Worksheet output contains summary tables of the modeling data and a summary of the information in the Core Output. The worksheets generated depend on the analysis type and model settings. They present the output in a form that can be used for reporting and further analyses and are listed on the Results tab underneath Output Data.
Condition Numbers: Rank and condition number of the matrix of partial derivatives for each iteration.
The matrix is of full rank, since Rank is equal to the number of parameters. If the Rank were less than three, that would indicate that there was not enough information in the data to estimate all three parameters. The condition value is the square root of the ratio of the largest to the smallest eigenvalue and values should be less than 10^n where n is the number of parameters.
Correlation Matrix: A correlation matrix for the parameters, for each sort level.
If any values get close to 1 or –1, there may be too many parameters in the model and a simpler model may work better.
Diagnostics: Diagnostics for each function in the model and for the total:
CSS: corrected sum of squared observations
WCSS: weighted corrected sum of squared observations
SSR: sum of squared residuals
WSSR: weighted sum of squared residuals
S: estimate of residual standard deviation
DF: degrees of freedom
CORR_(OBS,PRED): correlation between observed Y and predicted Y
WT_CORR_(OBS,PRED): weighted correlation
AIC: Akaike Information Criterion goodness of fit measurement
SBC: Schwarz Bayesian Criterion goodness of fit measurementa
Differential Equations: The value of the partial derivatives for each parameter at each time point for each value of the sort variables.
Dosing Used: The dosing regimen specified for the modeling.
Eigenvalues: Eigenvalues for each level of the sort variables.
An eigenvalue of matrix A is a number l, such that Ax=lx for some vector x, where x is the eigenvector. Eigenvalues and their associated eigenvectors can be thought of as building blocks for matrices.
Final Parameters and Final Parameters Pivoted: Parameter names, units, estimates, standard error of the estimates, CV% (values < 20% are generally considered to be very good), univariate intervals, and planar intervals for each level of the sort variables.
Fitted Values: (Deconvolution models) Predicted data for each profile.
Initial Estimates: Parameter names, initial values, and lower and upper bounds for each level of the sort variables.
Minimization Process: Iteration number, weighted sum of squares, and value for each parameter, for each level of the sort variables.
This worksheet shows how parameter values converged as the iterations were performed. If the number of iterations is approaching the specified limit, there may be some problems with the model.
Parameters: (Deconvolution models) The smoothing parameter delta and absorption lag time for each profile.
Partial Derivatives and Stacked Partial Derivatives: Values of the differential equations at each time in the dataset.
Predicted Data: Time and predicted Y for multiple time points, for each sort level.
Secondary Parameters and Secondary Parameters Pivoted: Available for Michaelis-Menten, PK, PD, PK/PD Linked and ASCII models.
Secondary parameter name, units, estimate, standard error of the estimate, and CV% for each sort level.
Summary Tableb: The sort variables, X, Y, transformed X, transformed Y, predicted Y, residual, weight, standard error of predicted Y, standardized residuals, for each sort level.
For link models, also includes CP and Ce. For indirect response models, also includes CP.
Values: (Deconvolution models) Time, input rate, cumulative amount (Cumul_Amt, using the dose units) and fraction input (Cumul_Amt/test dose or, if no test doses are given, then fraction input approaches one) for each profile.
Variance Covariance Matrix: A variance-covariance matrix for the parameters, for each sort level.
User Settings: Model number, minimization method, convergence criterion, maximum number of iterations allowed, and the weighting scheme.
aAIC and SBC are only meaningful during comparison of models.
A smaller value is better, negative is better than positive, and a more negative value is even better. AIC is computed as:
AIC=N log (WRSS)+2P
where N is the number of observations with positive weight, log is the natural logarithm, WRSS is the weighted residual sum of squares, P is the number of parameters.
bIf there are no statements to transform the data, then X and Y will equal X(obs) and Y(obs).
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