Displaying 20 results from an estimated 235 matches for "mle".

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2009 Feb 03

11

Convert mle list to a hash

These patches convert the mle list to a hash. The same patches apply on
ocfs2 1.4 too.
Currently, we use the same number of hash pages for mles and lockres''.
This will be addressed in a future patch that will make both of them
configurable.
Sunil

2009 Feb 26

13

o2dlm mle hash patches - round 2

...a bugfix
that we are retracting in mainline currently. The patch may need more testing.
While I did hit the condition in my testing, Marcos hasn''t. I am sending it
because it can be queued for 2.6.30. Give us more time to test.
3. Patch 13 will be useful when we later attempt to size the mle hash appropriately.
Sunil

2009 Apr 17

26

OCFS2 1.4: Patches backported from mainline

Please review the list of patches being applied to the ocfs2 1.4 tree.
All patches list the mainline commit hash.
Thanks
Sunil

2007 Jan 14

2

ks.test not working?

Hi,
I am trying the following:
library(ismev)
library(evd)
fit <- gev.fit(x,show=FALSE)
ks.test(x,pgev,fit$mle[1],fit$mle[2],fit$mle[3])
but I am getting:
Warning message:
cannot compute correct p-values with ties in: ks.test(x, pgev,
fit$mle[1], fit$mle[2], fit$mle[3])
where x is:
[1] 239 38 1 43 22 1 5 9 15 6 1 9 156
25 3 100 6
[18] 5 100 10 103 25 5...

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",
fixe...

2008 Jun 19

1

try to find the MLE of a function

Hi everyone:
I have a density function f(x|theta)=theta*x^(theta-1),where
0<x<1,0<theta<infinite
I want to pratice on R to find the MLE of this function,here is my code:
x <- (0:10)/10
f<-function(theta) prod(theta*x^(theta-1))
mle(f)
and r gave me :Error in eval(expr, envir, enclos) : argument is missing,
with no default
what mistake I just made?and how to add a constraint of theta>0 in my
function.
Great thanks...

2012 Sep 11

1

Strange result from GAMLSS

Hi Folks! Just started using the gamlss package and I tried a simple code
example (see below). Why the negative sigma?
John
> y <- rt(100, df=1)> m1<-fitDist(y, type="realline")Warning messages:1: In MLE(ll3, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma, :
possible convergence problem: optim gave code=1 false convergence
(8)2: In MLE(ll4, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma,
:
possible convergence problem: optim gave code=1 false convergence
(8)3: In MLE(ll4, start = li...

2009 Mar 17

36

[git patches] Ocfs2 updates for 2.6.30

...Mushran (17):
ocfs2/hb: Expose the list of heartbeating nodes via debugfs
ocfs2: Move struct recovery_map to a header file
ocfs2: Expose the file system state via debugfs
ocfs2: Remove debugfs file local_alloc_stats
ocfs2/dlm: Encapsulate adding and removing of mle from dlm->master_list
ocfs2/dlm: Clean up struct dlm_lock_name
ocfs2/dlm: Refactor dlm_clean_master_list()
ocfs2/dlm: Create and destroy the dlm->master_hash
ocfs2/dlm: Activate dlm->master_hash for master list entries
ocfs2/dlm: Indent dlm...

2008 Nov 03

1

IWLS vs direct ML estimation

Hi,
I am thinking about IWLS vs ML estimation. When I use glm() for a
2-parameter distribution (e.g., Weibull), I can otain the MLE of scale
parameter given shape parameter through IWLS. Because this scale parameter
usually converges to the MLE.
In this point, I am wondering:
i) can you say that the direct MLE, which is obtained by maximizing a
likelihood function, is equalvant to the indirect MLE, which is obtained by
IWLS?...

2006 Feb 13

2

Sweave, mle and curve

...<<fig=TRUE>>=
## Simulate Y:
n <- 25
Y <- sum(rpois(n, lambda = 1))
Y
## Define minusloglik:
minusloglik <- function(theta) n * theta - Y * log(theta)
curve(minusloglik, 0.2, 2, xlab = "theta")
library(stats4)
cat("Y is now ", Y, "\n")
fit <- mle(minusloglik, start = list(theta = Y/n))
summary(fit)
@
\end{document}
In R, I get:
> Sweave("lec4.Snw")
Writing to file lec4.tex
Processing code chunks ...
1 : echo term verbatim eps pdf
Y is now 27
Y is now 24
You can now run LaTeX on 'lec4.tex'
>
and the latex...

2018 May 28

2

to R Core T: mle function in 32bits not respecting the constrain

I have an issue using mle in versions of 32 bits.
I am writing a package which I want to submit to the CRAN.
When doing the check, there is an example that has an error running in the
32 bits version.
The problem comes from the mle function, using it with a lower constrain.
In 64 bits version it works fine but when I put...

