Roccurve

Purpose

Calculate and display ROC curve(s) for yknown and ypred.

Synopsis

roc = roccurve(yknown, ypred, options)

Description

ROC curves can be used to assess the specificity and sensitivity for different predicted y-value thresholds, for input y known and predicted.

Cases:

If yknown is nx1 logical vector and ypred is nxm, then m roc curves are produced, one for each column of ypred. roc is a dataset nx(2*m), containing column-pairs of Specificity and Sensitivity for each yknown vs. ypred pairing.

If yknown is nxm logical and ypred is nxm then m roc curves are produced, one for each pair of yknown and its corresponding ypred column. roc is a dataset nx(2*m).

If yknown is multi-column, nxm, and ypred has a different number of columns, nxp, then an error is thrown.

Inputs

• first = first input is this.
• yknown = nx1 logical vector, or vector of only 0's and another integer or nxm logical vector.
• ypred = nxm double array, m columns of y predictions.

Outputs

• roc = Output is a dataset with the specificity/sensitivity data (roc).

Options

options = a structure array with the following fields:

• plots: [ {'none'} | 'final' ] governs plotting of results, and
• figure: [ 'new' | 'gui' | figure_handle ] governs location for plot. 'new' plots onto a new figure. 'gui' plots using noninteger figure handle. A figure handle specifies the figure onto which the plot should be made.
• plotstyle: [ 'roc' | 'threshold' | {'all'} ] governs type of plots.

'roc' and 'threshold' give only the specified type of

plot. 'all' shows both types of plots on one figure (default).

Plot style can also be specified as 1 (which gives 'roc' plots) or 2 (which gives 'threshold' plots)

See Also

plsdaroc