  Given that hierarchical count data in many fields are not
Normally-distributed and include random effects, this paper extends the
Generalized Linear Mixed Models (GLMMs) into Poisson Mixed-Effect Linear Model
(PMELM) and do numerical simulation experiments to verify the approach proposed
by Rakhmawati et al. (2016) in detecting outliers. This paper produces random
data based on epilepsy longitudinal data in Thall and Vail (1990), use six ways
to contaminate it and try to use code mentioned in supplementary materials in
previous research to detect the man-made outlier. Output shows that this method
is effective sometimes but does not always work, this is probably because of
the limitation of coding or some other reasons. Even though the data set and
local influence method has been researched and analyzed extensively in previous
papers, this paper makes contributions in data visualization. Figures in this
paper show the effect of each influencial component, which are clearer than the
original output in R and SAS.
