Discriminant analysis is a classification method. It assumes that different classes generate data based on different Gaussian distributions.

To train (create) a classifier, the fitting function estimates the parameters of a Gaussian distribution for each class (see Creating a Classifier Using fitcdiscr).

To predict the classes of new data, the trained classifier finds the class with the smallest misclassification cost (see How the predict Method Classifies).

Linear discriminant analysis is also known as the Fisher discriminant, named for its inventor, Sir R. A. Fisher [2].

This example shows how to train a basic discriminant analysis classifier to classify irises in Fisher's iris data.

Load the data.

```
load fisheriris
```

Create a default (linear) discriminant analysis classifier.

MdlLinear = fitcdiscr(meas,species);

To visualize the classification boundaries of a 2-D linear classification of the data, see Create and Visualize Discriminant Analysis Classifier.

Classify an iris with average measurements.

meanmeas = mean(meas); meanclass = predict(MdlLinear,meanmeas)

meanclass = cell 'versicolor'

Create a quadratic classifier.

MdlQuadratic = fitcdiscr(meas,species,'DiscrimType','quadratic');

To visualize the classification boundaries of a 2-D quadratic classification of the data, see Create and Visualize Discriminant Analysis Classifier.

Classify an iris with average measurements using the quadratic classifier.

meanclass2 = predict(MdlQuadratic,meanmeas)

meanclass2 = cell 'versicolor'

The model for discriminant analysis is:

Each class (

`Y`

) generates data (`X`

) using a multivariate normal distribution. In other words, the model assumes`X`

has a Gaussian mixture distribution (`gmdistribution`

).For linear discriminant analysis, the model has the same covariance matrix for each class; only the means vary.

For quadratic discriminant analysis, both means and covariances of each class vary.

Under this modeling assumption, `fitcdiscr`

infers
the mean and covariance parameters of each class.

For linear discriminant analysis, it computes the sample mean of each class. Then it computes the sample covariance by first subtracting the sample mean of each class from the observations of that class, and taking the empirical covariance matrix of the result.

For quadratic discriminant analysis, it computes the sample mean of each class. Then it computes the sample covariances by first subtracting the sample mean of each class from the observations of that class, and taking the empirical covariance matrix of each class.

The `fit`

method does not use prior probabilities
or costs for fitting.

`fitcdiscr`

constructs weighted
classifiers using the following scheme. Suppose *M* is
an *N*-by-*K* class membership matrix:

*M _{nk}* = 1 if
observation

The estimate of the class mean for unweighted data is

$${\widehat{\mu}}_{k}=\frac{{{\displaystyle \sum}}_{n=1}^{N}{M}_{nk}{x}_{n}}{{{\displaystyle \sum}}_{n=1}^{N}{M}_{nk}}.$$

For weighted data with positive weights *w _{n}*,
the natural generalization is

${\widehat{\mu}}_{k}=\frac{{{\displaystyle \sum}}_{n=1}^{N}{M}_{nk}{w}_{n}{x}_{n}}{{{\displaystyle \sum}}_{n=1}^{N}{M}_{nk}{w}_{n}}.$

The unbiased estimate of the pooled-in covariance matrix for unweighted data is

$\widehat{\Sigma}=\frac{{{\displaystyle \sum}}_{n=1}^{N}{{\displaystyle \sum}}_{k=1}^{K}{M}_{nk}\left({x}_{n}-{\widehat{\mu}}_{k}\right){\left({x}_{n}-{\widehat{\mu}}_{k}\right)}^{T}}{N-K}.$

For quadratic discriminant analysis, `fitcdiscr`

uses *K* = 1.

For weighted data, assuming the weights sum to 1, the unbiased estimate of the pooled-in covariance matrix is

$\widehat{\Sigma}=\frac{{{\displaystyle \sum}}_{n=1}^{N}{{\displaystyle \sum}}_{k=1}^{K}{M}_{nk}{w}_{n}\left({x}_{n}-{\widehat{\mu}}_{k}\right){\left({x}_{n}-{\widehat{\mu}}_{k}\right)}^{T}}{1-{{\displaystyle \sum}}_{k=1}^{K}\frac{{W}_{k}^{\left(2\right)}}{{W}_{k}}},$

where

${W}_{k}={\displaystyle {\sum}_{n=1}^{N}{M}_{nk}{w}_{n}}$ is the sum of the weights for class

*k*.${W}_{k}^{\left(2\right)}={\displaystyle {\sum}_{n=1}^{N}{M}_{nk}{w}_{n}^{2}}$ is the sum of squared weights per class.

