Program ImmunoAssay: ImmunoAssay Example One This file, ImmunoAssayOneOutput.txt, was created at 18:24:45 on 09-Feb-2011 Five parameter logistic fitting: r = top + (bottom - top)/((1 + (a/C50)^HillSlope)^asymm) r = assay response; a = analyte concentration Non-linear regression (Nelder and Mead simplex procedure) Convergence criterion was not satisfied The following results are, or are derived from, the current estimates on exiting the regression method Estimated parameters The statistics are obtained assuming that the model behaves as a linear model about the minimum. The Hessian matrix is calculated as the numerically derived second derivatives of chi square with respect to all pairs of parameters. Consequentlty treat the statistics with great caution. Best Estimate of Coefficient t-value p-value estimate the error of t P > |t| variation (%) top 3.976 0.045 1.1314 88.3836 8.8818E-16 bottom -0.0202 0.0427 211.6108 -0.4726 0.6467 C50 27.6215 99.5518 360.4138 0.2775 0.7871 HillSlope 2.2786 0.1245 5.4638 18.3024 5.0988E-9 Asymm 113.7547 909.3134 799.3637 0.1251 0.9029 Best Pre-minimum Post-minimum Initial Fractional estimate gradient gradient estimate step top 3.976 -0.0064 -0.0010 0.0 0.395 bottom -0.0202 0.0024 0.0025 3.95 0.395 C50 27.6215 1.5374E-4 5.8032E-4 2.75 0.275 HillSlope 2.2786 -0.0027 0.0203 1.0 0.1 Asymm 113.7547 -5.0399E-5 -3.0229E-5 1.0 0.1 analyte observed calculated weight unweighted weighted estimated analyte concn response response residual residual concn error * 0.0 0.0 -0.0202 1.0 -0.0202 -0.0202 0.4773 0.5 0.05 0.0282 1.0 -0.0218 -0.0218 0.2769 1.0 0.1 0.2093 1.0 0.1093 0.1093 0.1799 1.5 0.6 0.5326 1.0 -0.0674 -0.0674 0.076 2.0 1.0 0.9757 1.0 -0.0243 -0.0243 0.063 2.5 1.5 1.4937 1.0 -0.0063 -0.0063 0.0578 3.0 2.0 2.0321 1.0 0.0321 0.0321 0.0584 3.5 2.5 2.5387 1.0 0.0387 0.0387 0.0645 4.0 3.0 2.9746 1.0 -0.0254 -0.0254 0.0805 4.5 3.4 3.3198 1.0 -0.0802 -0.0802 0.1147 5.0 3.5 3.5721 1.0 0.0721 0.0721 0.132 5.5 3.7 3.7428 1.0 0.0428 0.0428 0.2028 6.0 3.9 3.8497 1.0 -0.0503 -0.0503 0.778 6.5 3.94 3.9119 1.0 -0.0281 -0.0281 2.4139 10.0 3.95 3.9759 1.0 0.0259 0.0259 2.6321 * the estimated error in the estimated concentration on entering this response via getSampleConcn(response) Degrees of freedom 10 Number of data points 15 Number of estimated paramaters 5 Sum of squares of the unweighted residuals 0.0386 Correlation: analyte concentration and responses Linear Correlation Coefficient (R) 0.9111 Linear Correlation Coefficient Probability 1.1675E-6 Correlation: observed responses and calculated responses Linear Correlation Coefficient 0.9994 Linear Correlation Coefficient Probability 8.636E-21 Parameter - parameter correlation coefficients top bottom C50 HillSlope Asymm top 1.0 -0.1091 -0.2192 -0.2098 -0.2338 bottom -0.1091 1.0 -0.2953 0.6022 -0.2839 C50 -0.2192 -0.2953 1.0 -0.7253 0.9997 HillSlope -0.2098 0.6022 -0.7253 1.0 -0.7094 Asymm -0.2338 -0.2839 0.9997 -0.7094 1.0 Coefficient of determination, R = 0.9989 Adjusted Coefficient of determination, R' = 0.9988 Coefficient of determination, F-ratio = 11267.3084 Coefficient of determination, F-ratio probability = 1.7274E-20 Total (weighted) sum of squares = 33.4913 Regression (weighted) sum of squares = 33.4527 Error (weighted) sum of squares = 0.0386 Number of iterations taken 3001 Maximum number of iterations allowed 3000 Number of restarts taken 0 Maximum number of restarts allowed 3 Standard deviation of the simplex at the minimum 0.0 Convergence tolerance 1.0E-9 simplex sd < the tolerance times the mean of the absolute values of the y values Step used in numerical differentiation to obtain Hessian matrix d(parameter) = parameter*1.0E-4 End of file