Screeplot {analogue} | R Documentation |
Draws screeplots of performance statistics for models of varying complexity.
Screeplot(x, ...) ## Default S3 method: Screeplot(x, ...) ## S3 method for class 'mat': Screeplot(x, k, restrict = 20, display = c("rmse", "rmsep", "avg.bias", "max.bias", "r.squared"), weighted = TRUE, xlab = NULL, ylab = NULL, main = NULL, sub = NULL, ...) ## S3 method for class 'bootstrap': Screeplot(x, k, restrict = 20, display = c("rmse","rmsep","avg.bias","max.bias", "r.squared"), legend = TRUE, loc.legend = "topright", xlab = NULL, ylab = NULL, main = NULL, sub = NULL, ..., lty = c("solid","dashed"))
x |
object on which method dispatch applied; currently only for
class mat and bootstrap . |
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. |
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.
Five measures of model performance are currently available: i) root mean square error (RMSE); ii) root mean square error of prediction (RMSEP); iii) average bias — the mean of the model residuals; iv) 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 bootstrap
method, apparent and bootstrap
versions of these statistics are available and plotted.
Currently only models of class mat
and
bootstrap
are supported.
Gavin Simpson
## load the example data data(swapdiat) data(swappH) data(rlgh) ## process so common set of columns for training and test ## number of training set samples n.train <- nrow(swapdiat) ## merge training and test set on columns dat <- join(swapdiat, rlgh, verbose = TRUE) ## convert to proportions dat <- dat / 100 ## subset data back into training and test sets swapdiat <- dat[1:n.train, ] rlgh <- dat[(n.train+1):nrow(dat), ] ## fit the MAT model using the squared chord distance measure swap.mat <- mat(swapdiat, swappH, method = "SQchord") swap.mat ## Screeplot(swap.mat)