`predict`

uses three quantities to classify
observations: posterior
probability, prior
probability, and cost.

`predict`

classifies so as to minimize the expected
classification cost:

$$\widehat{y}=\underset{y=1,\mathrm{...},K}{\mathrm{arg}\mathrm{min}}{\displaystyle \sum _{k=1}^{K}\widehat{P}\left(k|x\right)C\left(y|k\right)},$$

where

$$\widehat{y}$$ is the predicted classification.

*K*is the number of classes.$$\widehat{P}\left(k|x\right)$$ is the posterior probability of class

*k*for observation*x*.$$C\left(y|k\right)$$ is the cost of classifying an observation as

*y*when its true class is*k*.

The space of `X`

values divides into regions
where a classification `Y`

is a particular value.
The regions are separated by straight lines for linear discriminant
analysis, and by conic sections (ellipses, hyperbolas, or parabolas)
for quadratic discriminant analysis. For a visualization of these
regions, see Create and Visualize Discriminant Analysis Classifier.

The posterior probability that a point *x* belongs
to class *k* is the product of the prior probability and
the multivariate normal density. The density function of the multivariate
normal with mean *μ _{k}* and
covariance Σ

$$P\left(x|k\right)=\frac{1}{{\left(2\pi \left|{\Sigma}_{k}\right|\right)}^{1/2}}\mathrm{exp}\left(-\frac{1}{2}{\left(x-{\mu}_{k}\right)}^{T}{\Sigma}_{k}^{-1}\left(x-{\mu}_{k}\right)\right),$$

where $$\left|{\Sigma}_{k}\right|$$ is the determinant of Σ* _{k}*,
and $${\Sigma}_{k}^{-1}$$ is the inverse matrix.

Let *P*(*k*) represent the
prior probability of class *k*. Then the posterior
probability that an observation *x* is of class *k* is

$$\widehat{P}\left(k|x\right)=\frac{P\left(x|k\right)P\left(k\right)}{P\left(x\right)},$$

where *P*(*x*) is a normalization
constant, namely, the sum over *k* of *P*(*x*|*k*)*P*(*k*).

The prior probability is one of three choices:

`'uniform'`

— The prior probability of class`k`

is 1 over the total number of classes.`'empirical'`

— The prior probability of class`k`

is the number of training samples of class`k`

divided by the total number of training samples.A numeric vector — The prior probability of class

`k`

is the`j`

th element of the`Prior`

vector. See`fitcdiscr`

.

After creating a classifier `obj`

, you can
set the prior using dot notation:

obj.Prior = v;

where `v`

is a vector of positive elements
representing the frequency with which each element occurs. You do
not need to retrain the classifier when you set a new prior.

There are two costs associated with discriminant analysis classification: the true misclassification cost per class, and the expected misclassification cost per observation.

**True Misclassification Cost per Class. **`Cost(i,j)`

is the cost of classifying an observation
into class `j`

if its true class is `i`

.
By default, `Cost(i,j)=1`

if `i~=j`

,
and `Cost(i,j)=0`

if `i=j`

. In other
words, the cost is `0`

for correct classification,
and `1`

for incorrect classification.

You can set any cost matrix you like when creating a classifier.
Pass the cost matrix in the `Cost`

name-value pair
in `fitcdiscr`

.

After you create a classifier `obj`

, you can
set a custom cost using dot notation:

obj.Cost = B;

`B`

is a square matrix of size `K`

-by-`K`

when
there are `K`

classes. You do not need to retrain
the classifier when you set a new cost.

**Expected Misclassification Cost per Observation. **Suppose you have `Nobs`

observations that you
want to classify with a trained discriminant analysis classifier `obj`

.
Suppose you have `K`

classes. You place the observations
into a matrix `Xnew`

with one observation per row.
The command

[label,score,cost] = predict(obj,Xnew)

returns, among other outputs, a cost matrix of size `Nobs`

-by-`K`

.
Each row of the cost matrix contains the expected (average) cost of
classifying the observation into each of the `K`

classes. `cost(n,k)`

is

$$\sum _{i=1}^{K}\widehat{P}\left(i|Xnew(n)\right)C\left(k|i\right)},$$

where

*K*is the number of classes.$$\widehat{P}\left(i|Xnew(n)\right)$$ is the posterior probability of class

*i*for observation*Xnew*(*n*).$$C\left(k|i\right)$$ is the cost of classifying an observation as

*k*when its true class is*i*.

