Plotscores

Plotscores

Purpose

Extract and display scores information from model.

Synopsis

a = plotscores(modl,options)

a = plotscores(modl,pred,options)

a = plotscores(scores,labels,classes)

Description

Given a standard model structure, relevant scores information (e.g. labels) is collected and passed to plotgui for plotting.

If no output is requested, then plotscores initiates an interactive plotting utility to make scores plots. If an output is requested, no plots are made, and the output a is a dataset object containing the scores, labels, and other information that is needed for subsequent plotting.

Inputs

• modl = a standard model structure. plotscores will extract the scores, labels, and and other information that is needed for plotting.
• pred = a standard prediction structure (e.g. output from PCA call: pred = pca(x,modl,options);)
• scores = a M by K scores matrix (class "double").

Optional Inputs

• labels is a character or cell array with M rows containing sample labels
• classes a vector with M integer elements of class identifiers.
• Optional input options is discussed below.

Outputs

• a = a dataset containing scores and label information that can be passed to plotgui.

Options

options = a structure array with the following fields:

• plots: ['none' | 'final' | {'auto'} |], governs plotting behavior
  • 'auto' makes plots if no output is requested {default},
• figure: [],governs where plots are made
  • when figure = [] plots are made in a new figure window {default}, this can also be a valid figure number (i.e. figure handle)
• sct: [ 0 | {1} ], tells whether to plot cal (modl scores) with test (pred scores), sct = 1 plots original calibration data with prediction set {default = 1}.

The default options can be retreived using: options = plotscores('options');.

See Also

analysis, knnscoredistance, mcr, modelstruct, modelviewer, mpca, pca, pcr, plotgui, plotscores_defaults, plotloads, pls