Hi
r-help-boun...@r-project.org napsal dne 24.02.2009 12:41:50:
>
> Hi Peter,
>
> You are totally right and it was a miscalculating and misunderstanding
from
> me.
> Regarding the R-squared calculation of non linear model (question 2), is
> there any way to do that?
I am not an expert statist
Hi Peter,
You are totally right and it was a miscalculating and misunderstanding from
me.
Regarding the R-squared calculation of non linear model (question 2), is
there any way to do that?
Regards
Saeed
Petr Pikal wrote:
>
> Hi
>
> r-help-boun...@r-project.org napsal dne 24.02.2009 11:31:22:
Hi
r-help-boun...@r-project.org napsal dne 24.02.2009 11:31:22:
>
> Hi,
>
> Thank you for the reply and suggestions.
>
> I have two questions?
> 1) If I want to use log, it seems that I have to take log from both
sides of
> the model which will lead to lm(log(q)~log(-depth)). What is
tehdiff
Hi,
Thank you for the reply and suggestions.
I have two questions?
1) If I want to use log, it seems that I have to take log from both sides of
the model which will lead to lm(log(q)~log(-depth)). What is tehdifference
between this syntax and lm(log(q) ~ I(-depth))?
2) How can I calculate the R
Hi Saeed,
one approach is to try out several initial value combinations for a and b.
It often helps to find initial values of the same order of magnitude and of the
same sign
as the final estimates.
To get such initial values, you could linearize the model:
lm(log(q) ~ I(-depth))
and supply
Hi
I have a data set with two variables "q" and "depth" as follows:
q<-c(tapply(weight[Soil=="Jy"], Depth[Soil=="Jy"], mean)). This commns
returns 7 "q" values:
0.68790 0.84555 0.405416667 0.15277 0.03310 0.03140
0.00518
The "depth" values are produced using this command
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