| dc.description.abstract | Additive Main Effect and Multiplicative Interaction (AMMI) model was
commonly used to analyze Genotype Environment × Interaction with
normal response variables, now it had been generalized for categorical or
other non-normal response variables, called GAMMI model. This devel opment was conducted by introducing multiplicative terms to the Gen eralized Linear Model (GLM). This research round up our previous work
on developing an approach of Row Column Interaction Models (RCIMs)
comprise to GAMMI model and focus to get more generalized for count ing data with overdispersed and zeros problems. A few interesting things
here are (i) an issue of distribution on GLM sense and (ii) an issue of
model’s complexity that is the number of multiplicative terms to fit the
interaction effect more properly. On the distribution issue of counting
data, we will focus on Poisson, Negative Binomial (NB), and zero inflated
problems with Zero Inflated Poisson (ZIP) and Zero Inflated NB (ZINB)
Hadi, A. F., Sa’diyah, H. and Iswanto, R.
distribution. A simulation conducted by adding outlier(s) on a Poisson
counting data for overdispersed condition, and adding zeros observation
on the data for illustrating the zero problems. We propose the NB model
for overdispersed data and model of ZIP or ZINB for data with both,
overdispersed and zero problem. In the case of both illnesses conditions
happened simultaneously, the mean square error of NB and ZINB will in crease slightly. But the ZINB was resulting the simplest model of RCIM
with less number of interaction terms. | en_US |
