[R] Fitting ELISA measurements "unknowns" to 4 parameter logistic model

[Hugo Mildenberger]

Hello Chris,

You may also use the R-package "calib".

Hugo 


On Tuesday 01 February 2011 17:08:13 Christopher Anderson wrote:
> Hello,
> 
> I am trying to fit my Elisa results (absorbance readings)  to a standard
> curve. To create the standard curve model, I performed a 4-parameter
> logistic fit using the 'drc' package (ExpectedConc~Absorbance). This gave me
> the following:
> > FourP
> 
> A 'drc' model.
> 
> Call:
> drm(formula = Response ~ Expected, data = SC, fct = LL.4())
> 
> Coefficients:
> b:(Intercept)  c:(Intercept)  d:(Intercept)  e:(Intercept)
>         1.336          6.236         85.521         59.598
> 
> > summary(FourP)
> 
> Model fitted: Log-logistic (ED50 as parameter) (4 parms)
> 
> Parameter estimates:
> 
>               Estimate Std. Error  t-value p-value
> b:(Intercept)  1.33596    0.15861  8.42309  0.0011
> c:(Intercept)  6.23557    3.18629  1.95700  0.1220
> d:(Intercept) 85.52140    2.15565 39.67313  0.0000
> e:(Intercept) 59.59835    5.18781 11.48815  0.0003
> 
> Residual standard error:
> 
>  1.866876 (4 degrees of freedom)
> 
> Now that I have the 4 parameters, how do I fit the absorbance readings for
> the analytical unknowns to the standard curve model (as to estimate the
> concentrations of my unknown analytical samples)?
> I can use the argument 'predict', but this predicts absorbance given
> concentrations (y given x), I need to predict concentrations give absorbance
> (x given y).
> 
> Thanks!
> Chris
> 
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