Draws screeplots of performance statistics for models of varying complexity.
# S3 method for mat
screeplot(x, k, restrict = 20,
display = c("rmsep", "avg.bias",
"max.bias", "r.squared"),
weighted = FALSE, col = "red", xlab = NULL,
ylab = NULL, main = NULL, sub = NULL, ...)# S3 method for bootstrap.mat
screeplot(x, k, restrict = 20,
display = c("rmsep","avg.bias","max.bias",
"r.squared"),
legend = TRUE, loc.legend = "topright",
col = c("red", "blue"),
xlab = NULL, ylab = NULL,
main = NULL, sub = NULL,
...,
lty = c("solid","dashed"))
object of class mat
and bootstrap.mat
.
number of analogues to use. If missing 'k' is chosen automatically as the 'k' that achieves lowest RMSE.
logical; restrict comparison of k-closest model to k
restrict
.
which aspect of x
to plot? Partial match.
logical; should the analysis use weighted mean of env data of analogues as fitted/estimated values?
x- and y-axis labels respectively.
main and subtitle for the plot.
logical; should a legend be displayed on the figure?
character; a keyword for the location of the
legend. See legend
for details of allowed keywords.
Colours for lines drawn on the screeplot. Method for class
"bootstrap.mat"
takes a vector of two colours.
vector detailing the line type to use in drawing the
screeplot of the apparent and bootstrap statistics,
respectively. Code currently assumes that length(lty)
is 2.
arguments passed to other graphics functions.
Gavin Simpson
Screeplots are often used to graphically show the results of cross-validation or other estimate of model performance across a range of model complexity.
Four measures of model performance are currently available: i) root
mean square error of prediction (RMSEP); ii) average bias --- the
mean of the model residuals; iii) maximum bias --- the maximum average
bias calculated for each of n sections of the gradient of the
environmental variable; and v) model
For the maximum bias statistic, the response (environmental) gradient is split into n = 10 sections.
For the bootstrap
method, apparent and bootstrap
versions of these statistics are available and plotted.
## Imbrie and Kipp example
## load the example data
data(ImbrieKipp)
data(SumSST)
data(V12.122)
## merge training and test set on columns
dat <- join(ImbrieKipp, V12.122, verbose = TRUE)
## extract the merged data sets and convert to proportions
ImbrieKipp <- dat[[1]] / 100
V12.122 <- dat[[2]] / 100
## fit the MAT model using the chord distance measure
(ik.mat <- mat(ImbrieKipp, SumSST, method = "chord"))
screeplot(ik.mat)
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