This example shows how to perform linear and quadratic classification of Fisher iris data.

Load the sample data.

```
load fisheriris
```

The column vector, `species`

, consists of iris flowers of three different species, setosa, versicolor, virginica. The double matrix `meas`

consists of four types of measurements on the flowers, the length and width of sepals and petals in centimeters, respectively.

Use petal length (third column in `meas`

) and petal width (fourth column in `meas`

) measurements. Save these as variables PL and PW, respectively.

PL = meas(:,3); PW = meas(:,4);

Plot the data, showing the classification, that is, create a scatter plot of the measurements, grouped by species.

h1 = gscatter(PL,PW,species,'krb','ov^',[],'off'); h1(1).LineWidth = 2; h1(2).LineWidth = 2; h1(3).LineWidth = 2; legend('Setosa','Versicolor','Virginica','Location','best') hold on

Create a linear classifier.

X = [PL,PW]; MdlLinear = fitcdiscr(X,species);

Retrieve the coefficients for the linear boundary between the second and third classes.

MdlLinear.ClassNames([2 3]) K = MdlLinear.Coeffs(2,3).Const; L = MdlLinear.Coeffs(2,3).Linear;

ans = 2×1 cell array 'versicolor' 'virginica'

Plot the curve that separates the second and third classes

```
f = @(x1,x2) K + L(1)*x1 + L(2)*x2;
h2 = ezplot(f,[.9 7.1 0 2.5]);
h2.Color = 'r';
h2.LineWidth = 2;
```

Retrieve the coefficients for the linear boundary between the first and second classes.

MdlLinear.ClassNames([1 2]) K = MdlLinear.Coeffs(1,2).Const; L = MdlLinear.Coeffs(1,2).Linear;

ans = 2×1 cell array 'setosa' 'versicolor'

Plot the curve that separates the first and second classes.

f = @(x1,x2) K + L(1)*x1 + L(2)*x2; h3 = ezplot(f,[.9 7.1 0 2.5]); h3.Color = 'k'; h3.LineWidth = 2; axis([.9 7.1 0 2.5]) xlabel('Petal Length') ylabel('Petal Width') title('{\bf Linear Classification with Fisher Training Data}')

Create a quadratic discriminant classifier.

MdlQuadratic = fitcdiscr(X,species,'DiscrimType','quadratic');

Remove the linear boundaries from the plot.

delete(h2); delete(h3);

Retrieve the coefficients for the quadratic boundary between the second and third classes.

MdlQuadratic.ClassNames([2 3]) K = MdlQuadratic.Coeffs(2,3).Const; L = MdlQuadratic.Coeffs(2,3).Linear; Q = MdlQuadratic.Coeffs(2,3).Quadratic;

ans = 2×1 cell array 'versicolor' 'virginica'

Plot the curve that separates the second and third classes

f = @(x1,x2) K + L(1)*x1 + L(2)*x2 + Q(1,1)*x1.^2 + ... (Q(1,2)+Q(2,1))*x1.*x2 + Q(2,2)*x2.^2; h2 = ezplot(f,[.9 7.1 0 2.5]); h2.Color = 'r'; h2.LineWidth = 2;

Retrieve the coefficients for the quadratic boundary between the first and second classes.

MdlQuadratic.ClassNames([1 2]) K = MdlQuadratic.Coeffs(1,2).Const; L = MdlQuadratic.Coeffs(1,2).Linear; Q = MdlQuadratic.Coeffs(1,2).Quadratic;

ans = 2×1 cell array 'setosa' 'versicolor'

Plot the curve that separates the first and second and classes.

f = @(x1,x2) K + L(1)*x1 + L(2)*x2 + Q(1,1)*x1.^2 + ... (Q(1,2)+Q(2,1))*x1.*x2 + Q(2,2)*x2.^2; h3 = ezplot(f,[.9 7.1 0 1.02]); % Plot the relevant portion of the curve. h3.Color = 'k'; h3.LineWidth = 2; axis([.9 7.1 0 2.5]) xlabel('Petal Length') ylabel('Petal Width') title('{\bf Quadratic Classification with Fisher Training Data}') hold off

Discriminant analysis needs data sufficient to fit Gaussian
models with invertible covariance matrices. If your data is not sufficient
to fit such a model uniquely, `fitcdiscr`

fails.
This section shows methods for handling failures.

