Displaying 20 results from an estimated 7000 matches similar to: "problem with optim and integrate"
2008 Apr 05
2
How to improve the "OPTIM" results
Dear R users,
I used to "OPTIM" to minimize the obj. function below. Even though I used
the true parameter values as initial values, the results are not very good.
How could I improve my results? Any suggestion will be greatly appreciated.
Regards,
Kathryn Lord
#------------------------------------------------------------------------------------------
x = c(0.35938587,
2008 Apr 05
2
How to improve the "OPTIM" results
Dear R users,
I used to "OPTIM" to minimize the obj. function below. Even though I used
the true parameter values as initial values, the results are not very good.
How could I improve my results? Any suggestion will be greatly appreciated.
Regards,
Kathryn Lord
#------------------------------------------------------------------------------------------
x = c(0.35938587,
2008 Apr 26
1
integration error when I use "optim" and "integrate" simultaneously
Dear R users,
When I use two functions, 'optim' and 'integrate', simultaneously, I always
get an error like this
--------------------------------------------------------------------------
numint = function(z) {
dlnorm(z,mu[1],sqrt(exp(g[1]))) *
dnorm((z-mu[2])/sqrt(exp(g[2])))/sqrt(exp(g[2]))
}
integrate(numint,lower=0,upper=Inf)$value
Error in integrate(numint, lower = 0,
2007 Jul 21
1
Gamma MLE
Hello,
I was asked to try the following code on R,
gamma.mles
function (xx,shape0,rate0)
{
n<- length(xx)
xbar<- mean(xx)
logxbar<- mean(log(xx))
theta<-c(shape0,rate0)
repeat {
theta0<- theta
shape<- theta0[1]
rate<- theta0[2]
S<- n*matrix(c(log(rate)-digamma(shape)+logxbar,shape/rate-xbar),ncol=1)
I<- n*matrix(c(trigamma(shape),-1/rate,-1/rate,shape/rate^2),ncol=2)
2006 Nov 11
2
Bayesian question (problem using adapt)
In the following code I have created the posterior density for a Bayesian
survival model with four parameters. However, when I try to use the adapt
function to perform integration in four dimensions (on my old version of R
I get an error message saying that I have applied a non-function, although
the function does work when I type kernel2(param0, theta0), or on the
newer version of R the computer
2011 Jun 18
2
different results from nls in 2.10.1 and 2.11.1
Hi,
I've noticed I get different results fitting a function to some data on
my laptop to when I do it on my computer at work.
Here's a code snippet of what I do:
##------------------------------------------------------------------
require(circular) ## for Bessel function I.0
## Data:
dd <- c(0.9975948929787, 0.9093316197395, 0.7838819026947,
0.9096108675003, 0.8901804089546,
2011 Feb 22
2
mle
Hi,
I am looking for some help regarding the use of the mle function.
I am trying to get mle for 3 parameters (theta0, theta1 and theta2) that
have been defined in the the log-likelihood equation as theta0=theta[1],
theta1=theta[2] and theta2=theta[3].
My R code for mle is:
mle(Poisson.lik, start=list(theta=c(20,1,1), method="Nelder-Mead",
fixed=list(w=w, t1=t1, t2=t2))
But I keep
2011 Aug 13
3
optimization problems
Dear R users
I am trying to use OPTIMX(OPTIM) for nonlinear optimization.
There is no error in my code but the results are so weird (see below).
When I ran via OPTIM, the results are that
Initial values are that theta0 = 0.6 1.6 0.6 1.6 0.7. (In fact true vales
are 0.5,1.0,0.8,1.2, 0.6.)
--------------------------------------------------------------------------------------------
>
2006 Apr 05
2
R2WinBUGS error
Dear R-help,
I'm using the R2WinBUGS package and getting an error message:
Error in file(file, "r") : unable to open connection
In addition: Warning message:
cannot open file 'codaIndex.txt', reason 'No such file or
directory'
I'm using R 2.2.1 and WinBUGS 1.4.1 on a windows machine (XP). My R code
and WinBUGS code is given below.
2011 Aug 29
3
gradient function in OPTIMX
Dear R users
When I use OPTIM with BFGS, I've got a significant result without an error
message. However, when I use OPTIMX with BFGS( or spg), I've got the
following an error message.
