IBM SPSS Statistics 29

Click Analyze > Survival > Parametric Accelerated Failure Time (AFT) Models to obtain a Parametric Accelerated Failure Time (AFT) Model analysis, which invokes the parametric survival models procedure with non-recurrent life time data. Parametric survival models assume that survival time follow a known distribution, and this analysis fits accelerated failure time models with their model effects proportional with respect to survival time.

Click Analyze > Regression > Linear OLS Alternatives > Ridge to obtain a Linear Ridge Regression analysis. The new Linear Ridge extension procedure uses the Python sklearn.linear_model.Ridge class to estimate L2 or squared loss regularized linear regression models for a dependent variable on one or more independent variables, and includes optional modes to display trace plots and to select the alpha hyperparameter value based on crossvalidation. When a single model is fitted or crossvalidation is used to select alpha, a partition of holdout data can be used to estimate out-of-sample rformance.

Click Analyze > Regression > Linear OLS Alternatives > Lasso to obtain a Linear Lasso Regression analysis. The new Linear Lasso extension procedure uses the Python sklearn.linear_model.Lasso class to estimate L1 loss regularized linear regression models for a dependent variable on one or more independent variables, and includes optional modes to display trace plots and to select the alpha hyperparameter value based on crossvalidation. When a single model is fitted or crossvalidation is used to select alpha, a partition of holdout data can be used to estimate out-of-sample performance.

Click Analyze > Regression > Linear OLS Alternatives > Elastic Net to obtain a Linear Elastic Net Regression analysis. The new Linear Elastic Net extension procedure uses the Python sklearn.linear_model.ElasticNet class to estimate regularized linear regression models for a dependent variable on one or more independent variables. Regularization combines L1（Lasso）and L2（Ridge）penalties. The extension includes optional modes to display trace plots for different values of alpha for a given L1 ratio, and to select the L1 ratio and alpha hyperparameter values based on crossvalidation. When a single model is fitted or crossvalidation is used to select the penalty ratio and/or alpha, a partition of holdout data can be used to estimate out-of-sample performance.

Pseudo-R2 measures and the intra-class correlation coefficient are now included in Linear Mixed Models and Generalized Linear Mixed Models output (when appropriate). The pseudo-R2 measures are based entirely on the final estimates and are produced after estimation has been completed. The coefficient of determination R2 is a commonly reported statistic, because it represents the proportion of variance explained by a linear model. The intra-class correlation coefficient (ICC) is a related statistic that quantifies the proportion of variance explained by a grouping (random) factor in multilevel/ hierarchical data.

SPSS Statistics v29 includes upgraded versions of Python (3.10.4) and R (4.2.0).

By default, unselected cases are no longer hidden in the Data Editor when a subset of cases is selected, and the unselected cases are not discarded.New button on the Status bar to switch between Classic (Output and Syntax) and Workbook modes.

SSURVREG RECURRENT incorporates a shared frailty term into a parametric survival model. It is treated as a random component to account for an unobserved effect due to individual or group level variability. The new procedure compliments the Survival AFT procedure in SPSS Statistics 29.

The Data Editor's Overview tab provides visual and statistical summaries of your dataset and each variable in it, to help you quickly turn data into knowledge.

Youden's index is a summary measure of a receiver operating characteristic (ROC) curve. It is defined by Sensitivity – (1 – Specificity) and could be used as a single statistic to capture the performance of a dichotomous diagnostic test. Sensitivity referring to the percentage of true positives, Specificity referring to the percentage of true negatives.

New percentiles UI provides an easily accessible dialog for percentiles (through Analyze > Descriptive Statistics > Percentiles), offering the full range of available options. Users can save Empirical Best Linear Unbiased Predictions (EBLUPs) for random effects in mixed models to datasets or external files. Computation of multiple time-dependent covariates in the Cox Regression dialogs is now supported.