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Chapter 9
Exportmodel as SPSS Statistics data. Writesa dataset in IBM® SPSS® Statistics format containing
the parameter correlation or covariance matrix with parameter estimates, standard errors,
significancevalues, and degrees of freedom. The order of variables in the matrix file is as follows.
rowtype_. Takesvalues (and value labels), COV (Covariances), CORR (Correlations), EST
(Parameter estimates), SE (Standard errors), SIG (Significancelevels), and DF (Sampling
design degrees of freedom). There is a separate case with row type COV (or CORR) for each
model parameter, plus a separate case for each of the other row types.
varname_. Takesvalues P1, P2, ..., corresponding to an ordered list of all model parameters,
for row types COV or CORR, with value labels corresponding to the parameter strings shown
in the parameter estimates table. The cells are blank for other row types.
P1,P2, ... These variables correspond to an ordered list of all model parameters, with variable
labels corresponding to the parameter strings shown in the parameterestimates table, and
take values according to the rowtype. For redundant parameters, all covariances are set
to zero; correlations are set to the system-missingvalue; all parameter estimates are set at
zero; and all standard errors, significance levels, and residual degrees of freedom are set to
the system-missing value.
Note:Thisfileis not immediately usable for further analyses in other procedures that read a matrix
file unless those procedures acceptall the row types exported here.
ExportM odel as XML. Saves the parameter estimates and the parameter covariance matrix, if
selected, in XML (PMML) format. You can use this model file to apply themodel information to
other data files for scoring purposes.
Complex Samples General Linear Model Options
Figure 9-7
General Linear Model Options dialog box
User-MissingValues. All design variables, as well as the dependent variable andany covariates,
must have valid data. Cases with invalid data for any of these variables are deleted from the
analysis. These controls allow you to decide whether user-missing values are treated as valid
among the strata, cluster,subpopulation, and factor variables.
ConfidenceInter val. This is the confidence interval level for coefficient estimates and estimated
marginalmeans. Specify a value greater than or equal to 50 and less than 100.