Plsdaroc: Difference between revisions
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ROC curves can be used to assess the specificity and sensitivity possible with different predicted y-value thresholds for a PLSDA model. Inputs are a PLSDA model model, an optional index into the y-columns used in the model ycol [default = all columns], and an options structure. Output is a dataset with the sensitivity/specificity data roc. | ROC curves can be used to assess the specificity and sensitivity possible with different predicted y-value thresholds for a PLSDA model. Inputs are a PLSDA model model, an optional index into the y-columns used in the model ycol [default = all columns], and an options structure. Output is a dataset with the sensitivity/specificity data roc. | ||
===Options=== | ===Options=== | ||
* plots : [ 'none' | {'final'}] governs plotting on/off | * '''plots''' : [ 'none' | {'final'}] governs plotting on/off | ||
===See Also=== | ===See Also=== | ||
[[discrimprob]], [[plsda]], [[plsdthres]], [[simca]] | [[discrimprob]], [[plsda]], [[plsdthres]], [[simca]] | ||
Revision as of 19:57, 2 September 2008
Purpose
Calculate and display ROC curves for PLSDA model.
Synopsis
- roc = plsdaroc(model,ycol,options)
Description
ROC curves can be used to assess the specificity and sensitivity possible with different predicted y-value thresholds for a PLSDA model. Inputs are a PLSDA model model, an optional index into the y-columns used in the model ycol [default = all columns], and an options structure. Output is a dataset with the sensitivity/specificity data roc.
Options
- plots : [ 'none' | {'final'}] governs plotting on/off