Classification log-likelihood under the assumption that the true class membership is known.The closer these values are to 1 the better the predictions. Reduction of errors (Lambda), entropy R², standard R². These pseudo R-squared statistics indicate how well one can predict class memberships based on the observed variables (indicators and covariates).Classification errors (based on modal assignment).The lower the value, the better the model. these statistics (information criteria) weight fit and parsimony by adjusting the LL to account for the number of parameters in the model. BIC, AIC, AIC3, CAIC and SABIC (based on LL).log-likelihood (LL), log-prior (associated to Bayes constants) as well as the log-posterior.It indicates the proportion of the sample that needs to be movedto another cell to get a perfect fit. Dissimilarity index: A descriptive measure indicating how much the observed and estimated cell frequencies differ from one another.BIC, AIC, AIC3 and CAIC and SABIC (based on L²). These statistics (information criteria) weight fit and parsimony by adjusting the LL to account for the number of parameters in the model.X 2 and Cressie-Read. These are alternatives to L 2 that should yield a similar p-value according to large sample theory if the model specified is valid and the data is not sparse.Likelihood-ratio goodness-of-fit value (L²) for the current model and the associated bootstrap p-value.Model Summary Statistics: Number of cases used in model estimation, number of distinct parameters estimated, seed and best seed that can reproduce the current model more quickly using the number of starting sets =0.Įstimation Summary: for each of the Expectation-Maximization and Newton-Raphson algorithms, XLSTAT reports the number of iterations used, the log-posterior value, the likelihood-ratio goodness-of-fit value, as well as the final convergence value. XLSTAT-LG provides one section per model (each model being represented by a specific number of classes): It is also possible to optimize Bayes constants, sets of random starting values, as well iteration parameters for both the Expectation-Maximization and Newton-Raphson algorithms, which are used for model estimation. XLSTAT-LG allows lauching computations automatically on different models according to different number of classes. Binomial Count: Binomial logistic regression model.Ordinal (with more than 2 ordered levels): Adjacent-category ordinal logistic regression model.Nominal (with more than 2 levels): Multinomial logistic regression.Continuous: Linear regression model (with normally distributed residuals).The appropriate model is estimated according to the scale type of the dependent variable:.Each case may contain multiple records (Regression with repeated measurements).Each category represents a homogeneous subpopulation (segment) having identical regression coefficients (LC Regression Model).Includes a K-category latent variable X to cluster cases (LC model). Is used to predict a dependent variable as a function of predictor variables (Regression model).XLSTAT-LG is based on the Latent Gold ® software developed by Statistical Innovations. Since the latent variable is categorical, Latent Class modeling differs from more traditional latent variable approaches such as factor analysis, structural equation models, and random-effects regression models since these approaches are based on continuous latent variables. Formally, latent classes are represented by K distinct categories of a nominal latent variable X. Cases within the same latent class are homogeneous with respect to their responses on these indicators, while cases in different latent classes differ in their response patterns. The latent classes are constructed based on the observed (manifest) responses of the cases on a set of indicator variables. Latent class analysis (LCA) involves the construction of Latent Classes which are unobserved (latent) subgroups or segments of cases.
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