[R] Fitting a model

[Dani Valverde]

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
>>>>
>>>
>>
>
>