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
--
Eik Vettorazzi
Institut für Medizinische Biometrie und Epidemiologie
Universitätsklinikum Hamburg-Eppendorf
Martinistr. 52
20246 Hamburg
T ++49/40/42803-8243
F ++49/40/42803-7790
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