Generalized Linear Models Statistics
Model Effects. The following options are available:
- Analysis type. Specify the type of analysis to produce. Type I analysis is generally appropriate when you have a priori reasons for ordering predictors in the model, while Type III is more generally applicable. Wald or likelihood-ratio statistics are computed based upon the selection in the Chi-Square Statistics group.
- Confidence intervals. Specify a confidence level greater than 50 and less than 100. Wald intervals are based on the assumption that parameters have an asymptotic normal distribution; profile likelihood intervals are more accurate but can be computationally expensive. The tolerance level for profile likelihood intervals is the criteria used to stop the iterative algorithm used to compute the intervals.
- Log-likelihood function. This controls the display format of the log-likelihood function. The full function includes an additional term that is constant with respect to the parameter estimates; it has no effect on parameter estimation and is left out of the display in some software products.
Print. The following output is available:
- Case processing summary. Displays the number and percentage of cases included and excluded from the analysis and the Correlated Data Summary table.
- Descriptive statistics. Displays descriptive statistics and summary information about the dependent variable, covariates, and factors.
- Model information. Displays the dataset name, dependent variable or events and trials variables, offset variable, scale weight variable, probability distribution, and link function.
- Goodness of fit statistics. Displays deviance and scaled deviance, Pearson chi-square and scaled Pearson chi-square, log-likelihood, Akaike's information criterion (AIC), finite sample corrected AIC (AICC), Bayesian information criterion (BIC), and consistent AIC (CAIC).
- Model summary statistics. Displays model fit tests, including likelihood-ratio statistics for the model fit omnibus test and statistics for the Type I or III contrasts for each effect.
- Parameter estimates. Displays parameter estimates and corresponding test statistics and confidence intervals. You can optionally display exponentiated parameter estimates in addition to the raw parameter estimates.
- Covariance matrix for parameter estimates. Displays the estimated parameter covariance matrix.
- Correlation matrix for parameter estimates. Displays the estimated parameter correlation matrix.
- Contrast coefficient (L) matrices. Displays contrast coefficients for the default effects and for the estimated marginal means, if requested on the EM Means tab.
- General estimable functions. Displays the matrices for generating the contrast coefficient (L) matrices.
- Iteration history. Displays the iteration history for the parameter estimates and log-likelihood and prints the last evaluation of the gradient vector and the Hessian matrix. The iteration history table displays parameter estimates for every n ^{th} iterations beginning with the 0^{th} iteration (the initial estimates), where n is the value of the print interval. If the iteration history is requested, then the last iteration is always displayed regardless of n.
- Lagrange multiplier test of scale parameter or negative binomial ancillary parameter. Displays Lagrange multiplier test statistics for assessing the validity of a scale parameter that is computed using the deviance or Pearson chi-square, or set at a fixed number, for the normal, gamma, inverse Gaussian, and Tweedie distributions. For the negative binomial distribution, this tests the fixed ancillary parameter.
How to specify Statistics for Generalized Linear Models
This feature requires SPSS® Statistics Standard Edition or the Advanced Statistics Option.
- From the menus choose:
- In the Generalized Linear Models dialog, click Statistics.