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 
k 
number of analogues to use. If missing 'k' is chosen automatically as the 'k' that achieves lowest RMSE. 
restrict 
logical; restrict comparison of kclosest model to k
<= 
display 
which aspect of 
weighted 
logical; should the analysis use weighted mean of env data of analogues as fitted/estimated values? 
xlab, ylab 
x and yaxis 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 
col 
Colours for lines drawn on the screeplot. Method for class

lty 
vector detailing the line type to use in drawing the
screeplot of the apparent and bootstrap statistics,
respectively. Code currently assumes that 
... 
arguments passed to other graphics functions. 
Screeplots are often used to graphically show the results of crossvalidation 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
## 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)