Sorry, I had no seen your previous e-mail. Just another question. Is
there any way to obtain an R2 to have a numeric idea of how good is the
fitting?
Daniel Valverde Saubí
Grup de Biologia Molecular de Llevats
Facultat de Veterinària de la Universitat Autònoma de Barcelona
Edifici V, Campus UAB
08193 Cerdanyola del Vallès- SPAIN
Centro de Investigación Biomédica en Red
en Bioingeniería, Biomateriales y
Nanomedicina (CIBER-BBN)
Grup d'Aplicacions Biomèdiques de la RMN
Facultat de Biociències
Universitat Autònoma de Barcelona
Edifici Cs, Campus UAB
08193 Cerdanyola del Vallès- SPAIN
+34 93 5814126
En/na Eik Vettorazzi ha escrit:
Actually "drm" as posted before fits a sigmoid curve (a generalized
logistic function with 4 parameters, see ?LL.4), so I didn't get the
point of your new question.
Dani Valverde schrieb:
Thank you all for your answers. If you look at the plot resulting
from my data, it seems that it is some kind of sigmoid function, not
only polynomial. How could I fit it?
Best,
Dani
Daniel Valverde Saubí
Grup de Biologia Molecular de Llevats
Facultat de Veterinària de la Universitat Autònoma de Barcelona
Edifici V, Campus UAB
08193 Cerdanyola del Vallès- SPAIN
Centro de Investigación Biomédica en Red
en Bioingeniería, Biomateriales y
Nanomedicina (CIBER-BBN)
Grup d'Aplicacions Biomèdiques de la RMN
Facultat de Biociències
Universitat Autònoma de Barcelona
Edifici Cs, Campus UAB
08193 Cerdanyola del Vallès- SPAIN
+34 93 5814126
En/na Eik Vettorazzi ha escrit:
you might use the drc-package (equivalently you could use nls with
an appropriate "selfstart" model like SSlogis)
library(drc)
mm<-drm(delta~ph,fct=LL.4())
plot(mm)
From your plot I was assuming that "ph" is the independent variable
(as modelled above) - so if you want to predict a ph from delta you
will need the "inverse" function of your fitted model - you could
toy with ED from the drc package or do a simple grid search with
"predict".
hth.
Dani Valverde schrieb:
Hello,
This is a very basic question, but I don'y know the answer. I have
these data
delta <-
c(28.6-8.825,28.6-8.828,28.6-8.836,28.6-8.845,28.6-8.897,28.6-8.944,28.6-9.027,28.6-9.091,28.6-9.263,28.6-9.4,28.6-9.7,28.6-9.981,
28.6-10.287,28.6-10.48,28.6-10.684,28.6-10.875)
ph <- c(4.4,4.6,4.8,5,5.2,5.4,5.6,5.8,6,6.2,6.4,6.6,6.8,7,7.2,7.4)
plot(ph,delta,ylab=c(expression(Delta*delta)),xlab="pH")
Which kind of model can I fit on these, so that can I predict for a
given delta the pH of my sample? Once the model is fitted, how can
I plot it on the graph?
Best regards,
Dani
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