怎么用spss求下三角协方差矩阵？有高人指点一下吧

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本来以为是一个简单的操作，可是搜了半天也没见到答案，这个很难吗？我需要下三角的协方差矩阵，不是一般的那种。有高人之前一下吧。

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关键词：协方差矩阵 SPSS 高人指点 PSS 协方差

Printing the covariance matrix through the Reliability procedure, the Correlation procedure, or the Regression procedure

The Reliability procedure is somewhat simpler in that the covariance matrix is automatically printed as a separate table. The Correlation procedure combines the correlation, significance, cross-product deviations, covariance, and N for a variable pair into a single table cell. There is a way to reorganize this table so that the covariance matrix is printed as a separate table or subtable. It should be noted that the Reliability procedure uses listwise deletion of cases with missing values, ie., the covariances will be computed with only those cases that have no missing values on the variables in the analysis. The correlation procedure uses pairwise deletion by default, i.e. each covariance is calculated with all cases that have valid values
on that variable pair and each variance is calculated with all cases that have valid values on that variable. Listwise deletion is available as an option in the Correlation procedure. (See Technote 1475199, which addresses this distinction between the options.) The Regression procedure must be run from syntax for the covariance matrix option to be included. If you want listwise deletion and want the covariance matrix to be printed in a separate table, then the Reliability procedure will be the simplest solution. If you want pairwise deletion, you will need to use the Correlation or Regression procedure. The details for each procedure are provided below.

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The Reliability Procedure (Analyze->Scale->Reliability Analysis):
In the Reliability Analysis dialog, paste the variables of interest into the Items box and click the Statistics button. In the Statistics dialog that opens, click the checkbox for Covariances in the Inter-Item area. (There is also a checkbox for correlations. To request the correlation and covariance matrices in command syntax, add the keywords 'Corr' and 'Cov' respectively, to the /STATISTICS subcommand, as in:

RELIABILITY
/VARIABLES=time age salnow edlevel work jobcat minority
/FORMAT=NOLABELS
/SCALE(ALPHA)=ALL/MODEL=ALPHA
/STATISTICS=CORR COV .

The correlation and covariance matrices will print as separate tables.
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The Correlation Procedure (Analyze->Correlate->Bivariate):
In the "Bivariate Correlations" dialog, paste the variable of interest into the Variables box on the right of the dialog box. Then click the Options button. In the Options dialog, click the checkbox for
"Cross-product deviations and covariances". Click Continue and then click OK. To make this choice in a syntax command, add the keyword 'XPROD' to the /STATISTICS subcommand, as in:

CORRELATIONS
/VARIABLES=time age salnow edlevel work jobcat minority
/STATISTICS XPROD .

You may want to reorganize the Correlation table that is printed. In that table, the rows will be organized by variable, i.e. for each variable, the Pearson correlation, Sig. value, Sum of Squares and Cross-Products, Covariance, and N will be printed in one cell of the table.If you want separate tables for the correlation,..covariance, etc., you can use the pivot trays to accomplish this. Right-click anywhere in the Correlation table. In the pop-up menu that appears, choose
'SPSS Pivot Table Object->Open'. A pivot table window will open with the Correlation table. In that window: Open the Pivot menu and choose 'Pivoting trays'. In the Pivoting tray dialog, there will be 2 icons in the Row area. The first is for Variables and the second is for Statistics. Click the Variables (leftmost) icon and drag it to the right of the Statistics icon. If you close the Pivoting Trays dialog by clicking the x in the upper right corner, you will see in the Pivot Table window that the Pearson Correlation matrix is a separate subtable, as are each of the Sig., Sum of Squares and Cross-Products, Covariance and N matrices. If you like this organization, just close the Pivot Table window and your changes will be reflected in the Output window. If you want to hide all of the matrices in the table except the covariance matrix, then reopen the Pivoting trays dialog and drag the Statistics icon into the Layers area. When you close the Pivoting Trays dialog, only the Pearson Correlations matrix will be visible. However, there will be a Statistics tab with a scroll arrow at the top of the table. Scroll down to Covariance and click that choice. The Covariance matrix will become the visible matrix in this table. Close the Pivot Table window to return to the Output window.

_______________________________

The Regression Procedure:
As stated above, you can use the Regression procedure to print the covariance matrix but you will need to run it from a syntax command. You can build the command in the Regression dialog boxes. You will need to paste one of the variables into the "Dependent:" box and the other variables into the "Independent(s):" box. (The dependent variable will be in the first row and column of the covariance matrix.) Click the
Statistics button in the Regression dialog and check the box beside 'Descriptives' in the Statistics dialog and click Continue. Click Paste rather than OK. In the Syntax window, add the keyword "Cov" to the /DESCRIPTIVES subcommand, as in:

REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N cov
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT sex
/METHOD=ENTER time age salnow edlevel work jobcat minority .

The Covariance matrix will print as a subtable in the Correlations table. Note that regression uses listwise deletion by default but pairwise deletion can be requested from the "Regression: Options" dialog or by replacing "LISTWISE" with "PAIRWISE" in the REGRESSION command .

In SPSS, how do I generate a covariance matrix as a data set?

In SPSS, use the CORRELATION and the MCONVERT commands to create a covariance matrix.

The CORRELATION command generates a correlation matrix. The MCONVERT command converts the correlation matrix to a covariance matrix.

Suppose you have three variables (e.g., age, response, and time) from which you wish to create a covariance matrix and save it as a data set. The command would be:

  CORRELATION MATRIX OUT (*)
    /VARIABLES=age response time.

  MCONVERT
    /MATRIX=OUT ('c:\temp\covariance.sav').
The covariance matrix created will include the number of observations, standard deviations, means, and variable names. The data set covariance.sav will be saved in the c:\temp folder.

For the Lower Triangular Covariance Matrix, please read the attached PDF:
Lower Triangular Covariance Matrix.pdf (404.41 KB)

2014-4-5 04:52:09 上传

谢谢ReneeBK热心的回复。下三角的协方差是不是就是相关系数矩阵呀。