Predictive correlation coefficients

Normal or generalized coordinate Predictive correlation coefficient Quadrupole moment Electron density Bond order... 

The presented algorithm was applied to 4 proteins (lysozyme, ribonuclease A, ovomucid and bovine pancreatic trypsin inhibitor) containing 51 titratable residues with experimentally known pKaS [32, 33]. Fig. 2 shows the correlation between the experimental and calculated pKaS. The linear correlation coefficient is r = 0.952 the slope of the line is A = 1.028 and the intercept is B = -0.104. This shows that the overall agreement between the experimental and predicted pKaS is good. 

The idea behind this approach is simple. First, we compose the characteristic vector from all the descriptors we can compute. Then, we define the maximum length of the optimal subset, i.e., the input vector we shall actually use during modeling. As is mentioned in Section 9.7, there is always some threshold beyond which an inaease in the dimensionality of the input vector decreases the predictive power of the model. Note that the correlation coefficient will always be improved with an increase in the input vector dimensionality. 

Multiple linear regression analysis is a widely used method, in this case assuming that a linear relationship exists between solubility and the 18 input variables. The multilinear regression analy.si.s was performed by the SPSS program [30]. The training set was used to build a model, and the test set was used for the prediction of solubility. The MLRA model provided, for the training set, a correlation coefficient r = 0.92 and a standard deviation of, s = 0,78, and for the test set, r = 0.94 and s = 0.68. 

Once the descriptors have been computed, is necessary to decide which ones will be used. This is usually done by computing correlation coelficients. Correlation coelficients are a measure of how closely two values (descriptor and property) are related to one another by a linear relationship. If a descriptor has a correlation coefficient of 1, it describes the property exactly. A correlation coefficient of zero means the descriptor has no relevance. The descriptors with the largest correlation coefficients are used in the curve fit to create a property prediction equation. There is no rigorous way to determine how large a correlation coefficient is acceptable. 

If the normalized method is used in addition, the value of Sjj is 3.8314 X 10 /<3 , where <3 is the variance of the measurement of y. The values of a and h are, of course, the same. The variances of a and h are <3 = 0.2532C , cf = 2.610 X 10" <3 . The correlation coefficient is 0.996390, which indicates that there is a positive correlation between x and y. The small value of the variance for h indicates that this parameter is determined very well by the data. The residuals show no particular pattern, and the predictions are plotted along with the data in Fig. 3-58. If the variance of the measurements of y is known through repeated measurements, then the variance of the parameters can be made absolute. 

Correlation analysis quantifies the degree to which the value of one variable can be used to predict the value of another. The most frequently used method is the Pearson product-moment correlation coefficient. 

Table 2 also contains the correlation coefficient, r, for each K . If the predicted concentrations for a data set exactly matched the expected concentrations, r would equal 1.0. If there were absolutely no relationship between the predicted and expected concentrations, r would equal 0.0. 

For the data the squared correlation coefficient was 0.93 with a root mean square error of 2.2. The graph of predicted versus actual observed MS(1 +4) along with the summary of fit statistics and parameter estimates is shown in Figure 16.7. 

Comparing with the conventional three-phase beds, the axial solid holdup distribution is much more uniform and the radial distribution of gas holdup (sg) is much flatter in circulating beds, due to the relatively high Ul and solid circulation. The values of Eg and bed porosity can be predicted by Eqs. (7) and (8) with a correlation coefficient of 0.94 and 0.95, respectively. 

Bubble size in the circulating beds increases with Ug, but decreases with Ul or solid circulation rate (Gs) bubble rising velocity increases with Ug or Ul but decreases with Gs the ffequeney of bubbles increases with Ug, Ul or Gs. The axial or radial dispersion coefficient of liquid phase (Dz or Dr) has been determined by using steady or unsteady state dispersion model. The values of Dz and D, increase with increasing Ug or Gs, but decrease (slightly) with increasing Ul- The values of Dz and Dr can be predicted by Eqs.(9) and (10) with a correlation coefficient of 0.93 and 0.95, respectively[10]. 

Since we do not know the proper values for X and t, we need a way of Judging plausible values of X and t from the data. We do this by testing the transformed background measurements for normality. Our choice of a test for normality is the probability plot correlation coefficient r (12). The coefficient r is the correlation between the ordered measurements and predicted values for an ordered set of normal random observations. We denote the ordered background measure-ments by yB(l). where yB(l) < yB(2) < yBCnn) denote the... 

