AM
To: Ivan Krylov ; Lim, Hwanggyu
Cc: r-help@r-project.org
Subject: Re: [R] Question about nlminb function
This thread points out the important and often overlooked difference between
"convergence" of an algorithm and "termination"
of a program. I've been pushing this butt
Hello Ivan,
Thank you so much for your valuable comments. I will definitely look at the R
package of nloptr you introduced.
Best,
Hwanggyu
-Original Message-
From: Ivan Krylov
Sent: Friday, April 3, 2020 5:25 AM
To: Lim, Hwanggyu
Cc: r-help@r-project.org
Subject: Re: [R] Question
This thread points out the important and often overlooked
difference between "convergence" of an algorithm and "termination"
of a program. I've been pushing this button for over 30 years,
and I suspect that it will continue to come up from time to time.
Sometimes it is helpful to put termination
On Thu, 2 Apr 2020 10:26:07 +
"Lim, Hwanggyu" wrote:
> when n-1th estimates and nth estimates have absolute differences
> less than 0.001 for all three parameters, the iteration must stop
> I am using nlminb optimization function
nlminb function uses the PORT library. According to [1], the
Hello,
My name is Hwanggyu Lim. I am working estimating parameters of non-negative
function, which has local maximums. For example, the function has three
parameters (e.g., f(a, b, c)) and I need to estimate them.
For this, I am using nlminb optimization function and it works fine.
Here is my
Hi Spencer,
Thanks for your email.
Do you have a reference for generating the variance-covariance matrix
from the restricted variance-covariance? Is this a well known
technique?
Regards,
John
On 10/04/2008, Spencer Graves [EMAIL PROTECTED] wrote:
Hi, John:
I just got the following error
It's very well known that if a random vector X has a finite mean
mu and covariance Sig, and Y = A X, then
(1) EY = A %*% mu
and
(2) cov(Y) = A %*% Sig %*% t(X)
= tcrossprod(A %*% Sig, A)
Expression (1) says that mathematical expectation is a linear
operator.
Hi Spencer,
Sorry for not producing code as a worked example.
Here's an example:
==
# setting the seed number
set.seed(0)
# creating a correlation matrix
corr - diag(5)
corr[lower.tri(corr)] - 0.5
corr[upper.tri(corr)] - 0.5
# Data for the minimisation
mat -
Hi, John:
I just got the following error right after the attempt to use
'rmvnorm'.
Error: could not find function rmvnorm
I tried 'library(mvtnorm)', but the 'rmvnorm' in that package gave
me the following:
Error in rmvnorm(1, mean = c(3, -20, -10, 3, 2), sd = c(0.1, 15,
Dear All,
I wanted to post some more details about the query I sent to s-news last
week.
I have a vector with a constraint. The constraint is that the sum of the
vector must add up to 1 - but not necessarily positive, i.e.
x[n] - 1 -(x[1] + ...+x[n-1])
I perform the optimisation on the vector
Dear All,
I wanted to post some more details about the query I sent to s-news last
week.
I have a vector with a constraint. The constraint is that the sum of the
vector must add up to 1 - but not necessarily positive, i.e.
x[n] - 1 -(x[1] + ...+x[n-1])
I perform the optimisation on the vector
Have you considered optimizing over x1 = x[1:(length(x)-1]? You
could feed a wrapper function 'f2(x1, ...)' that computes xFull = c(x1,
1-sum(x1)) and feeds that to your 'fn'.
If this makes sense, great. Else, if my answer is not useful, be
so kind as to PLEASE do read the
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