similar to: Incomplete output with `sn' library package

Hello - I apologize if this question is simple/obvious, but I couldn't find a satisfactory answer online, and I am not very accustomed to working with R (Matlab is my poison. :-)). Any help would be greatly appreciated. I have a model with a three-level factor and a continuous covariate. The call I use is: mymodel <- glm(Response ~ Factor_covariate + continuous_covariate - 1,

Hi, I need to create a plot (type = "h") and then overlay a skewed-normal curve on this distribution, but I'm not finding a procedure to accomplish this. I want to use the plot function here in order to control the bin distributions. I have explored the sn library and found the dsn function. dsn uses known location, scaling and shape parameters associated with a given input

I have a data with 236 observations. After plotting the histogram, I found that it looks like non-central t distribution. I would like to get MLE for mu and df. I found an example to find MLE for gamma distribution from "fitting distributions with R": library(stats4) ## loading package stats4 ll<-function(lambda,alfa) {n<-200 x<-x.gam

Dear All, I am working with skewed-t copula in my research recently, so I needed to write an mle procedure instead of using a standard fit one; I stick to the sn package. On subsamples of the entire population that I deal with, everything is fine. However, on the total sample (difference in cross-sectional dimension: 30 vs 240) things go wrong - the objective function diverges to infinity. I

Hi, Is there a function in R that I can use to fit the data with skew t distribution? Speaking in detail, I first used the kernel density estimation to fit my data, then I drew the skew t using my specified location, scale, shape, and df to make it close to the kernel density. Now I want to get the parameter estimations of the skew t which give me the closet density to the kernel density.

Dear R users and Dear authors of the distr package and sequels I am trying to use the (very nice) package distrMod as I want to implement maximum likelihood (ML) fit of some univariate data for which I have derived a theoretical continuous density (pdf). As it is a parametric density, I guess that I should implement myself a new distribution of class AbscontDistributions (as stated in the pdf

Folks, I'm kind of newbie in R, but with some background in Matlab and VBA programming. Last month I was implementing a Maximum Likelihood Estimation in Matlab, but the algorithms didn't converge. So my academic advisor suggested using R. My problem is: estimate a mean reverting jump diffusion parameters. I've succeeded in deriving the likelihood function (which looks like a gaussian

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 =

hello users of the SN package, i thought i post here some useful help on R code on the 4 moments for the skew t sampling gives seldom good results for skewness and kurtosis, so one really needs the analytical results, it took me some time to get it from the article Azzalini, A. & Capitanio, A. (2003), Distributions generated by perturbation of symmetry with emphasis on a multivariate

Hello please note that the code at https://stat.ethz.ch/pipermail/r-help/2006-August/110892.html to compute indices of skewness and kurtosis for the skew-t distribution is not correct. It has been kindly pointed out that i made some error in this code, which was a bit too quickly copied from the paper. The 'sn' package already contains a facility for computing the cumulants, namely

Hello R users, before asking my question I'd like to stress that I'm an (absolute) beginner in using R, but enthused about the incredible possibilities of it. So I hope my questions are not too stupid. Here's my problem: I have a dataset with skewed distributions. In order to obtain approx multivariate normality by a Box-Cox-Transformation, I used the "Rfwdmv Package" to

With DSC, bpp can be fractional in multiples of 1/16. Change drm_dp_calc_pbn_mode to reflect this, adding a new parameter bool dsc. When this parameter is true, treat the bpp parameter as having units not of bits per pixel, but 1/16 of a bit per pixel v2: Don't add separate function for this Cc: amd-gfx at lists.freedesktop.org Cc: nouveau at lists.freedesktop.org Cc: intel-gfx at

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.

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,

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 http://stat.ethz.ch/R-manual/R-devel/library/stats4/html/mle.html

Hi the list, I would need some advice on something that looks like a FAQ: the possibility of providing vectors to optim() function. Here is a stupid and short example summarizing the problem: -------------------------------- example 1 ------------ 8< ---------------------- library(stats4) data <- rnorm(100,0,1) lik1 <- function(m, v, data) { N <- length(data) lik.mean <-

Dear R-Helpers: I have tried everything I can think of and hope not to appear too foolish when my error is pointed out to me. I have some real data (18 points) that look linear on a log-log plot so I used them for a comparison of lm() and survreg. There are no suspensions. survreg.df <- data.frame(Cycles=c(2009000, 577000, 145000, 376000, 37000, 979000, 17420000, 71065000, 46397000,

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) { loglik.fun <- match.fun(FUN)

I'm running FreeBSD 8 on a Sheevaplug (ARM5 processor) I compiled and installed Samba 3.3.9 from ports using defaults and defaults for dependencies. I left the smb.conf file untouched, ran testparm and immediately received the coredump. The same happens running smbd and nmbd. Can anyone help me resolve? http://old.nabble.com/file/p26879957/testparm.core.gz testparm.core.gz -- View this

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've done breaks backward