"Pseudo" discriminants never fail, because they
use the pseudoinverse of the covariance matrix Σ |

**Example: Singular Covariance Matrix. **When the covariance matrix of the fitted classifier is singular, `fitcdiscr`

can fail:

load popcorn X = popcorn(:,[1 2]); X(:,3) = 0; % a zero-variance column Y = popcorn(:,3); ppcrn = fitcdiscr(X,Y); Error using ClassificationDiscriminant (line 635) Predictor x3 has zero variance. Either exclude this predictor or set 'discrimType' to 'pseudoLinear' or 'diagLinear'. Error in classreg.learning.FitTemplate/fit (line 243) obj = this.MakeFitObject(X,Y,W,this.ModelParameters,fitArgs{:}); Error in fitcdiscr (line 296) this = fit(temp,X,Y);

To proceed with linear discriminant analysis, use a `pseudoLinear`

or `diagLinear`

discriminant
type:

ppcrn = fitcdiscr(X,Y,... 'discrimType','pseudoLinear'); meanpredict = predict(ppcrn,mean(X)) meanpredict = 3.5000

There are six types of discriminant analysis classifiers: linear
and quadratic, with *diagonal* and *pseudo* variants
of each type.

To obtain a quadratic classifier even when your covariance matrix
is singular, set obj = fitcdiscr(X,Y,'DiscrimType','pseudoQuadratic') % or 'diagQuadratic' |

Choose a classifier type by setting the `discrimType`

name-value
pair to one of:

`'linear'`

(default) — Estimate one covariance matrix for all classes.`'quadratic'`

— Estimate one covariance matrix for each class.`'diagLinear'`

— Use the diagonal of the`'linear'`

covariance matrix, and use its pseudoinverse if necessary.`'diagQuadratic'`

— Use the diagonals of the`'quadratic'`

covariance matrices, and use their pseudoinverses if necessary.`'pseudoLinear'`

— Use the pseudoinverse of the`'linear'`

covariance matrix if necessary.`'pseudoQuadratic'`

— Use the pseudoinverses of the`'quadratic'`

covariance matrices if necessary.

`fitcdiscr`

can fail for
the `'linear'`

and `'quadratic'`

classifiers.
When it fails, it returns an explanation, as shown in Deal with Singular Data.

`fitcdiscr`

always succeeds
with the diagonal and pseudo variants. For information about pseudoinverses,
see `pinv`

.

You can set the discriminant type using dot notation after constructing a classifier:

obj.DiscrimType = 'discrimType'

You can change between linear types or between quadratic types, but cannot change between a linear and a quadratic type.

The *resubstitution error* is the difference
between the response training data and the predictions the classifier
makes of the response based on the input training data. If the resubstitution
error is high, you cannot expect the predictions of the classifier
to be good. However, having low resubstitution error does not guarantee
good predictions for new data. Resubstitution error is often an overly
optimistic estimate of the predictive error on new data.

The *confusion matrix* shows how many errors,
and which types, arise in resubstitution. When there are `K`

classes,
the confusion matrix `R`

is a `K`

-by-`K`

matrix
with

`R(i,j)`

= the number of observations
of class `i`

that the classifier predicts to be of
class `j`

.

**Example: Resubstitution Error of a Discriminant Analysis Classifier. **Examine the resubstitution error of the default discriminant
analysis classifier for the Fisher iris data:

load fisheriris obj = fitcdiscr(meas,species); resuberror = resubLoss(obj) resuberror = 0.0200

The resubstitution error is very low, meaning `obj`

classifies
nearly all the Fisher iris data correctly. The total number of misclassifications
is:

resuberror * obj.NumObservations ans = 3.0000

To see the details of the three misclassifications, examine the confusion matrix:

R = confusionmat(obj.Y,resubPredict(obj)) R = 50 0 0 0 48 2 0 1 49 obj.ClassNames ans = 'setosa' 'versicolor' 'virginica'

`R(1,:) = [50 0 0]`

means`obj`

classifies all 50 setosa irises correctly.`R(2,:) = [0 48 2]`

means`obj`

classifies 48 versicolor irises correctly, and misclassifies two versicolor irises as virginica.`R(3,:) = [0 1 49]`

means`obj`

classifies 49 virginica irises correctly, and misclassifies one virginica iris as versicolor.

Typically, discriminant analysis classifiers are robust and do not exhibit overtraining when the number of predictors is much less than the number of observations. Nevertheless, it is good practice to cross validate your classifier to ensure its stability.

