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imported>Donal (Created page with "===Purpose=== Calculate and display ROC curve(s) for yknown and ypred. ===Synopsis=== : roc = roccurve(yknown, ypred, options) ===Description=== ROC curves can be used to as...") |
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:If yknown is multi-column, nxm, and ypred has a different number of columns, nxp, then an error is thrown. | :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. | * '''first''' = first input is this. | ||
* '''yknown''' = nx1 logical vector, or vector of only 0's and another integer or nxm logical vector. | * '''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. | * '''ypred''' = nxm double array, m columns of y predictions. | ||
===Outputs=== | |||
* '''roc''' = Output is a dataset with the specificity/sensitivity data (roc). | * '''roc''' = Output is a dataset with the specificity/sensitivity data (roc). | ||
===Options=== | |||
options = a structure array with the following fields: | options = a structure array with the following fields: | ||
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:plot. 'all' shows both types of plots on one figure (default). | :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) | :Plot style can also be specified as 1 (which gives 'roc' plots) or 2 (which gives 'threshold' plots) | ||
===See Also=== | ===See Also=== | ||
[[ | [[plsdaroc]] |
Revision as of 17:14, 2 October 2012
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)