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Application of the mixed model method requires data sets in which all individuals are measured for genome-wide genotypes and the environmental risk factor.
The mixed linear model method requires large samples measured for both individual level genetic and environmental measures (based on power considerations (e.g., Dudbridge, 2013; Visscher et al. 2014), we estimate a sample size of at least 5000 individuals).
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Almost all proposed modeling methods require tuned adjustable parameters which have been optimized based on experimental data.
Simulation methods require a computer model of the disturbing load and the flickermeter.
Based on reference models and knowledge modelling, this method requires the definition of a collaborative information system to assist design actors.
Once obtained the linear model, our method requires to check for spatial autocorrelation.
Firstly, Leydesdorff and Sun (2009), Kwon (2011) and Kwon et al. (2012) included the internationally co-authored papers as the fourth element of the model; this method requires a huge amount of data processing and cleaning of the institutional address information (Leydesdorff and Sun 2009).
The marginal model using GEE method requires data to be missing completely at random, whereas the cluster-specific model using RELR requires data to be missing at random.
Compared with the traditional model, TFRWν-SVM method requires fewer samples and has better forecasting precision.
An improved version of this model checking method requiring less input parameters is described in [ 54].
A third limitation arises from the use of GLM for model selection, as this method requires a stable sample number.
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