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The first criteria uses the error between the data recorded or simulated by the calibrated nonlinear finite element model and the data predicted by the linear model.
Moreover, the dynamic behaviors predicted by the linear model are compared to the predictions of the nonlinear model.
The results obtained by using these non-linear models are numerically compared with that obtained by the linear model.
The economic benefits are based on the degrees of freedom and the active constraints at the steady state predicted by the linear model embedded in the controller.
Spatial auto- and cross-correlation of uncertain numerical inputs that are spatially variable were represented by the linear model of coregionalization.
However, the water retention data predicted by the logistic model were more biased (higher systematic root mean error of estimate, RMSEs) than those predicted by the linear model.
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Target gene expression was normalized, prior to the analysis, by fitting the linear model described above to the observed data (Proc GLM, SAS 8.2).
The contribution of each of the six factors was plotted, excluding the residuals, which include gene-to-gene variation and variance unexplained by the linear models.
The figures depict the main effects tested by the linear models, with each variable (continuous vs rest-inserted loading, 100 vs 1000 repetitions) depicted in a separate figure.
The preprocessing of microarray data was conducted by the RMA [ 20– 22] integrative method, and the statistical analysis of gene differential expression was computed by the linear models and empirical Bayes methods [ 23].
When we limited the genes in Tables 1 and 2 to those showing significant differential expression at the P = 0.05 level by the linear modeling method, 25/54 genes in Table 1 and 24/68 genes in Table 2 were retained.
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by the linear shear-spring
by the linear part
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