![]() Parameters lists the final point estimate and standard error for each parameter. # Achieved convergence tolerance: 0.0000000149įormula reminds us of the model we specified. # Residual standard error: 0.04563 on 32 degrees of freedom # We actually loaded it ealier with source("././AMfunctions.R"), Let’s enter into R all the components for 4PL curve-fitting: # - Function: 4PL curve function. From the log(concentration) plot, \(c\), the inflection point, looks like it may be around 8 so on the absolute scale that is 2981. The \(b\) parameter is harder to imagine, but for an ascending curve, a negative number is needed -1 will do. The upper asymptote, \(a\), looks like it’s going off to a limit of about 1. The mean of the zero-calibrators, 0.1, is a good starting value for parameter \(d\). Given the simple structure of the O∬onnell data and the strong relationship between concentration and response, starting values do not need to be very accurate. Based on the plot above and the interpretation of the parameters of the 4PL function, we can visually estimate reasonable starting values. Unlike linear regression, nonlinear regression models are fit using an iterative algorithm, which requires starting values for each unknown parameter.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |