Spencer Graves
Paul, David A wrote:
The most commonly used dose-response functions for nonlinear calibration curves are the four- and five-parameter logistic functions. The four-
parameter logistic is specified as
F(z) = delta + (alpha - delta)/(1 + (z/gamma)^beta)
so I'm not sure where you are getting your dose-response functional form from. In any case, you can fit this model using either nls( ) or nlme( ), depending on whether or not you want to fit a random-effects model. For references related to the four- and five-parameter logistic functions, you can read
1. Rodbard, D., and Frazier, G.R. (1975) "Statistical analysis of radioligand assay data," Methods Enzymol., vol. 37, p. 3 - 22.
2. Dudley, R.A., Edwards, P., and Ekins, R.P. (1985) "Guidelines for immunoassay data processing," Clin. Chem., vol. 31, no. 8, p. 1264 - 1271
The first of these articles introduces the four-parameter logistic, and the second refines its parametrization as well as introduces the five-parameter logistic for use in situations where the calibration curve is asymmetric. You should also acquire "Mixed Effects Models in S and Splus", by Drs. Pinheiro and Bates if you intend to do anything with mixed effects models.
Best,
david paul
-----Original Message-----
From: Andrea Calandra [mailto:[EMAIL PROTECTED] Sent: Thursday, July 10, 2003 11:39 AM
To: [EMAIL PROTECTED]
Subject: [R] info
HI
I'm a student in chemical engineering, and i have to implement an algoritm about FIVE PARAMETERS INTERPOLATION for a calibration curve (dose, optical
density)
y = a + (c - a) /(1+ e[-b(x-m])
where x = ln(analyte dose + 1) y = the optical absorbance data a = the curves top asymptote b = the slope of the curve c = the curves bottom asymptote m = the curve X intercept
Have you never seen this formula, because i don't fine information or lecterature about solution of this!!!
Can i help me
Hi Mr. Calandra
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