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This indicates that the semi-supervised PCR has the potential to perform at least equally well as PCR, although it appears that identifying the optimal set of PC for the semi-supervised PCR model is even more difficult than for the PCR model.
All PCR model were significantly poorest than the best model.
The PCR model is generated on a subset of the components.
A whole-cell PCR model was used to demonstrate this concept.
And the results show, PCR model significantly reduces the effects of changes in temperature-sensitive points on model's accuracy; what's more, the model has good forecasting accuracy and robustness by using PCR model combines with traverse optimization method.
The predicted shear strength of all SFRC beams, evaluated by the PCR model of Eq. (13), was correlated with the observed shear capacity of the respective beams, and the relationship is presented in Fig. 5.
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This problem is more evident for the multiple linear regression (MLR) models than more robust and generalized partial least squares (PLS) and principal component regression (PCR) models.
Here, a comparison of the predictive performance was made using four types of multiple linear regression (MLR) and principal component regression (PCR) models in the prediction of chlorophyll-a (chl-a) concentration in the Yeongsan (YS) Reservoir, Korea, an estuarine reservoir that historically suffers from high levels of nutrient input.
Although no significant difference was found between results obtained using Partial Least Squares (PLS) and Principal Component Regression (PCR) models, however, discrepancies among results was found to be attributed to the variation in the discrimination power of adopted spectrophotometric methods on spectral data.
Moreover, the SPA-MLR models proved to be comparable to or even better than the full-spectrum partial last squares (PLS) or principal component regression (PCR) models for UV Vis (Araújo et al. 2011) as well as the low-resolution plasma spectra analysis (Galvão et al. 2001).
Similarly, for the RT-PCR model, there is again good agreement between the simulated and experimental data sets when one assumes the RT enzyme is more likely to make mutations at some positions than others.
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