Multi-component score

The second step of the in silico screen is to calculate H-bond propensities. For a given active ingredient (A) and a coformer (B), three sets of H-bond propensity calculations are performed for A on its own, for B on its own and for the two-component A B system. A multi-component (MC) score can be calculated by subtracting the propensity value of the most likely pure form interaction (AA or BB propensity) from the equivalent value for coaystal interactions (AB or BA propensity). A H-bonding-based drive towards cocrystal formation is indicated if the MC score is positive. 

TABLE 2.3 Multi-component HBP Screening Results Summary for Paracetamol with Potential Coformer Molecules Ranked in Order of Their MC Score... 

It is unclear how different component scores are aggregated and weighted in the final index - the Coalition reports that suggested weights were defined based on a survey of Coalition members - but future versions of the Index promise to be based on stakeholder desires and a multi-criteria analysis that should allow different audiences to weight their own, customized index. 

One way to think about the factors obtained from the principal component analysis which are independent is to interpret them as defining a multi-dimensional space. For further analyses and in order to locate individuals within the 14-dimensional space, factor scores were calculated. First, the loading of each variable on a factor was multiplied by the individual s original value for that variable. In the next step of the procedure, the same calculation was repeated for all variables in the factor for that individual. These scores were then summed. The process was repeated for all factors for that same individual and then repeated for all other individuals. Finally, all scores were standardised to a mean of 0 with a standard deviation of 1. These procedures facilitate further statistical treatment of the motivational patterns and other variables of interest such as travel experience. 

A conceptual model is the rationale for and description of the concepts that a measurement instrument is intended to assess and the interrelationships of those concepts. A measurement model is an instrument s scale and subscale structure and the procedures followed to create scale and subscale scores. An example is the well-defined conceptual and measurement models for the scales and scale structure of the SF-36. The SF-36 contains 36 items that cover nine theory-based health concepts. Eight of these health concepts are measured by multi-item scales. There is a clearly defined means of creating the individual scale scores and the physical and mental component summary scales. ... 

In step (ii) any multi-way regression model may be used and tested. Usually, different model types (e.g. Af-PLS and Tucker3-based regression on scores model), or models with a different number of components (e.g. a two-component Af-PLS model and a three-component W-PLS model) are tested. To have complete independence of and y, the matrices involved in building the model have to be preprocessed based on interim calibration data each time step (ii) is entered. 

Multi-way PCA is statistically and algorithmically consistent with PCA (Wise et al. 1999 Westerhuis et al. 1999). Thus, it decomposes the initial matrix X in the summation of the product of scores vectors (t) and loading matrices (P), plus a residual matrix (. These residuals are minimized by least squares, and are considered to be associated to the non-deterministic part of the information. The systematic component of the information, expressed by the product (t x P), represents... 

The main method for modelling preferences under uncertainty is the Multi-Attribute Utility Theory (MAUT). In its simples (additive) form, a multi attribute utility function resembles a multi-attribute value function. The way to find parameters of a utility function is however different. While in the case of MAVT the scores and weights can be determined based on direct comparison of consequences, in the case of MAUT these components are found through lottery types of questions (Keeney Raiffa, 1999). 

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