. I look forward to the results.
Best regards,
Michael
unaffiliatd
> Gesendet: Freitag, 21. Oktober 2016 um 09:39 Uhr
> Von: "Berend Hasselman" <b...@xs4all.nl>
> An: "Mike meyer" <1101...@gmx.net>
> Cc: ProfJCNash <profjcn...@gmail.com>
sue.
For that reason it is (in my view) a bad idea to force the user to set up his
problem in
R-model notation.
Michael
unaffiliated
> Gesendet: Donnerstag, 20. Oktober 2016 um 15:26 Uhr
> Von: ProfJCNash <profjcn...@gmail.com>
> An: "S Ellison" <s.elli...@lgcgroup.com>, &
How do you reply to a specific post on this board instead of the thread?
I am too incompetent to find this out myself.
Thanks,
Michael
unaffiliated
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
And finally, to put to rest the notion that the number of residuals is in any
way significant for the
solution of the least squares problem I submit to you the function
f(x,y)=(x²+y²)²
of 2 variables but only one residual f_1(x,y)=x²+y² which nonetheless has a
unique minimum
at the point
>From my reading of the above cited document I get the impression that the
>algorithm
(algorithm 3.16, p27) can easily be adapted to handle the case m 0 and so the system becomes ill conditioned.
Why can we not get around this as follows: as soon as mu is below some threshold
we solve instead
Make that f(x,u)=||x||².
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal,
@SE: yes, not every system of equations with more variables than equations is
solvable,
we need an additional condition e.g. full rank of the coefficient matrix.
Uniqueness of the solution was not required.
@BH:
Yes this is the document, it is a nice presentation.
I did not read the first page
@pd: you know that a System of equations with more variables than equations is
always solvable
and if a unique solution is desired one of mimimal norm can be used.
According to "Methods for nonlinear least squares problems" by Madsen, Nielsen
and Tingleff the LM-algorithm
solves Systems of the
Greetings,
The description of nls.lm specifies that in minimizing a sum of squares of
residuals
the number of residuals must be no less than the dimension of the independent
variable
("par").
In fact the algorithm does not work otherwise (termination code 0).
But this condition is
Hello,
I have both 32 and 64 bit verions of R installed. What happens if I open a
workspace saved from 64 bit R
in the 32 bit version or conversely?
I am fairly careless but never noticed any problems.
__
R-help@r-project.org mailing list -- To
10 matches
Mail list logo
