ter).
This point is where I need some help : is there any function to make
non-linear fitting of (at least) 2 data sets with shared parameters ?
Hope someone will have some good news for me
Etienne Toffin
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T1=rep(c
("YES","YES","NO","NO"),2),T2=rep(c("YES","NO","YES","NO"),2),Freq=c
(12,5,0,7,24,1,0,0))
What do you think of such datas ? Can I use any statistical method to
test my hypothesis ? Any advice ?
Than
if there is any statistical method to compare the
estimated values of parameters of the two distributions ? And wether
it's the case, how to perform it in R ?
Hope I'm clear enough for getting help,
Etienne
-----------
Etienne To
REATMENT) is different
Do you have any other idea ?
Thanks,
Etienne
One short question about nls: are there any reason why nlm should be
used rather than nls and vice-versa (nls results are quite more full
than those of nlm)?
Hope this helps,
Spencer
Etienne Toffin wrote:
Hi,
used rather than nls and vice-versa (nls results are quite more full
than those of nlm)?
>Hope this helps,
>Spencer
>
> Etienne Toffin wrote:
>> Hi,
>>
>> I'm using a non linear model to fit experimental survival curves.
>>
>> This model de
- are these methods different ?
- which one should be preferentially used ?
This is not really a question about R but more about statistics…
I don't think I'm really clear and I know I'm not rigorous at all in
my descriptions, but I hope someone will understand me.
Thanks,
E
the linear regression with lm(y ~ x).
It seems to be surprising to me: is this result normal ? Is there any
problem in the R-squared value calculated in this case ?
Etienne Toffin
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Hi there,
I've got a small question :
is there any post-hoc test for Kruskal rank sum test integrated in R ?
I know that the Nemenyi test is one of the post-hoc that can be used,
but there's no (to by knowledge) R function for it.
What should I do ?
Thanks,
Etienne
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