GLM, GRM, and
MANOVA Syntax  Keyword Estimate
ESTIMATE [=] Value List;
Example. ESTIMATE = 
0 1 1 0 

0 1 0 1; 
Optional keyword; specify the coefficients that are to be used in the
linear combination of parameter estimates for the custom hypothesis; multiple
ESTIMATE specifications may appear
in the same analysis. Note that tests of linear combinations of parameter
estimates can also be requested from the GLM Results dialog, where a convenient
and efficient userinterface is provided for specifying the coefficients.
Continuous
predictors. The number of parameters specified in the Value
list must match the number of parameters in the respective model. In the
example shown above, if a multiple regression model was specified with
the intercept (X0) and three
continuous predictor variables (X1,
X2, X3),
then for each linear combination four (intercept plus three predictors)
parameters must be specified (b0,
b1, b2,
b3); the statement shown above
0*b0+1*b11*b2+0*b3 = 0
0*b0+1*b1+0*b21*b3 = 0
Thus, these coefficients will test the hypothesis that the parameter
estimates for X1, X2,
and X3 are identical.
Categorical predictors. In models involving
categorical
predictors, the interpretation of the parameter estimates, and thus
the coefficients in the ESTIMATE
command depends on the parameterization of the model (overparameterized
or sigmarestricted).
If the overparameterized model is specified, the ESTIMATE
command can be used to specify planned
comparisons between selected cells in the design. Refer
to Hypotheses
about linear combinations of effects and Testing
specific hypotheses for additional details. In either case,
the number of coefficients following the ESTIMATE
command must match the number (and order) of parameters in the model;
remember that missing cells are dropped from the design
matrix, and thus the corresponding parameters will also be dropped.
In complex designs, it is easiest to use the Estimate
option on the GLM
Results dialog to construct custom hypotheses.
Applies
to. GLM,
GRM,
and ANOVA/MANOVA