This feature is for OriginPro only.
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. Note that beginning with Origin 2020b, there is a shortened syntax that follows the form [Book]Sheet!(N1:N2), N1 = the beginning column index and N2 being the ending column index in a contiguous range of columns. More complex strings from non-contiguous data of the form [Book]Sheet!([Book]Sheet!N1:N2,[Book]Sheet!N3:N4) are also possible.
|
| 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.
|