Code covered by the BSD License
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DAP(varargin)
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DSEst(data, maxrep, DSdim)
Donoho-Stahel robust dispersion/location estimation method for gaussian data
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DSWeightings(directions, DSPa...
Computes maximum wheightings by data projection through unit norm vectors.
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DataImport(samples, valid, da...
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Delta=computeDeltaMatrix(TLis...
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ExitSession(hObject)
Exit menu
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FList=compute_F_matrices(B, m...
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ImportDonohoStahelDimensions
Loads the the Donoho-Stahel Estimator's dimensional constants data file
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InputInfo(samples, preDefInpu...
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Q=galg(TList, numGrp)
G algorithm
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SEst(data, maxrep, bdp)
Computes biweight multivariate S-estimator for location/dispersion with algorithm of
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T=computeTMatrix(TList, numGr...
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TList=compute_T_matrices(FLis...
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Tbsb(c,p);
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Tbsc(alpha,p);
constant for Tukey Biweight S
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classifyRuleCoef(m_model, m_d...
Computes the Classification Rule Coefficients
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computeCPCModel(covList, m_co...
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computeCPCSigmaList(B, LList,...
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computeEntries_F_matrices(FLi...
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computeEntries_F_matrices(m, ...
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computeInvCovList(covList, nu...
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computeLikelihoodRatioTest(mo...
testStatistic = testStatistic + 2 * (m_constants.numGrp - 1) * m_constants.numVar; % AIC for Flury Decomposition of Chi Square
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computeLinearModel(covList, m...
Linear Model Algorithm
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computeProportionalModel(covL...
Proportional Covariance Matrices Algorithm
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crossValidation(model, m_dap)
Cross-Validation
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discrimRuleCoef(m_model, m_da...
Computes the Discrimination Rule Coefficients, that is, the Discriminant
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dispList(list,outputStr)
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displayCPCModel(cpcCovList, L...
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displayCellList(model, cellLi...
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displayCoefCellList(model, ce...
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displayCoefList(coefList)
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displayLikelihoodRatioTest(pV...
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displayLinearModel(pooledCov,...
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displayProportionalModel(prop...
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displayQuadraticModel(quadrat...
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falg(covList, numVar, numGrp)
F algorithm
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huber(weightings, locationCon...
Huber's function
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jvec=ranpn1(n,p1)
Returns the subsamples' indexes
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ksiint(c,s,p);
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linearCVCoefficent(D, dataEle...
Linear Cross Validation Coefficent
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linearRule(location, D, dataE...
Linear Discriminant Rule
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linearSampleClassification(di...
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mahalanobis(dat,meanvct,covma...
Computes the mahalanobis distances.
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plotClassification(m_model, c...
Plots the classified data along with discrimination regions
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plotClassificationBoundaries(...
Plots the modeled concentration bounderies for the groups
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plotDiscriminationRegions(m_m...
Plots the discrimination regions for the groups
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plotMalclassification(m_model...
Plots the malclassified data along with discrimination regions
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plotNormalProbability(m_dap)
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psibiweight(x,c)
Computes Tukey's biweight psi function with constant c for all
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quadraticCVCoefficent(D, data...
Quadratic Cross Validation Coefficent
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quadraticRule(location, D, da...
Quadratic Discriminant Rule
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quadraticSampleClassification...
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randomset(tot,nel,seed)
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randomset1(tot,nel)
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rhobiweight(x,c)
Computes Tukey's biweight rho function with constant c for all
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sestck(x,start,c,k,tol)
Computes Tukey's biweight objectief function (scale) corresponding
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unitVectors(vecDim, numVector...
Computes unit norm vectors either from a gaussian distribution for
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validateData(datasets, sample...
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View all files
Discriminant Analysis Programme
by Bartolomeu Rabacal
25 Aug 2008
(Updated 20 Nov 2008)
Discrimination and Classification of data to and from groups with classical/robust estimation
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| File Information |
| Description |
The purpose of Discriminant Analysis Programme (DAP) is to facilitate discrimination and classification of (to be) grouped data with robust estimation- and modeled structures for the covariances in a one-go software. The robust estimation methods are the S-estimator and Donoho-Stahel estimator. The included covariance structure models are Common Principal Components, Proportional, classical Quadratic and Linear ones; Hypothesis Testing is performed for these fitted models except for arbitrary covariances. The Discriminant Rules are found and the Classification Rules Coefficients are computed after the given training data sample and used to classify the classification sample data, if provided. They are also used to find malclassified data elements by Cross-Validation (Leave-One-Out) method of the training sample, being recomputed for each element.
It includes complementary graphical outputs for bivariate data such as normality plots and group separation. |
| MATLAB release |
MATLAB 6.5 (R13)
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| Updates |
| 20 Nov 2008 |
The added Discriminant Regions are defined by Separatory Hyperplanes or Hypersurfaces computed by the Discriminant Rules Coefficients. The old Discriminant Rules are now the Classification ones. Improved/corrected graphical plot headers.
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