search for: mle

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

...ed to 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

After some parallel mount/umount test on ocfs2, we got this: slab error in kmem_cache_destroy(): cache `o2dlm_mle': Can't free all objects. Then we found a memleak situation in dlm_add_migration_mle(). When a mle found, it will be removed from dlm->hlist. If there is no pointer to it at that moment, the mle will become an ?orphan mle? that no process can find and release. Signed-off-by: Xuejiufei...

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

Hi I have the following formula (I hope it is clear - if no, I can try to do better the next time) h(x, a, b) = integral(0 to pi/2) ( ( integral(D/sin(alpha) to Inf) ( ( f(x, a, b) ) dx ) dalpha ) and I want to do an mle with it. I know how to use mle() and I also know about integrate(). My problem is to give the parameter values a and b to the integrate function. In other words, how can I write h <- function... so that I can estimate a and b? Thanks, Rainer -- Rainer M. Krug, Dipl. Phys. (Germany), MSc...

Hi, How can we set the environment for the minuslog function in mle()? The call in this code fails because the "ll" function cannot find the object 'y'. Modifying from the example in ?mle: library(stats4) ll <- function(ymax=15, xhalf=6) { -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE)) } fit.mle <- function(FUN, x, y) {...

I'm trying to calculate the maximum likelihood estimate for a binomial distribution. Here is my code: y <- c(2, 4, 2, 4, 5, 3) n <- length(y) binomial.ll <- function (pi, y, n) { ## define log-likelihood output <- y*log(pi)+(n-y)*(log(1-pi)) return(output) } binomial.mle <- optim(0.01, ## starting value binomial.ll, ## log likelihood method="BFGS", ## optimization method hessian=TRUE, ## numerial Hessian control...

Hi everyone! I am using the mle {stats4} to estimate the parameters of distributions by MLE method. I have a problem with the examples they provided with the mle{stats4} html files. Please check the example and my question below! *Here is the mle html help file * http://stat.ethz.ch/R-manual/R-devel/library/stats4/html/mle.html...

...e 1 ------------ 8< ---------------------- library(stats4) data <- rnorm(100,0,1) lik1 <- function(m, v, data) { N <- length(data) lik.mean <- dnorm(mean(data), m, sqrt(v/N), log=T) lik.var <- dchisq(N*var(data)/v, N-1, log=T) return(-lik.mean - lik.var) } ml.result <- mle(lik1, start=list(m=2, v=2), fixed=list(data=data)) summary(ml.result) --------------------------------------------------------------------------------------- This works perfectly (except that the default algorithm sometimes tries some negative variance parameters). However, if the parameters (m...

So, I've embarked on my threatened modifications to the mle subset of the stats4 package. Most of what I've done so far has *not* been adding the slick formula interface, but rather making it work properly and reasonably robustly with real mle problems -- especially ones involving reasonably complex fixed and default parameter sets. Some of what I...

Hi the list, I am experiencing several issues with profile.mle (and consequently with confint.mle) (stat4 version 2.9.2), and I have to spend a lot of time to find workarounds to what looks like interface bugs. I would be glad to get feedback from experienced users to know if I am really asking too much or if there is room for improvement. * Problem #1 wi...

R 2.3.0, all packages up to date Linux, SuSE 10.0 Hi I want to calculate AIC or BIC from several results from mle calculation. I found the AIC function, but it does not seem to work with objects of class mle - If I execute the following: ml1 <- mle(...) AIC(ml1) I get the following error messale: Error in logLik(object) : no applicable method for "logLik" Therefore I am using the following to c...

...xp(fit)/rowSums(exp(fit)) sum(Y*log(p)) } grad<- function(b=rep(0,(J-1)*K)){ B=matrix(c(b,rep(0,K)),ncol=J,nrow=K) fit <- X%*%B p<-exp(fit)/rowSums(exp(fit)) Yp <- Y-p Yp<-matrix(rep(t(Yp),each=K),ncol=K*J,by=T) X <- matrix(rep(X,J) ,ncol=K*J) apply(Yp*X,2,sum) } library(stats4) mle(LL) Error in validObject(.Object) : invalid class "mle" object: invalid object for slot "fullcoef" in class "mle": got class "list", should be or extend class "numeric" mle(LL,gr=grad) Error in optim(start, f, method = method, hessian = TRUE, ...)...

Hi Can anybody please suggest me about the documentation of "factanal.fit.mle()" (Not factanal()------ searching factanal.fit.mle() in R always leads to factanal()). Is there any function for doing principal component factor analysis in R. Regards Souvik Bandyopadhyay JRF, Dept Of Statistics Calcutta University [[alternative HTML version deleted]]

I'm trying to figure out the best way of fitting the same negative log-likelihood function to more than one set of data, using mle() from the stats4 package. Here's what I would have thought would work: -------------- library(stats4) ## simulate values r = rnorm(1000,mean=2) ## very basic neg. log likelihood function mll <- function(mu,logsigma) { -sum(dnorm(r,mean=mu,sd=exp(logsigma),log=TRUE)) } mle(minuslog...

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...

I made one typo in my previous mail.   May I ask one statistics related question please? I have one query on MLE itself. It's property says that, for large sample size it is normally distributed [i.e. asymptotically normal]. On the other hand it is "Consistent" as well. My doubt is, how this two asymptotic properties exist simultaneously? If it is consistent then asymptotically it should collaps...

I learned R & MLE in the last few days. It is great! I wrote up my explorations as http://www.mayin.org/ajayshah/KB/R/mle/mle.html I will be most happy if R gurus will look at this and comment on how it can be improved. I have a few specific questions: * Should one use optim() or should one use stats4::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...

Hi there, I had successfully use "MLE" function to solve my problem. Is there anyone knows how to get related information? i.e., value of likelihood function, information matrix, and etc. I know MLE-class can do it but I can not find any information tells me how to do it. Thanks a billions, Yihsu [[alternative HTML version...