The GLMM uses: (1) a link function (typically nonlinear on the parameters) to specify the relationship between the mean of the response variable and a linear model; and (2) a variance function to describe the relationship between the mean and the variance of the distribution of the response variable, which enables it to relate traditional linear regression to non-normal data.
To overcome this limitation we collect data on the type of ICS (fluticasone or fluticasone/salmeterol) as a co-variable, which enables us to include it as a confounder in the multi-variable analysis or to perform stratified analysis.
We pursue two separate analyses: an analysis ofindependent variables, in which we tie each study to a particular guiding learning theory in an attempt to determine which guiding theory has had the most predictive success; and an analysis of dependent variables, which enables us to determine which measurement techniques have been most sensitive to the learning benefits of AV technology.
We use both arm-index s and time t (rather than only time) as input variables, which enables the approach to encode the movement of invertebrate arm movements with a compact model (namely, by encoding the movement of all points along the arm).
This paper examines these two complex, multi-variable propositions using Space Syntax theory, a method which enables a mathematical analysis of the structural and visual configuration of the spaces in a plan to be undertaken.
The simplified model is described by physically meaningful state variables, which enables controller designers to effectively consider practical objectives and constraints.
When we use the subsample with available socioeconomic variables, which enables us to take these characteristics into account, the results are the same, in both hospital fixed and random effects specifications (columns 5 and 6 of Table 3).
DCA produces an ordination plot of major axes of variation in a dataset, which enables a large number of variables to be combined.
Thus, the design parameterization is separated from the analysis discretization, which enables a reduction in the number of design variables, compared with methods that use element-wise variables.
This paper presents an indoor fire evacuation model which enables a comprehensive representation of interacting essential variables, i.e. building environment, occupants and combustion products.
