MODEL LINEAR EFEK CAMPURAN TERGENERALISASI DENGAN MENGGUNAKAN FUNGSI LINK LOGIT; GENERALIZED LINEAR MIXED MODEL USING LOGIT LINK FUNCTION

Tamami, I Tamami, N

2016 | Skripsi | FMIPA

Generalized linear mixed models, or GLMMs, are a powerful class of statistic models that combine the characteristic of generalized linear models (GLMs) and mixed effect model (LME). They handle a wide range of response distributions, and a wide range of scenarios where observations have been sampled in some kind of groups. One of the type of GLMMs is using logit link function that is logistic model with fixed and random effects. Estimation parameter of GLMMs can be achieved using various method, one of them is maximum likelihood estimation with approximation. The best model selection using the likelihood ratio test alongside with Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). GLMMs is applied to real data, through psychology object about emotional people in verbal (verbal aggression) by situation that had been around.

Kata Kunci : Linear mixed effects models, generalized linear models, generalized linear mixed models, logistic models, link function, marginal model, conditional models, maximum likelihood use approximation, likelihood ratio test, AIC and BIC.