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| The ANOVA Procedure |
When a MANOVA statement appears before the first RUN statement, PROC ANOVA enters a multivariate mode with respect to the handling of missing values; in addition to observations with missing independent variables, observations with any missing dependent variables are excluded from the analysis. If you want to use this mode of handling missing values but do not need any multivariate analyses, specify the MANOVA option in the PROC ANOVA statement.
You can specify the following options in the MANOVA statement as test-options in order to define which multivariate tests to perform.

Alternatively, you can input the transformation matrix directly by entering the elements of the matrix with commas separating the rows, and parentheses surrounding the matrix. When this alternate form of input is used, the number of elements in each row must equal the number of dependent variables. Although these combinations actually represent the columns of the M matrix, they are displayed by rows.
When you include an M= specification, the analysis requested in the MANOVA statement is carried out for the variables defined by the equations in the specification, not the original dependent variables. If you omit the M= option, the analysis is performed for the original dependent variables in the MODEL statement.
If an M= specification is included without either the MNAMES= or the PREFIX= option, the variables are labeled MVAR1, MVAR2, and so forth by default. For further information, see the section "Multivariate Analysis of Variance" in Chapter 28, "The GLM Procedure."
You can specify the following options in the MANOVA statement after a slash as detail-options:
For example, the statement
manova / printe;
displays the error SSCP matrix and the partial correlation matrix computed from the error SSCP matrix.
The following statements give several examples of using a MANOVA statement.
proc anova;
class A B;
model Y1-Y5=A B(A);
manova h=A e=B(A) / printh printe;
manova h=B(A) / printe;
manova h=A e=B(A) m=Y1-Y2,Y2-Y3,Y3-Y4,Y4-Y5
prefix=diff;
manova h=A e=B(A) m=(1 -1 0 0 0,
0 1 -1 0 0,
0 0 1 -1 0,
0 0 0 1 -1) prefix=diff;
run;
The first MANOVA statement specifies A as the hypothesis effect and B(A) as the error effect. As a result of the PRINTH option, the procedure displays the hypothesis SSCP matrix associated with the A effect; and, as a result of the PRINTE option, the procedure displays the error SSCP matrix associated with the B(A) effect.
The second MANOVA statement specifies B(A) as the hypothesis effect. Since no error effect is specified, PROC ANOVA uses the error SSCP matrix from the analysis as the E matrix. The PRINTE option displays this E matrix. Since the E matrix is the error SSCP matrix from the analysis, the partial correlation matrix computed from this matrix is also produced.
The third MANOVA statement requests the same analysis as the first MANOVA statement, but the analysis is carried out for variables transformed to be successive differences between the original dependent variables. The PREFIX=DIFF specification labels the transformed variables as DIFF1, DIFF2, DIFF3, and DIFF4.
Finally, the fourth MANOVA statement has the identical effect as the third, but it uses an alternative form of the M= specification. Instead of specifying a set of equations, the fourth MANOVA statement specifies rows of a matrix of coefficients for the five dependent variables.
As a second example of the use of the M= specification, consider the following:
proc anova;
class group;
model dose1-dose4=group / nouni;
manova h = group
m = -3*dose1 - dose2 + dose3 + 3*dose4,
dose1 - dose2 - dose3 + dose4,
-dose1 + 3*dose2 - 3*dose3 + dose4
mnames = Linear Quadratic Cubic
/ printe;
run;
The M= specification gives a transformation of the dependent variables dose1 through dose4 into orthogonal polynomial components, and the MNAMES= option labels the transformed variables as LINEAR, QUADRATIC, and CUBIC, respectively. Since the PRINTE option is specified and the default residual matrix is used as an error term, the partial correlation matrix of the orthogonal polynomial components is also produced.
For further information, see the "Multivariate Analysis of Variance" section in Chapter 28, "The GLM Procedure."
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