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The impact of the correlation between gas and electric loads on the probabilistic results is investigated.
Figure 3 demonstrates the impact of the correlation between the two original signals on the performance of LMCS.
The association parameters, ψ1 and ψ2, were varied to determine the impact of the correlation of Y T and Y E on model performance under model misspecification.
Correct interpretation of the data would not be possible without considering the impact of the correlation between urinary creatinine and plasma folate.
In order to verify the impact of the correlation among factors, the uncorrelated solution was then tested, showing further decrease in model fit (χ2 = 1943.63 p <.001, df = 665, CFI = 0.68, RMSEA 0.06).
This result helps in understanding the impact of the correlation on power of multicenter studies, whatever their designs are, and is particularly useful for observational studies where the center effect question is not often taken into account at the planning and/or at the analysis stages [ 22, 23].
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The impact of the correlations was examined by repeating the parameterization of the models described below with both data sets separately and then both together.
To study the impact of the correlations, we also quantified the discriminability under the assumption that the correlations are zero.
The impact of this correlation between the gene-level test statistics can be seen in the PCGSE results shown in Fig. 3.
These results are consistent with those of Karoui et al. [ 18] who observed no impact of the genetic correlation on validation correlations and regression coefficients.
We also demonstrate the direct impact of the improved correlation estimates for imputation and joint-testing methods, which take correlation matrices as input.
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