**Cross Validating a Discriminant Analysis Classifier **

This example shows how to perform five-fold cross validation of a quadratic discriminant analysis classifier.

Load the sample data.

```
load fisheriris
```

Create a quadratic discriminant analysis classifier for the data.

quadisc = fitcdiscr(meas,species,'DiscrimType','quadratic');

Find the resubstitution error of the classifier.

qerror = resubLoss(quadisc)

qerror = 0.0200

The classifier does an excellent job. Nevertheless, resubstitution error can be an optimistic estimate of the error when classifying new data. So proceed to cross validation.

Create a cross-validation model.

```
cvmodel = crossval(quadisc,'kfold',5);
```

Find the cross-validation loss for the model, meaning the error of the out-of-fold observations.

cverror = kfoldLoss(cvmodel)

cverror = 0.0200

The cross-validated loss is as low as the original resubstitution loss. Therefore, you can have confidence that the classifier is reasonably accurate.

Sometimes you want to avoid certain misclassification errors more than others. For example, it might be better to have oversensitive cancer detection instead of undersensitive cancer detection. Oversensitive detection gives more false positives (unnecessary testing or treatment). Undersensitive detection gives more false negatives (preventable illnesses or deaths). The consequences of underdetection can be high. Therefore, you might want to set costs to reflect the consequences.

Similarly, the training data `Y`

can have a
distribution of classes that does not represent their true frequency.
If you have a better estimate of the true frequency, you can include
this knowledge in the classification `Prior`

property.

**Example: Setting Custom Misclassification Costs. **Consider the Fisher iris data. Suppose that the cost of classifying
a versicolor iris as virginica is 10 times as large as making any
other classification error. Create a classifier from the data, then
incorporate this cost and then view the resulting classifier.

Load the Fisher iris data and create a default (linear) classifier as in Example: Resubstitution Error of a Discriminant Analysis Classifier:

load fisheriris obj = fitcdiscr(meas,species); resuberror = resubLoss(obj) resuberror = 0.0200 R = confusionmat(obj.Y,resubPredict(obj)) R = 50 0 0 0 48 2 0 1 49 obj.ClassNames ans = 'setosa' 'versicolor' 'virginica'

`R(2,:) = [0 48 2]`

means`obj`

classifies 48 versicolor irises correctly, and misclassifies two versicolor irises as virginica.Change the cost matrix to make fewer mistakes in classifying versicolor irises as virginica:

obj.Cost(2,3) = 10; R2 = confusionmat(obj.Y,resubPredict(obj)) R2 = 50 0 0 0 50 0 0 7 43

`obj`

now classifies all versicolor irises correctly, at the expense of increasing the number of misclassifications of virginica irises from`1`

to`7`

.

**Example: Setting Alternative Priors. **Consider the Fisher iris data. There are 50 irises of each kind
in the data. Suppose that, in a particular region, you have historical
data that shows virginica are five times as prevalent as the other
kinds. Create a classifier that incorporates this information.

Load the Fisher iris data and make a default (linear) classifier as in Example: Resubstitution Error of a Discriminant Analysis Classifier:

load fisheriris obj = fitcdiscr(meas,species); resuberror = resubLoss(obj) resuberror = 0.0200 R = confusionmat(obj.Y,resubPredict(obj)) R = 50 0 0 0 48 2 0 1 49 obj.ClassNames ans = 'setosa' 'versicolor' 'virginica'

`R(3,:) = [0 1 49]`

means`obj`

classifies 49 virginica irises correctly, and misclassifies one virginica iris as versicolor.Change the prior to match your historical data, and examine the confusion matrix of the new classifier:

obj.Prior = [1 1 5]; R2 = confusionmat(obj.Y,resubPredict(obj)) R2 = 50 0 0 0 46 4 0 0 50

The new classifier classifies all virginica irises correctly, at the expense of increasing the number of misclassifications of versicolor irises from

`2`

to`4`

.

This example shows how to make a more robust and simpler model by trying to remove predictors without hurting the predictive power of the model. This is especially important when you have many predictors in your data. Linear discriminant analysis uses the two regularization parameters, Gamma and Delta (see Definitions), to identify and remove redundant predictors. The `ClassificationDiscriminant.cvshrink`

method helps identify appropriate settings for these parameters.

