PERFORMANCE OF MIXED EFFECTS FOR CLUSTERED COUNTING DATA MODELS

2020 ◽  
Vol 3 (2) ◽  
pp. 34-41
Author(s):  
Intesar N. El-Saeiti ◽  
Khalil Mostafa ALsawi
Keyword(s):  
Generalized Linear Models ◽  
Standard Error ◽  
Mixed Models ◽  
Linear Models ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Likelihood Estimation ◽  
Real Data ◽  
Estimation Technique ◽  
Counting Data

This article is concerned with hierarchical generalized linear models. It includes generalized linear models and generalized linear mixed models, which are related to linear models. In generalized linear mixed models, the dependent variable and the standard error follow any distribution from the exponential family, e.g. normal, Poisson, binomial, gamma, etc. We studied counting data, and then use the Poisson-gamma model,where the dependentvariable follows the Poisson distribution and the standard error follow the gamma distribution. Several estimation techniques can be used for generalized linear mixed model. In this paperthe hierarchical likelihood estimation technique was used to prove the performance of H-likelihood methodwhen thecounting data were balanced or unbalanced. Real data were used to test the performance of Poisson-gamma H-likelihood estimation method in case of balanced and unbalanced counting data.When real data used in the past research for another problem, it was noticed that the performance of the hierarchical likelihood estimation technique gave a close approximations in the event of balanced and unbalanced counting data, and the output of the technique was approximately equivalent in both instances.

Testing the differences of LT50, LD50, or ED50

2018 ◽  
Vol 48 (6) ◽  
pp. 729-734 ◽  
Author(s):  
Juha Lappi ◽  
Jaana Luoranen
Keyword(s):  
Approximate Method ◽  
Generalized Linear Models ◽  
Mixed Models ◽  
Logistic Model ◽  
Linear Models ◽  
Generalized Linear Mixed Models ◽  
Wald Test ◽  
Delta Method ◽  
Treatment Variable ◽  
The Relationship

An approximate method is derived for testing the differences of LT50, LD50, or ED50, which indicate the temperature or dose needed to kill or damage half of the plants, respectively. It is assumed that a logistic model is used to describe the relationship between probability and a treatment variable in the framework of generalized linear mixed models or generalized linear models. The method is based on the delta method and the Wald test. In the forest sciences, this method can be used when dose, temperature, or time responses are compared in different treatments, cultivars, or origins.

scholarly journals Longitudinal Data Analyses Using Linear Mixed Models in SPSS: Concepts, Procedures and Illustrations

2011 ◽  
Vol 11 ◽  
pp. 42-76 ◽  
Author(s):  
Daniel T. L. Shek ◽  
Cecilia M. S. Ma
Keyword(s):  
Longitudinal Data ◽  
Generalized Linear Models ◽  
Mixed Models ◽  
Linear Models ◽  
Linear Mixed Models ◽  
Hierarchical Linear Models ◽  
Data Analyses ◽  
Basic Concepts ◽  
User Friendly ◽  
Over Time

Although different methods are available for the analyses of longitudinal data, analyses based on generalized linear models (GLM) are criticized as violating the assumption of independence of observations. Alternatively, linear mixed models (LMM) are commonly used to understand changes in human behavior over time. In this paper, the basic concepts surrounding LMM (or hierarchical linear models) are outlined. Although SPSS is a statistical analyses package commonly used by researchers, documentation on LMM procedures in SPSS is not thorough or user friendly. With reference to this limitation, the related procedures for performing analyses based on LMM in SPSS are described. To demonstrate the application of LMM analyses in SPSS, findings based on six waves of data collected in the Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes) in Hong Kong are presented.

scholarly journals Fitting phenological curves with Generalized Linear Mixed Models (GLMMs)

2020 ◽  
Author(s):  
Collin Edwards ◽  
Elizabeth E. Crone
Keyword(s):  
Mixed Models ◽  
Ad Hoc ◽  
Linear Models ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Data Sets ◽  
Phenological Data ◽  
Standard Linear ◽  
Phenological Shifts ◽  
Phenological Metrics

AbstractUnderstanding organismal phenology has been an emerging interest in ecology, in part because phenological shifts are one of the most conspicuous signs of climate change. While we are seeing increased collection of phenological data and creative use of historical data sets, existing statistical tools to measure phenology are generally either limited (e.g., first day of observation, which has problematic biases) or are challenging to implement (often requiring custom coding, or enough data to fit many parameters). We present a method to fit phenological data with Gaussian curves using linear models, and show how robust phenological metrics can be obtained using standard linear regression tools. We then apply this method to eight years of Baltimore checkerspot data using generalized linear mixed models (GLMMs). This case study illustrates the ability of years with extensive data to inform years with less data and shows that butterfly flight activity is somewhat earlier in warmer years. We believe our new method fills a convenient midpoint between ad hoc measures and custom-coded models.

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