2.13.3.4 discrim(Pro)

Menu Information

Statistics: Multivariate Analysis: Discriminant Analysis

Brief Information

Perform discriminant analysis and canonical discriminant analysis.

Additional Information

This feature is for OriginPro only.

Minimum Origin Version Required: 8.6

Command Line Usage

  1. discrim group:=5 var:=1:4;
  2. discrim group:=5 var:=1:4 -t "Canonical Discriminant Analysis";

Variables

Display
Name
Variable
Name
I/O
and
Type
Default
Value
Description
Group for Training Data group

Input

Range

Select data from a column to specify group for training data.
Training Data var

Input

Range

<active>
Select data to specify training data.
Predict Membership for Test Data test

Input

int

0
Determine whether to predict membership for test data. If checked (1), pvar is available.
Test Data pvar

Input

Range

Select data to specify test data.
Prior Probabilities prior

Input

int

0
Select the type of prior probabilities for each group.

Option list:

  • Equal
    Prior probabilities are equal for all groups.
  • Proportional to group size
    Prior probability for a group is proportional to the number of observations in the group.
Discriminant Function method

Input

int

0
Select the method to classify.

Option list:

  • Linear
    Using Linear Discriminant Function. The pooled within-group covariance matrix is used to calculate Mahalanobis distance.
  • Quadratic
    Using Quadratic Discriminant Function. Within-group covariance matrices are used to calculate Mahalanobis distance.

For more details, see the algorithm of discriminant functions.

Canonical Discriminant Analysis candisc

Input

int

1
Specify whether (1) or not (0) to perform Canonical Discriminant Analysis.
Cross Validation cv

Input

int

0
Specify whether (1) or not (0) to classify training data using Cross Validation method.
Descriptive Statistics stat

Input

int

1
Specify whether (1) or not (0) to perform Descriptive Statistics on training data including means, standard deviations for each variable in each group and total.
Descriptive Matrices dmat

Input

int

0
Specify whether (1) or not (0) to calculate covariance matrix, correlation matrix and group distance(squared Mahalanobis) matrices of training data.
Univariate ANOVA anova

Input

int

0
Specify whether (1) or not (0) to perform Univariate ANOVA on training data to test the difference in group means for each variable.
Equality Test of Covariance Matrices equal

Input

int

0
Specify whether (1) or not (0) to perform Equality Test of Covariance Matrices on training data to test the equality of within-group covariance matrices.
Pooled Within-group Covariance/Correlation Matrix pcov

Input

int

0
Specify whether (1) or not (0) to output pooled within-group covariance matrix and correlation matrix for training data.
Within-group Covariance Matrices gcov

Input

int

0
Specify whether (1) or not (0) to output within-group covariance matrices for training data.
Discriminant Function Coefficients dcoeff

Input

int

0
Specify whether (1) or not (0) to calculate discriminant function coefficients including constant and linear coefficients. This option is enabled only when method is Linear.
Canonical Structure Matrix cstruct

Input

int

0
Specify whether (1) or not (0) to calculate the canonical structure matrix in Canonical Discriminant Analysis.
Canonical Coefficients ccoeff

Input

int

0
Specify whether (1) or not (0) to calculate canonical coefficients in Canonical Discriminant Analysis including unstandardized canonical coefficients and standardized canonical coefficients.
Canonical Scores cscore

Input

int

1
Specify whether (1) or not (0) to calculate canonical scores and canonical group means in Canonical Discriminant Analysis.
Posterior Probabilities prob

Input

int

1
Specify whether (1) or not (0) posterior probabilities are included in the classification result for observations of training data and test data in different groups.
Squared Mahalanobis Distance dist

Input

int

0
Specify whether (1) or not (0) squared Mahalanobis distance is included in the classification result for observations of training data and test data in different groups.
Atypicality Index ai

Input

int

0
Specify whether (1) or not (0) atypicality index is included in the classification result for observations of training data and test data in different groups.
Classification Summary cstat

Input

int

1
Specify whether (1) or not (0) to summarize the classification result including observation count in each predicted group, error rate for training data and cross validation of training data.
Classification Summary Plot cplot

Input

int

0
Specify whether (1) or not (0) to show Classification Summary Plot in the report, which shows the source of predicted group members.
Classification Fit Plot fplot

Input

int

0
Specify whether (1) or not (0) to show Classification Fit Plot in the report, which shows the posterior probabilities of observations for the predicted group.
Canonical Score Plot splot

Input

int

1
Specify whether (1) or not (0) to show Canonical Score Plot in the report, which shows scores of observations in the first two canonical variables.
Discriminant Analysis Report rt

Output

ReportTree

<new>
Specify the sheet for the discriminant analysis report.
Classification Result for Training Data rdtrain

Output

ReportData

<new>
Specify the sheet for the classification result of training data.
Classification Result for Test Data rdtest

Output

ReportData

<new>
Specify the sheet for the classification result of test data.
Canonical Scores rdscore

Output

ReportData

<new>
Specify the sheet for canonical scores.
Plot Data rdplot

Output

ReportData

<new>
Specify the sheet for plot data. This variable is hidden in the dialog.

Description

This function performs the Discriminant Analysis on range data. For more information, see the Discriminant Analysis.

Examples

Example1

  1. Import the data file \Samples\Statistics\Fisher's Iris Data.dat.
  2. Run the script.
discrim group:=5 var:=1:4 rdtrain:=[<input>]<input> -r 2;

Example2

  1. Import the data file \Samples\Graphing\US Mean Temperature.dat.
  2. Run the script.
kmeans ir:=4:15 num:=5 rd:=[<input>]<input> -r 2;
page.active=1;
discrim group:=17 var:=4:15 -t "Canonical Discriminant Analysis";

Algorithm

See the algorithm of Discriminant Analysis.

References

See the reference of Discriminant Analysis.

Related X-Functions

pca, kmeans, hcluster