2011 Jun 22

1

AIC() vs. mle.aic() vs. step()?

I know this a newbie question, but I've only just started using AIC for
model comparison and after a bunch of different keyword searches I've
failed to find a page laying out what the differences are between the
AIC scores assigned by AIC() and mle.aic() using default settings.
I started by using mle.aic() to find the best submodels, but then I
wanted to also be able to make comparisons with a couple of submodels
that were nowhere near the top, so I started calculating AIC values
using AIC(). What I found was that not only the scores, but...

2006 Oct 16

1

MLE Methods

Greetings Forum,
I am new to R and and writing in hopes of getting some help.
Our MLE results from a home grown software do not match with that of R. We are using a censored sample and will really appreciate if you could give us any pointers as to which MLE method is used in R... to my knowledge there are different flavors of MLE used.
Thanks in Advance...
-------------...

1999 Dec 09

1

nlm() problem or MLE problem?

I am trying to do a MLE fit of the weibull to some data, which I attach.
fitweibull<-function()
{
rt<-scan("r/rt/data2/triam1.dat")
rt<-sort(rt)
plot(rt,ppoints(rt))
a<-9
b<-.27
fn<-function(p) -sum( log(dweibull(rt,p[1],p[2])) )
cat("starting -log like=",fn(c(a,b)),"...

2018 May 28

0

to R Core T: mle function in 32bits not respecting the constrain

> On May 27, 2018, at 10:31 PM, francesc badia roca <fbr600 at gmail.com> wrote:
>
> I have an issue using mle in versions of 32 bits.
>
> I am writing a package which I want to submit to the CRAN.
> When doing the check, there is an example that has an error running in the
> 32 bits version.
>
> The problem comes from the mle function, using it with a lower constrain.
> In 64 bits ve...

2011 Jul 07

3

AR vs ARIMA question

Dear R People:
Here is some output from AR and ARIMA functions:
> xb <- arima.sim(n=120,model=list(ar=0.85))
> xb.ar <- ar(xb)
> xb.ar
Call:
ar(x = xb)
Coefficients:
1
0.6642
Order selected 1 sigma^2 estimated as 1.094
> xb.arima <- arima(xb,order=c(1,0,0),include.mean=FALSE)
> xb.arima
Call:
arima(x = xb, order = c(1, 0, 0), include.mean = FALSE)

2009 Oct 06

2

mle from stats4

I am using mle as a wrapper from optim( ). How would I extract the
convergence code, to know that optim( ) converged properly?
Thanks,
Stephen Collins, MPP | Analyst
Global Strategy | Aon Benfield
[[alternative HTML version deleted]]

2012 Mar 21

2

Error in fitdist- mle failed to estimate parameters

Hi,
I am trying fit certain data into Beta distribution. I get the error saying
"Error in fitdist(discrete_random_variable_c, "beta", start = NULL, fix.arg
= NULL) : the function mle failed to estimate the parameters, with the
error code 100"
Below is the sorted data that I am trying to fit. Where am I going wrong.
Thanks a lot for any help.
Vinod
2.05495e-05,3.68772e-05,4.21994e-05,4.38481e-05,5.55001e-05,5.74267e-05,6.27489e-05,6.43976e-05,6.64938e-05,7.40247e-05,7...

2009 Apr 08

3

MLE for bimodal distribution

Hello everyone,
I'm trying to use mle from package stats4 to fit a bi/multi-modal
distribution to some data, but I have some problems with it.
Here's what I'm doing (for a bimodal distribution):
# Build some fake binormally distributed data, the procedure fails also with
real data, so the problem isn't here
data = c(rn...

2005 Aug 29

1

Different sings for correlations in OLS and TSA

...389.7,353.3,354.6,371.8,397.7,438.5,467.9,505.7,574.7,644.7,667.8,616.4,509.6,447,413.1,384.1),start=1980,freq=1)
ts.anr<-ts(c(104.1,102.4,97.9,96.2,95.1,95.1,97.9,101.6,105.9,111.1,117.9,121.3,121.8,114.2,107.6,105.1,101.9,98.6),start=1980,freq=1)
# to find autocorrelations via (p)acf's and mle I do:
fun.tsa.mle<-function(x){
par(mfrow=c(3,1))
acf(x)
pacf(x)
# AR model is estimated
m1<- ar.mle(x)
# An estimation of the unexplained portion of variance
m1.1<-m1$var.pred
# plot the function
plot(x)
# Give a printout
print(m1)
print("unexplained portion of variance:")
print...