This feature is for OriginPro only.
1. pca -r 1 mtype:=cov std:=1 screeplot:=0 splot:=1;
2. pca std:=1 lplot:=0 biplot:=0;
3. pca std:=1 eigenvec:=0 xcomp:=2 ycomp:=1 splot:=1;
Display
Name
|
Variable
Name
|
I/O
and
Type
|
Default
Value
|
Description
|
| Variables
|
irng
|
Input
Range
|
<active>
|
Specify the input variables. At least two input datasets are required. 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.
|
| Observation Labels
|
label
|
Input
Range
|
<optional>
|
Specify the dataset for observation label. It can be used for the scores plot.
|
| Analyze
|
mtype
|
Input
int
|
0
|
Select the matrix type to analyze.
Option list:
- corr:Correlation Matrix
- Use correlation matrix to analyze.
- cov:Covariance Matrix
- Use covariance matrix to analyze.
|
| Number of Components to Extract
|
npc
|
Input
int
|
2
|
Specify the number of principal components to extract. This value should be between 1 and the number of variables.
|
| Standardize Scores
|
std
|
Input
int
|
0
|
Determine whether to standardize scores.
For excluding missing values in listwise fashion, scores will be standardized as unit variance.
For the pairwise, scores will be scaled by the square root of the eigenvalue.
The scores data in Score Plot and Biplot will be standardized if std is 1.
|
| Exclude Missing Values
|
missing
|
Input
int
|
0
|
Specify the mode for excluding missing values from analysis.
Option list:
- Listwise
- An observation containing one or more missing values will be excluded in the analysis.
- Pairwise
- An observation is excluded only in the calculation of covariance or correlation between two variables if missing values exist in either of the two variables for the observation.
|
| Simple Descriptive Statistics
|
stat
|
Input
int
|
1
|
Specify whether to perform simple statistics on input data including the mean, standard deviation and number of observations in the analysis for each variable.
|
| Correlation Matrix
|
corr
|
Input
int
|
1
|
Specify whether to calculate correlation matrix. Note that the method to calculate the correlation matrix depends on the mode of missing.
|
| Eigenvalues
|
eigenval
|
Input
int
|
1
|
Specify whether to output eigenvalues including the proportion and the cumulative proportion. Bartlett's Test result is shown when mtype is cov.
|
| Eigenvectors
|
eigenvec
|
Input
int
|
1
|
Specify whether to output eigenvectors for principal components specified by npc.
|
| Scores
|
scores
|
Input
int
|
1
|
Specify whether to output scores for principal components specified by npc.
|
| Scree Plot
|
screeplot
|
Input
int
|
1
|
Specify whether to show the Scree Plot for eigenvalues.
|
| Principal Component for X Axis
|
xcomp
|
Input
int
|
1
|
Specify the principal component for the X axis in component plots.
|
| Principal Component for Y Axis
|
ycomp
|
Input
int
|
2
|
Specify the principal component for the Y axis in component plots.
|
| Loading Plot
|
lplot
|
Input
int
|
1
|
Specify whether to show the Loading Plot for eigenvectors.
|
| Score Plot
|
splot
|
Input
int
|
0
|
Specify whether to show the Score Plot for scores.
|
| Biplot
|
biplot
|
Input
int
|
1
|
Specify whether to show the Biplot for eigenvectors and scores.
|
| PCA Report
|
rt
|
Output
ReportTree
|
<new>
|
Specify the sheet for the Principal Component Analysis report.
|
| Score Data
|
rd
|
Output
ReportData
|
<new>
|
Specify the sheet for scores.
|
| Plot Data
|
rdplot
|
Output
ReportData
|
<new>
|
Specify the sheet for plot data. This variable is hidden in the dialog.
|