One way will be to solve this as an ordinary optimization problem with
an equality constraint. Function `alabama::auglag` can do this:
library(alabama)
fn <- function(p) sum((df$y - p[1]*exp(-p[2]*df$x))^2)
heq <- function(p) sum(p[1]*exp(-p[2]*df$x)) - 5
# Start with initial valu
> On Jun 20, 2018, at 8:50 AM, Lorenzo Isella wrote:
>
> Dear All,
> I have a problem I haver been struggling with for a while: I need to
> carry out a non-linear fit (and this is the
> easy part).
> I have a set of discrete values {x1,x2...xN} and the corresponding
> {y1, y2...yN}. The difficu
I recommend posting this on a mathematics discussion forum like Stack Exchange
and (re-)reading the Posting Guide for this mailing list.
I think you are going to need to re-write your model function to algebraically
combine your original model along with the constraint, and then use the
origina
Dear All,
I have a problem I haver been struggling with for a while: I need to
carry out a non-linear fit (and this is the
easy part).
I have a set of discrete values {x1,x2...xN} and the corresponding
{y1, y2...yN}. The difficulty is that I would like the linear fit to
preserve the sum of the val
I'm not really familiar with what you are doing. when I try to debug
something like this, I run each step separately to determine where the
error is. For example, if I clean up the code a bit and run the derivs
function:
derivs(time,y,parms)
[[1]]
[1] 429.709540 438.844035 281.741953 404.175
Hi Malgorzata,
Did you try to _install_ rather than just _load_ the XLConnect package?
install.packages("XLConnect")
Sad to say, your code:
time=c(16,17,18,19,20,21,22,23,24,25,26)
#x=c(20.2,18.9,16.5)
y=c(7.63,9.22,4.86,4.78,0.38,6.13,3.91,38.41,2.58,36.95,1.73)
cE=c(15.05,38.01,41.09,31.41,3.5
Hello,
I get an error message:Error in checkFunc(Func2, times, y, rho) : The number
of derivatives returned by func() (22) must equal the length of the initial
conditions vector (2)
I try to optimise system of differential equations with 2 extra variables
derived from the data.frame.
I didn't
Hi Malgorzata,
There is currently another thread on the list about attaching R code
to emails. The problem appears to be that if you attach a file with
the extension ".R" it is sent in a way that is blocked by the mail
server. If you want to attach an R code file to your message, change
the extensi
Go back and read the Posting Guide.
1) Missing code. Only certain types of attachments get through, and the
recommendation is to include everything in the body of the email. Whatever you
did, it did not get through to us.
2) Please make it clear what results you obtained and what results you
I try to do optimisation of the system of the differential equations. I've
managed to make my exemplary code working, however after changing equations on
the exact model and data on the real one, I get an error messages and code
doesn't work.
I have a partial data for L (concentrations were ass
f J C Nash (U30A) [mailto:nas...@uottawa.ca]
Sent: 13 July 2013 13:07
To: r-help@r-project.org; Stephen Clark
Subject: [R] Optimisation does not optimise!
Considering that I devised the code initially on a computer with only 8K bytes
for program and data, and it appears that your problem has 10
AM, r-help-requ...@r-project.org wrote:
Date: Fri, 12 Jul 2013 21:22:00 +0100
From: Stephen Clark
To:"r-help@R-project.org"
Subject: [R] Optimisation does not optimise!
Message-ID:
<928c4f7877280844b906d12d63a3f15b01145e5b5...@hermes8.ds.leeds.ac.uk>
Content-Type: tex
Hello,
I have the following code and data. I am basically trying to select individuals
in a sample (by setting some weights) to match known counts for a zone. This is
been done by matching gender and age bands. I have tested the function to be
optimised and it does behave as I would expect whe
On 18 April 2013 18:38, Thomas Foxley wrote:
> Rune,
>
> Thank you very much for your response.
>
> I don't actually have the models that failed to converge from the first
> (glmulti) part as they were not saved with the confidence set. glmulti
> generates thousands of models so it seems reasonabl
Rune,
Thank you very much for your response.
I don't actually have the models that failed to converge from the first
(glmulti) part as they were not saved with the confidence set. glmulti
generates thousands of models so it seems reasonable that a few of these
may not converge.
The clmm() m
On 15 April 2013 13:18, Thomas wrote:
>
> Dear List,
>
> I am using both the clm() and clmm() functions from the R package 'ordinal'.
>
> I am fitting an ordinal dependent variable with 5 categories to 9 continuous
> predictors, all of which have been normalised (mean subtracted then divided
> b
Dear List,
I am using both the clm() and clmm() functions from the R package
'ordinal'.
I am fitting an ordinal dependent variable with 5 categories to 9
continuous predictors, all of which have been normalised (mean
subtracted then divided by standard deviation), using a probit link
functi
> Partha Sinha
> Wed Feb 1 07:31:08 CET 2012
>
> Can optimisation (simplex etc) be done through R?
The GLPK mixed-integer library has R language bindings.
I am not sure how up-to-date it is though. Information
is available here:
http://en.wikibooks.org/wiki/GLPK/R
best wishes
---
Robbie Mor
> Can optimisation (simplex etc) be done through R?
>
Try the CRAN task view on optimisation at, for example,
'http://finzi.psych.upenn.edu/views/Optimization.html
***
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01, 2012 1:31 AM
To: r-help
Subject: [R] Optimisation
Can optimisation (simplex etc) be done through R?
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Can optimisation (simplex etc) be done through R?
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-containe
Hello,
By fixing log_vraisemblance, gradient and hessian function linked to a
family density like exp(theta.xi), I'm looking for some efficients
estimators by PML.
So I've seen optim,nlminb, et maxLik procedure.
But I 'm not sure that the heteroscedacity of my estimators are considered.
Does an
Dear R-users,
The following provides a basis to illustrate some questions I have about
xyplot() customization.
x <- rep(1:2, times = 6)
y <- numeric()
for (i in 1:3) {
y <- c(y, 1:4*10^(i-1))
}
g <- rep(1:6, each = 2)
ax <- rep(1:3, each = 4)
tmp <- data.frame(x,y,g)
xyplot(y ~ x | g,
a
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