[R] info

[Paul, David A]

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:a.CALANDRA at mclink.it] 
Sent: Thursday, July 10, 2003 11:39 AM
To: R-help at stat.math.ethz.ch
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