similar to: mle function

Dear, I'm starting on R language. I would like some help to implement a MLE function. I wish to obtain the variables values (alpha12, w_g12, w_u12) that maximize the function LL = Y*ln(alpha12 + g*w_g12 + u*w_u12). Following the code: rm(list=ls()) ls() library(stats4) Model = function(alpha12,w_g12,w_u12) { Y = 1 u = 0.5 g = -1 Y*log(alpha12 + g*w_g12 + u*w_u12) } res =

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

I am just trying to teach myself how to use the mle function in R because it is much better than what is provided in MATLAB. I am following tutorial material from the internet, however, it gives the following errors, does anybody know what is happening to cause such errors, or does anybody know any better tutorial material on this particular subject. >

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)

Hi, I have written out the log-likelihood function to fit some data I have (called ONES20) to the non-central chi-squared distribution. >library(stats4) >ll<-function(lambda,k){x<-ONES20; 25573*0.5*lambda-25573*log(2)-sum(-x/2)-log((x/lambda)^(0.25*k-0.5))-log(besselI(sqrt(lambda*x),0.5*k-1,expon.scaled=FALSE))} > est<-mle(minuslog=ll,start=list(lambda=0.05,k=0.006))

Hi, Actually I am facing a similar problem. I would like to fit both an ordinary (symmetric) and a non-central t distribution to my (one-dimensional) data (quite some values.. > 1 mio.). For the symmetric one, fitdistr or funInfoFun (using fitdistr) from the qAnalyst package should do the job, and for the non-central one.. am I right to use gamlss(x ~ 1, family=GT()) ? Anyway, I am a little

I am interested in estimating the variance components from different groups of variables in a linear regression. For example, I want to model dependent variable ?Y? based on six covariates as follows: (these are land use types measured at three scales for each Y, "Local", "Riparian", and "Catchment" - these are not nested as there is a unique area for each Y -

Dear R-help readers, I am writing to you in order to ask you a few questions about distribution fitting in R. I am trying to find out whether the set of event interarrival times that I am currently analyzing is distributed with a Gamma or General Pareto distribution. The event arrival granularity is in minutes and interarrival times are in seconds, so the values I have are 0, 60, 120, 180, and

Good morning, I have something like this: names(coint_tests) <- apply(b,2,paste, collapse="_") which prints 15 names like: A_B, C_D, E_F, ... AA,B,C,D.. Are time series. Then there is a vector called coint_tests of length 15 which yields "yes" or "no". I need to add a function to plot the time series Ai_Bi if the coint_tests vectors gives me a "YES".

my problem actually arised with fitting the data to the weibulldistribution, where it is hard to see, if the proposed parameterestimates make sense. data1:2743;4678;21427;6194;10286;1505;12811;2161;6853;2625;14542;694;11491; ?? ?? ?? ?? ?? 14924;28640;17097;2136;5308;3477;91301;11488;3860;64114;14334 how am I supposed to know what starting values i have to take? i get different

Hello. I am trying to fit my data sample x with different distributions such that the integral from min(x) to max(x) of the fitted distribution will be one. Therefore I have wrote my own log-likelihood functions and then I am using mle {stats4}. So, for example: ll_gamma <- function(a,b) { integrand <- function(y){dgamma(y, shape=a, rate=b)} integ_res <-

Dear all I've got the dataset data:2743;4678;21427;6194;10286;1505;12811;2161;6853;2625;14542;694;11491; ?? ?? ?? ?? ?? 14924;28640;17097;2136;5308;3477;91301;11488;3860;64114;14334 I know from other testing that it should be possible to fit the data with the exponentialdistribution. I tried to get parameterestimates for the exponentialdistribution with R, but as the values of the parameter

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

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...

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]]

Hi just wondering if there is a package that can get the maximum likelihood or method of moments estimator for distributions with censored data? The distributions I'm interested in are: Exponential, pareto, beta, gamma and lognormal. -- View this message in context: http://www.nabble.com/MLE-for-censored-distributions-in-R-tp15022863p15022863.html Sent from the R help mailing list archive at

I am a student (and very to new to R) working on a senior design project that is attempting to determine the demand distributions for single copy newspaper draws at individual sales outlet locations. Our sales data is right-censored, because sell-outs constitute a majority of the data, and we are also testing the relevance of including covariates (weather, seasonality, economic condition, etc.).

hi, I want to use EM for MLE estimation.WHAT would be the relevant package or code?? Regards, Subhajit. [[alternative HTML version deleted]]

Hi I am beginner with R I would like to compare the performance of Maximum likelihood Estimator (MLE) and Method of Moment Estimator (MME) effect probability of rejection, p. My MLE is x=rlnorm(10,meanlog = 2, sdlog =5) x xbar=mean(x) ssqrt=var(x) xbar ssqrt #MLE y=log(x) m1=mean(y) s1=var(y) m1 s1 #MME m2=log(xbar^2/sqrt(ssqrt+xbar^2)) s2 = log((ssqrt+xbar^2)/xbar^2) m2

Dear [R]-list, I'm looking for a classic equivalent of the wle.gamma function (library wle) that estimate robustly the shape and the scale parameters of gamma data. I have a vector of iid gamma rv : >data=rgamma(100,shape=10,scale=3) and a vector of their weights: >weights=c(rep(.5/70,70),rep(.25/20,20),rep(.25/10,10)) and want to estimate the scale and shape of the gamma