plsdthres
Purpose
Bayesian threshold determination for PLS Discriminate
Analysis.
Synopsis
[threshold,misclassed,prob] = plsdthres(model,options)
[threshold,misclassed,prob] = plsdthres(y,ypred,options)
Description
PLSDTHRES
uses the distribution of calibration-sample predictions obtained from a PLS
model built for two or more logical classes to automatically determine a
threshold value which will best split those classes with the least probability
of false classifications for future predictions. It is assumed that the
predicted values for each class are approximately normally distributed. The
calibration can contain more than 2 classes, in which case thresholds to
distinguish all classes will be determined. It is assumed that with more than 2
classes the primary misclassification threat is from the adjacent class(es).
Inputs
y = measured Y-block values used in PLS, and
ypred = PLS predicted Y values for calibration samples.
model
= a PLS/PLSDA model structure from which y and ypred should be obtained
automatically.
Outputs
threshold
= [], vector of
thresholds. If y
consists of more than two classes, threshold will be a vector giving the upper
bound y-value for each class.
misclassed
= [], array
containing the fraction of misclassifications for each class (rows): Column 1 =
false negatives and Column 2 = false positives.
prob = lookup matrix of predicted y (column 1) vs. probability
of each class (columns 2 to end).
Options
options
is a structure array with the following fields:
display: [
{'on'} | 'off' ], governs level of display,
plots: ['none' | 'final' | {'auto'} |], governs plotting behavior, 'auto' makes plots if no
output is requested {default},
cost: [], vector of logarithmic cost biases for each class
in y, cost is used to bias against
misclassification of a particular class or classes {default = [] uses all zeros i.e. equal
cost}.
prior: [], vector of prior probabilities of observing each
class. If any class prior is Inf, the frequency of observation of that class in
the calibration is used as its prior probability. If all priors are Inf, this
has the effect of providing the fewest incorrect predictions assuming that the
probability of observing a given class in future samples is similar to the
frequency that class in the calibration set. {default = [] uses all ones i.e. equal priors.}
See Also
class2logical, crossval, discrimprob, plsda, plsdaroc, simca