**Load data and create a classifier.**

Create a linear discriminant analysis classifier for the `ovariancancer`

data. Set the `SaveMemory`

and `FillCoeffs`

name-value pair arguments to keep the resulting model reasonably small. For computational ease, this example uses a random subset of about one third of the predictors to train the classifier.

load ovariancancer rng(1); % For reproducibility numPred = size(obs,2); obs = obs(:,randsample(numPred,ceil(numPred/3))); Mdl = fitcdiscr(obs,grp,'SaveMemory','on','FillCoeffs','off');

**Cross validate the classifier.**

Use 25 levels of `Gamma`

and 25 levels of `Delta`

to search for good parameters. This search is time consuming. Set `Verbose`

to `1`

to view the progress.

[err,gamma,delta,numpred] = cvshrink(Mdl,... 'NumGamma',24,'NumDelta',24,'Verbose',1);

Done building cross-validated model. Processing Gamma step 1 out of 25. Processing Gamma step 2 out of 25. Processing Gamma step 3 out of 25. Processing Gamma step 4 out of 25. Processing Gamma step 5 out of 25. Processing Gamma step 6 out of 25. Processing Gamma step 7 out of 25. Processing Gamma step 8 out of 25. Processing Gamma step 9 out of 25. Processing Gamma step 10 out of 25. Processing Gamma step 11 out of 25. Processing Gamma step 12 out of 25. Processing Gamma step 13 out of 25. Processing Gamma step 14 out of 25. Processing Gamma step 15 out of 25. Processing Gamma step 16 out of 25. Processing Gamma step 17 out of 25. Processing Gamma step 18 out of 25. Processing Gamma step 19 out of 25. Processing Gamma step 20 out of 25. Processing Gamma step 21 out of 25. Processing Gamma step 22 out of 25. Processing Gamma step 23 out of 25. Processing Gamma step 24 out of 25. Processing Gamma step 25 out of 25.

**Examine the quality of the regularized classifiers.**

Plot the number of predictors against the error.

figure; plot(err,numpred,'k.') xlabel('Error rate'); ylabel('Number of predictors');

Examine the lower-left part of the plot more closely.

axis([0 .1 0 1000])

There is a clear tradeoff between lower number of predictors and lower error.

**Choose an optimal tradeoff between model size and accuracy.**

Multiple pairs of `Gamma`

and `Delta`

values produce about the same minimal error. Display the indices of these pairs and their values.

minerr = min(min(err)) [p,q] = find(err < minerr + 1e-4); % Subscripts of err producing minimal error numel(p) idx = sub2ind(size(delta),p,q); % Convert from subscripts to linear indices [gamma(p) delta(idx)]

minerr = 0.0139 ans = 4 ans = 0.7202 0.1145 0.7602 0.1131 0.8001 0.1128 0.8001 0.1410

These points have as few as 20% of the total predictors that have nonzero coefficients in the model.

numpred(idx)/ceil(numPred/3)*100

ans = 39.8051 38.9805 36.8066 28.7856

To further lower the number of predictors, you must accept larger error rates. For example, to choose the `Gamma`

and `Delta`

that give the lowest error rate with 200 or fewer predictors.

low200 = min(min(err(numpred <= 200))); lownum = min(min(numpred(err == low200))); [low200 lownum]

ans = 0.0185 173.0000

You need 195 predictors to achieve an error rate of 0.0185, and this is the lowest error rate among those that have 200 predictors or fewer.

Display the `Gamma`

and `Delta`

that achieve this error/number of predictors.

[r,s] = find((err == low200) & (numpred == lownum)); [gamma(r); delta(r,s)]

ans = 0.6403 0.2399

**Set the regularization parameters.**

To set the classifier with these values of `Gamma`

and `Delta`

, use dot notation.

Mdl.Gamma = gamma(r); Mdl.Delta = delta(r,s);

**Heat map plot**

To compare the `cvshrink`

calculation to that in Guo, Hastie, and Tibshirani [3], plot heat maps of error and number of predictors against `Gamma`

and the index of the `Delta`

parameter. (The `Delta`

parameter range depends on the value of the `Gamma`

parameter. So to get a rectangular plot, use the `Delta`

index, not the parameter itself.)

% Create the Delta index matrix indx = repmat(1:size(delta,2),size(delta,1),1); figure subplot(1,2,1) imagesc(err); colorbar; colormap('jet') title 'Classification error'; xlabel 'Delta index'; ylabel 'Gamma index'; subplot(1,2,2) imagesc(numpred); colorbar; title 'Number of predictors in the model'; xlabel 'Delta index' ; ylabel 'Gamma index' ;

You see the best classification error when `Delta`

is small, but fewest predictors when `Delta`

is large.