In QSAR equations, n is the number of data points, r is the correlation coefficient between observed values of the dependent and the values predicted from the equation, is the square of the correlation coefficient and represents the goodness of fit, is the cross-validated (a measure of the quality of the QSAR model), and s is the standard deviation. The cross-validated (q ) is obtained by using leave-one-out (LOO) procedure [33]. Q is the quality factor (quality ratio), where Q = r/s. Chance correlation, due to the excessive number of parameters (which increases the r and s values also), can. 

Experimental conditions and initial rates of oxidation are summarized in Table V. For comparison, initial rates of dry oxidation at the same temperature and pressure of oxygen predicted by Equation 9 are included in parentheses. The predicted dry rate, measured dry rate, and measured wet rates are compared in Figure 2. The logarithms of the initial rates of heat production during wet oxidation increase approximately linearly (correlation coefficient = 0.92) with the logarithm of the partial pressure of oxygen and lead to values of In k = 2.5 and r = 0.9, as compared with values of In k = 4.8 and r = 0.6 for dry oxidation at this temperature. 

Note, however, there are two critical limitations to these "predicting" procedures. First, the mathematical models must adequately fit the data. Correlation coefficients (R ), adjusted for degrees of freedom, of 0.8 or better are considered necessary for reliable prediction when using factorial designs. Second, no predictions outside the design space can be made confidently, because no data are available to warn of unexpectedly abrupt changes in direction of the response surface. The areas covered by Figures 8 and 9 officially violate this latter limitation, but because more detailed... 

FIGURE 3.3 Graph of linear correlation coefficient r versus /J2 f°r various Pi in (a) WEG, (b) FAN, and (c) PAR. Curves are predictions points are simulation averages. 

The mechanistic simulation ACAT model was modified to account automatically for the change in small intestinal and colon k as a function of the local (pH-dependent) log D of the drug molecule. The rank order of %HIA from GastroPlus was directly compared with rank order experimental %HIA with this correction for the log D of each molecule in each of the pH environments of the small intestine. A significant Spearman rank correlation coefficient for the mechanistic simulation-based method of 0.58 (p < 0.001) was found. The mechanistic simulation produced 71% of %HIA predictions within 25% of the experimental values. 

You may be surprised that for our example data from Miller and Miller ([2], p. 106), the correlation coefficient calculated using any of these methods of computation for the r-value is 0.99887956534852. When we evaluate the correlation computation we see that given a relatively equivalent prediction error represented as (X - X), J2 (X - X), or SEP, the standard deviation of the data set (X) determines the magnitude of the correlation coefficient. This is illustrated using Graphics 59-la and 59-lb. These graphics allow the correlation coefficient to be displayed for any specified Standard error of prediction, also occasionally denoted as the standard error of estimate (SEE). It should be obvious that for any statistical study one must compare the actual computational recipes used to make a calculation, rather than to rely on the more or less non-standard terminology and assume that the computations are what one expected. 

Xing B, McGill W, Dudas M (1994a) Cross-correlation of polarity curves to predict partition coefficients of nonionic organic contaminants. Environ Sci Techno1 28 1929-1933... 

Can the species activity coefficients be calculated accurately An activity coefficient relates each dissolved species concentration to its activity. Most commonly, a modeler uses an extended form of the Debye-Hiickel equation to estimate values for the coefficients. Helgeson (1969) correlated the activity coefficients to this equation for dominantly NaCl solutions having concentrations up to 3 molal. The resulting equations are probably reliable for electrolyte solutions of general composition (i.e., those dominated by salts other than NaCl) where ionic strength is less than about 1 molal (Wolery, 1983 see Chapter 8). Calculated activity coefficients are less reliable in more concentrated solutions. As an alternative to the Debye-Hiickel method, the modeler can use virial equations (the Pitzer equations ) designed to predict activity coefficients for electrolyte brines. These equations have their own limitations, however, as discussed in Chapter 8. 

In the clinically relevant concentration range56 the relation between the response of the sensor and the viral concentration is linear (a linear fit through the data points in Fig. 10.16a gives a correlation coefficient of 0.98) facilitating easy virus concentration predictions with a calibrated sensor. Furthermore, even at the lowest measured virus concentration (850 particles/ml) a high signal-to-noise ratio of... 

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