GLM Quick Specs - Options Tab

Select the Options tab of the GLM Quick Specs dialog box to access the options described here.

Random factors

Click the Random factors button to display the Random Effects (Mixed Model) dialog box, which contains the currently selected categorical predictor variables (factors). Choose the categorical variables (factors) that represent random factors in the design. Note that in the analysis, all interaction effects involving random factor effects are treated as random effects. For a discussion of random effects, and the estimation of variance components, see the Introductory Overview, or the Variance Components and Mixed-Model ANOVA/ANCOVA module.

Sweep delta

Enter the negative exponent for a base-10 constant delta (delta = 10-sdelta) in the Sweep delta field; the default value is 7. Delta is used (1) in sweeping, to detect redundant columns in the design matrix, and (2) for evaluating the estimability of hypotheses; specifically a value of 2*delta is used for the estimability check.

Inverse delta

Enter the negative exponent for a base-10 constant delta (delta = 10-idelta) in the Inverse delta field; the default value is 12. Delta for matrix inversion is used to check for matrix singularity in matrix inversion calculations.

Parameterization

Use the Parameterization group box to select the type of parameterization options you want to use for your general linear model.

Sigma-restricted

Select the Sigma-restricted check box to compute the design matrix for categorical predictors in the model based on sigma-restricted coding; if it is not selected, the overparameterized model will be used. The sigma-restricted model is the default parameterization, except for models that involve nested or random effects; see the Introductory Overview topic Sigma-Restricted vs. Overparameterized Model for details.

No intercept

Select the No intercept check box to exclude the intercept from the model. This option is not available, and no-intercept is the default, for mixture models (see also Experimental Design for a discussion of designs for mixtures).

Lack of fit

Select the Lack of fit check box to compute the sums of squares for the pure error, i.e., the sums of squares within all unique combinations of values for the (continuous and categorical) predictor variables. On the GLM Results dialog, options are available to test the lack-of-fit hypothesis. Note that in large designs with continuous predictors, the computations necessary to estimate the pure error can be very time consuming. See Lack-of-fit tests using pure error for a discussion of lack-of-fit tests and pure error; see also Experimental Design.

Cross-validation

Click the Cross-validation button to display the Cross-Validation dialog box for specifying a categorical variable and a (code) value to identify observations that should be included in the computations for fitting the model (the analysis sample); all other observations with valid data for all predictor variables and dependent variables will automatically be classified as belonging to the validation sample (see the GLM Results - Residuals tab topic for a description of the available residual statistics for observations in the validation sample); note that all observations with valid data for all predictor variables but missing data for the dependent variables will automatically be classified as belonging to the prediction sample (see the Residuals tab topic for a description of available statistics for the prediction sample).

Sums of squares

Use the Sums of squares group box to select the method for constructing main effect and interaction hypotheses in unbalanced and incomplete designs; these methods are discussed in detail in the Introductory Overview topic Six types of sums of squares. For the sigma-restricted model the default value is Type VI (unique or effective hypothesis decomposition; see Hocking, 1985) and Type IV is not valid; for the overparameterized model the default value is Type III (orthogonal; see Goodnight, 1980), and Type VI is not valid. See also General ANOVA/MANOVA and GLM Notes - Sums of squares for additional details.

For alternative ways of specifying designs in GLM, see Methods for specifying designs.