Discriminant analysis assumes that the data comes from a Gaussian mixture model (see Creating a Classifier Using fitcdiscr). If the data appears to come from a Gaussian mixture model, you can expect discriminant analysis to be a good classifier. Furthermore, the default linear discriminant analysis assumes that all class covariance matrices are equal. This section shows methods to check these assumptions:

The Bartlett test (see Box [1]) checks equality of the covariance matrices of the various
classes. If the covariance matrices are equal, the test indicates
that linear discriminant analysis is appropriate. If not, consider
using quadratic discriminant analysis, setting the `DiscrimType`

name-value
pair to `'quadratic'`

in `fitcdiscr`

.

The Bartlett test assumes normal (Gaussian) samples, where neither the means nor covariance matrices are known. To determine whether the covariances are equal, compute the following quantities:

Sample covariance matrices per class

*σ*, 1 ≤_{i}*i*≤*k*, where*k*is the number of classes.Pooled-in covariance matrix

*σ*.Test statistic

*V*:$$V=\left(n-k\right)\mathrm{log}\left(\left|\Sigma \right|\right)-{\displaystyle \sum _{i=1}^{k}\left({n}_{i}-1\right)\mathrm{log}\left(\left|{\Sigma}_{i}\right|\right)}$$

where

*n*is the total number of observations, and*n*is the number of observations in class_{i}*i*, and |Σ| means the determinant of the matrix Σ.Asymptotically, as the number of observations in each class

*n*become large,_{i}*V*is distributed approximately*χ*^{2}with*kd*(*d*+ 1)/2 degrees of freedom, where*d*is the number of predictors (number of dimensions in the data).

The Bartlett test is to check whether *V* exceeds
a given percentile of the *χ*^{2} distribution
with *kd*(*d* + 1)/2 degrees of
freedom. If it does, then reject the hypothesis that the covariances
are equal.

**Example: Bartlett Test for Equal Covariance Matrices. **Check whether the Fisher iris data is well modeled by a single
Gaussian covariance, or whether it would be better to model it as
a Gaussian mixture.

load fisheriris; prednames = {'SepalLength','SepalWidth','PetalLength','PetalWidth'}; L = fitcdiscr(meas,species,'PredictorNames',prednames); Q = fitcdiscr(meas,species,'PredictorNames',prednames,'DiscrimType','quadratic'); D = 4; % Number of dimensions of X Nclass = [50 50 50]; N = L.NumObservations; K = numel(L.ClassNames); SigmaQ = Q.Sigma; SigmaL = L.Sigma; logV = (N-K)*log(det(SigmaL)); for k=1:K logV = logV - (Nclass(k)-1)*log(det(SigmaQ(:,:,k))); end nu = (K-1)*D*(D+1)/2; pval = 1 - chi2cdf(logV,nu)

pval = 0

The Bartlett test emphatically rejects the hypothesis of equal
covariance matrices. If `pval`

had been greater than `0.05`

,
the test would not have rejected the hypothesis. The result indicates
to use quadratic discriminant analysis, as opposed to linear discriminant
analysis.

A Q-Q plot graphically shows whether an empirical distribution is close to a theoretical distribution. If the two are equal, the Q-Q plot lies on a 45° line. If not, the Q-Q plot strays from the 45° line.

**Check Q-Q Plots for Linear and Quadratic Discriminants. **For linear discriminant analysis, use a single covariance matrix
for all classes.

load fisheriris; prednames = {'SepalLength','SepalWidth','PetalLength','PetalWidth'}; L = fitcdiscr(meas,species,'PredictorNames',prednames); N = L.NumObservations; K = numel(L.ClassNames); mahL = mahal(L,L.X,'ClassLabels',L.Y); D = 4; expQ = chi2inv(((1:N)-0.5)/N,D); % expected quantiles [mahL,sorted] = sort(mahL); % sorted obbserved quantiles figure; gscatter(expQ,mahL,L.Y(sorted),'bgr',[],[],'off'); legend('virginica','versicolor','setosa','Location','NW'); xlabel('Expected quantile'); ylabel('Observed quantile'); line([0 20],[0 20],'color','k');

Overall, the agreement between the expected and observed quantiles
is good. Look at the right half of the plot. The deviation of the
plot from the 45° line upward indicates that the data has tails
heavier than a normal distribution. There are three possible outliers
on the right: two observations from class `'setosa'`

and
one observation from class `'virginica'`

.

