See bubble for more details. |
reference |
An integer telling which of the objects should be taken as a reference if plot.diff equals TRUE. reference
equal to 1 means that the first object is the reference, reference equal to 2 means that the second object is the reference etc. |
plot.diff |
logical, if plot.diff is TRUE the number specified in reference defines the CV object that is taken as a reference
What is shown in the plot is reference data squared minus the other data squared. So the color red means that the CV is doing
worse than the reference, vice-versa for green. This is very useful to see where the differences between the results are spatially and if
there is a pattern. |
A data.frame with for each cross-validation result a number of diagnostics:
mean_error |
The mean of the cross-validation residual. Ideally small. |
MSE |
Mean Squared error. |
MSNE |
Mean Squared Normalized Error, mean of the squared z-scores. Ideally small. |
cor_obspred |
Correlation between the observed and predicted values. Ideally 1. |
cor_predres |
Correlation between the predicted and the residual values. Ideally 0. |
RMSE |
Root Mean Squared Error of the residual. Ideally small. |
URMSE |
Unbiased Root Mean Squared Error of the residual. Ideally small. |
iqr |
Interquartile Range of the residuals. Ideally small. |
Paul Hiemstra, p.hiemstra@geo.uu.nl
krige.cv
, bubble
, autofitVariogram
, autoKrige.cv
,
# Load the data data(meuse) coordinates(meuse) = ~x+y data(meuse.grid) gridded(meuse.grid) = ~x+y # Perform cross-validation kr.cv = autoKrige.cv(log(zinc)~1, meuse, model = c("Exp")) kr_dist.cv = autoKrige.cv(log(zinc)~sqrt(dist), meuse, model = c("Exp")) kr_dist_ffreq.cv = autoKrige.cv(log(zinc)~sqrt(dist)+ffreq, meuse, model = c("Exp")) # Compare the results compare.cv(kr.cv, kr_dist.cv, kr_dist_ffreq.cv) compare.cv(kr.cv, kr_dist.cv, kr_dist_ffreq.cv, bubbleplots = TRUE) compare.cv(kr.cv, kr_dist.cv, kr_dist_ffreq.cv, bubbleplots = TRUE, col.names = c("OK","UK1","UK2")) compare.cv(kr.cv, kr_dist.cv, kr_dist_ffreq.cv, bubbleplots = TRUE, col.names = c("OK","UK1","UK2"), plot.diff = TRUE)