An efficient analysis of cassava and rubber yields in Thailand using GEE and LMM with spatial effects

Abstract

The objectives of this research are to propose an efficient and proper model that fits the cassava and rubber yields data. A generalized estimating equation (GEE) and a linear mixed model (LMM) with spatial correlation following the conditional autoregressive model (CAR) were adopted. The dependent variables are the cassava and rubber yields collected each month in every province of Thailand. The factors considered are rainfall, averaged temperatures, and regions. The results from GEE and LMM show that the factors influencing on the cassava and rubber yields are rainfall, averaged temperature, and region. Both GEE and LMM fit the correlated data. The GEE is used to explain the influence of factors on the yields in all provinces while the LMM is used to explain the influence of factors on the yields in each province.