As shown in Bartlett Test of Equal Covariance Matrices for Linear Discriminant Analysis, the data does not match a single covariance matrix. Redo the calculations for a quadratic discriminant.

load fisheriris; prednames = {'SepalLength','SepalWidth','PetalLength','PetalWidth'}; Q = fitcdiscr(meas,species,'PredictorNames',prednames,'DiscrimType','quadratic'); Nclass = [50 50 50]; N = L.NumObservations; K = numel(L.ClassNames); mahQ = mahal(Q,Q.X,'ClassLabels',Q.Y); expQ = chi2inv(((1:N)-0.5)/N,D); [mahQ,sorted] = sort(mahQ); figure; gscatter(expQ,mahQ,Q.Y(sorted),'bgr',[],[],'off'); legend('virginica','versicolor','setosa','Location','NW'); xlabel('Expected quantile'); ylabel('Observed quantile for QDA'); line([0 20],[0 20],'color','k');

The Q-Q plot shows a better agreement between the observed and
expected quantiles. There is only one outlier candidate, from class `'setosa'`

.

The Mardia kurtosis test (see Mardia [4]) is an alternative to examining a Q-Q plot. It gives a numeric approach to deciding if data matches a Gaussian mixture model.

In the Mardia kurtosis test you compute *M*,
the mean of the fourth power of the Mahalanobis distance of the data
from the class means. If the data is normally distributed with constant
covariance matrix (and is thus suitable for linear discriminant analysis), *M* is
asymptotically distributed as normal with mean *d*(*d* + 2) and variance 8*d*(*d* + 2)/*n*, where

*d*is the number of predictors (number of dimensions in the data).*n*is the total number of observations.

The Mardia test is two sided: check whether *M* is
close enough to *d*(*d* + 2) with respect to a normal distribution of variance 8*d*(*d* + 2)/*n*.

**Example: Mardia Kurtosis Test for Linear and Quadratic Discriminants. **Check whether the Fisher iris data is approximately normally
distributed for both linear and quadratic discriminant analysis. According
to Bartlett Test of Equal Covariance Matrices for Linear Discriminant
Analysis,
the data is not normal for linear discriminant analysis (the covariance
matrices are different). Check Q-Q Plots for Linear and Quadratic Discriminants indicates
that the data is well modeled by a Gaussian mixture model with different
covariances per class. Check these conclusions with the Mardia kurtosis
test:

load fisheriris; prednames = {'SepalLength','SepalWidth','PetalLength','PetalWidth'}; L = fitcdiscr(meas,species,'PredictorNames',prednames); mahL = mahal(L,L.X,'ClassLabels',L.Y); D = 4; N = L.NumObservations; obsKurt = mean(mahL.^2); expKurt = D*(D+2); varKurt = 8*D*(D+2)/N; [~,pval] = ztest(obsKurt,expKurt,sqrt(varKurt))

pval = 0.0208

The Mardia test indicates to reject the hypothesis that the data is normally distributed.

Continuing the example with quadratic discriminant analysis:

Q = fitcdiscr(meas,species,'PredictorNames',prednames,'DiscrimType','quadratic'); mahQ = mahal(Q,Q.X,'ClassLabels',Q.Y); obsKurt = mean(mahQ.^2); [~,pval] = ztest(obsKurt,expKurt,sqrt(varKurt))

pval = 0.7230

Because `pval`

is high, you conclude the data
are consistent with the multivariate normal distribution.

[1] Box, G. E. P. *A General Distribution
Theory for a Class of Likelihood Criteria.* Biometrika
36(3), pp. 317–346, 1949.

[2] Fisher, R. A. *The Use of Multiple
Measurements in Taxonomic Problems.* Annals of Eugenics,
Vol. 7, pp. 179–188, 1936. Available at http://digital.library.adelaide.edu.au/dspace/handle/2440/15227.

[3] Guo, Y., T. Hastie, and R. Tibshirani. *Regularized
Discriminant Analysis and Its Application in Microarray.* Biostatistics,
Vol. 8, No. 1, pp. 86–100, 2007.

[4] Mardia, K. V. *Measures of multivariate
skewness and kurtosis with applications.* Biometrika 57
(3), pp. 519–530, 1970.

Was this topic helpful?