----------------------------------------------------------------------------------------------------
> optimx(par=theta0, fn=obj.fy, gr=gr.fy, method="BFGS",
>
2005 May 31
1
Solved: linear regression example using MLE using optim()
Thanks to Gabor for setting me right. My code is as follows. I found
it useful for learning optim(), and you might find it similarly
useful. I will be most grateful if you can guide me on how to do this
better. Should one be using optim() or stats4::mle?
set.seed(101) # For replicability
# Setup problem
X <- cbind(1, runif(100))
theta.true <- c(2,3,1)
y <- X
2013 Apr 08
0
Maximum likelihood estimation of ARMA(1,1)-GARCH(1,1)
Hello
Following some standard textbooks on ARMA(1,1)-GARCH(1,1) (e.g. Ruey
Tsay's Analysis of Financial Time Series), I try to write an R program
to estimate the key parameters of an ARMA(1,1)-GARCH(1,1) model for
Intel's stock returns. For some random reason, I cannot decipher what
is wrong with my R program. The R package fGarch already gives me the
answer, but my customized function
2007 Sep 12
1
Integrate() error message, I am at a loss
Hello!
I have a problem with integrate() in my function nctspa(). Integrate
produces an error message "evaluation of function gave a result of
wrong length". I don't know what that means. Could anyone suggest me
what is wrong with my function?
These are the examples of function calls that work OK:
nctspa(a=1:10,n=5)
nctspa(a=1:10, n=5, mu=2, theta=3, renorm=0)
This does not work:
2007 Feb 27
1
Additional args to fun in integrate() not found?
Hello, fellow Rdicts,
I have the code for the program below. I need to integrate a function
of "x" and "p". I use integrate to integrate over "x" and pass "p" as
an additional argument. "p" is specified and given default value in
the argument list. Still, integrate() cannot read "p", unless I
explicitly insert a numeric value in the
2009 Aug 19
1
BUGS
I am running a BUGS function with following
schools.sim <-bugs(data,inits,
parameters,
model.file="schools.txt",
n.chains=3,
n.iter=1000,
bugs.directory="E:/Rprograms")
My model.file IS in the directory
2011 Aug 17
2
An example of very slow computation
This message is about a curious difference in timing between two ways of computing the
same function. One uses expm, so is expected to be a bit slower, but "a bit" turned out to
be a factor of >1000. The code is below. We would be grateful if anyone can point out any
egregious bad practice in our code, or enlighten us on why one approach is so much slower
than the other. The problem
2006 Jul 20
2
Timing benefits of mapply() vs. for loop was: Wrap a loop inside a function
List:
Thank you for the replies to my post yesterday. Gabor and Phil also gave
useful replies on how to improve the function by relying on mapply
rather than the explicit for loop. In general, I try and use the family
of apply functions rather than the looping constructs such as for, while
etc as a matter of practice.
However, it seems the mapply function in this case is slower (in terms
of CPU
2003 Jul 22
4
greek in main title
Hello,
I have written a function that demonstrates the CLT by
generating samples following the exponential distribution,
calculating the means, plotting the histogram, and drawing
the limiting normal curve as an overlay. I have the title
of each histogram state the sample size and rate (1/theta)
for the exponential (the output is actually 4 histograms),
but I can't get the greek letter theta
2013 Mar 28
3
problem with plots with short example.
i am having problem running my own data. yesterday it was working just fine. today it is not. this is the code i was using as an example to follow. this code ALSO worked just fine yesterday, and is no longer working at all. i suspect it is a problem with either my computer or the software, at this point. if THIS won't even run.... something is wrong.
i can assure you this isn't
2005 May 30
1
Trying to write a linear regression using MLE and optim()
I wrote this:
# Setup problem
x <- runif(100)
y <- 2 + 3*x + rnorm(100)
X <- cbind(1, x)
# True OLS --
lm(y ~ x)
# OLS likelihood function --
ols.lf <- function(theta, K, y, X) {
beta <- theta[1:K]
sigma <- exp(theta[K+1])
e <- (y - X%*%beta)/sigma
logl <- sum(log(dnorm(e)))
return(logl)
}
optim(c(2,3,0), ols.lf, gr=NULL,
method="BFGS",