Variational Approximation Multivariate Generalized Linear Latent Model in Diversity Termites in Riau and Peninsular Malaysia
In order to account for correlated count data with excess zeros, we use a variational approximation multivariate latent generalized linear model. We performed two different simulation-based on level species and genus with Poisson and negative binomial to subject-specific interpretations. Methods: In this work, we use variational approximation to estimate parameter in multivariate latent generalized linear model. Otherwise, overdispersed a count outcome exhibiting many zeros, above the amount expected under- sampling from a Poisson distribution. Results: Through simulation studies, species counts follows negative binomial, and genus counts follow Poisson distribution and the performance of this methods evaluate by Akaike information criterion (AIC), Akaike information criterion corrected (AICc), and Bayesian Information Criterion (BIC). Conclusion: While these two sets of latent class parameters might be meaningful in certain species counts and genus counts.
Rezzy Eko Caraka, Rung Ching Chen, Youngjo Lee, Maengseok Noh, Toni Toharudin, Andi Saputra, Bens Pardamean