screeplot {analogue} | R Documentation |
Draws screeplots of performance statistics for models of varying complexity.
## S3 method for class '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 class '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"))
x |
object of class mat and bootstrap.mat . |
k |
number of analogues to use. If missing 'k' is chosen automatically as the 'k' that achieves lowest RMSE. |
restrict |
logical; restrict comparison of k-closest model to k
<= restrict . |
display |
which aspect of x to plot? Partial match. |
weighted |
logical; should the analysis use weighted mean of env data of analogues as fitted/estimated values? |
xlab, ylab |
x- and y-axis labels respectively. |
main, sub |
main and subtitle for the plot. |
legend |
logical; should a legend be displayed on the figure? |
loc.legend |
character; a keyword for the location of the
legend. See legend for details of allowed keywords. |
col |
Colours for lines drawn on the screeplot. Method for class
"bootstrap.mat" takes a vector of two colours. |
lty |
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. |
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 R^2.
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.
Currently only models of class mat
and
bootstrap.mat
are supported.
Gavin Simpson
## continue the example from ?join example(join) ## fit the MAT model using the squared chord distance measure swap.mat <- mat(swapdiat, swappH, method = "SQchord") swap.mat ## screeplot(swap.mat)