similar to: Maximum log likelihood estimates of the parameters of a nonlinear model.

Odette > Wondering how can I generate "Akaike weight" with R? I know the description, > but is there any function to generate by R on the web-site or R library? > I am using GLM or GLMM (family=binomial), so would be appreciated if you > help me. You could have a look at this. http://bm2.genes.nig.ac.jp/RGM2/R_current/library/aod/man/summary.aic.html Which is in the OAD

Hi there I am trying to learn how to compute mle in R for a multinomial negative log likelihood function. I am using for this the book by B. Bolker "Ecological models and data in R", chapter 6: "Likelihood an all that". But he has no example for multinomial functions. What I did is the following: I first defined a function for the negative log likelihood:

Hi Uwe and all, Error message was: error in normalizePath(path) : path[1]: no such file to load Many thanks, Odette On Fri, Jan 23, 2009 at 1:22 AM, Uwe Ligges <ligges@statistik.tu-dortmund.de > wrote: > > > Odette Gaston wrote: > >> Hi folks, >> >> I am currently having the problem with using R 2.8.1 that I cannot install >> some of packages from

I want to find ML estimates of a model using mle2 in bbmle package. When I insert new parameters (for new covariates) in model the log-likelihood value does not change and the estimated value is exactly the initial value that I determined. What's the problem? This is the code and the result: As you see the estimated values for b2 , b3 and b4 are the initial values of them. The

Hi folks, I am currently having the problem with using R 2.8.1 that I cannot install some of packages from CRAN or local drive and somebody may be able to help me. ex) faraway package and lme4 package. I have downloaded them in my hard drive as local, but still R was unable to find the package (message showed up as no such file). I could download most packages, but not all what I want. I showed

Hi everyone I am stuck on specifying my own maximum likelihood function for a special poisson model. My poisson model is as follow: O ~ Pois(b*N + b*RR*E) With O = observed cases b = constant (known) N = number of unexposed persons (known) E = number exposed persons (known) RR = relative risk (value is assumed under a scenario, e.g. RR=2.0) I used rpois to simulate the values of O for several

I am using the Ben bolker's R package "bbmle" to estimate the parameters of a binomial mixture distribution via Maximum Likelihood Method. For some data sets, I got the following warning messages: *Warning: optimization did not converge (code 1: ) There were 50 or more warnings (use warnings() to see the first 50)* Also, warnings() results the following: *In 0:(n - x) : numerical

The mle2 function (bbmle library) gives an example something like the following in its help page. How do I access the coefficients, standard errors, etc in the summary of "a"? > x <- 0:10 > y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8) > LL <- function(ymax=15, xhalf=6) + -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE)) > a <- mle2(LL,

Dear R helpers, I need to estimate a probit model with box constraints placed on several of the model parameters. I have the following two questions: 1) How are the standard errors calclulated in glm (family=binomial(link="probit")? I ran a typical probit model using the glm probit link and the nlminb function with my own coding of the loglikehood, separately. As nlminb does not

Can anyone help me figure out the right way to import a method that is defined as S3 in one package and S4 in another? Specifically: profile() is defined as an S3 method in the stats package: function (fitted, ...) UseMethod("profile") <bytecode: 0xa4cd6e8> <environment: namespace:stats> In stats4 it is defined as an S4 method: stats4:::profile standardGeneric for

Hi all, When I try to estimate the functional response of the Rogers type I equation (for the mle2 you need the package bbmle): > RogersIbinom <- function(N0,attackR2_B,u_B) {attackR2_B+u_B*N0} > RogersI_B <- mle2(FR~dbinom(size=N0,prob=RogersIbinom(N0,attackR2_B,u_B)/N0),start=list(attackR2_B=4.5,u_B=0.16),method="Nelder-Mead",data=data5) I get following error message

Hi there, I'm trying to fit a growth curve to some data and need to use a weighted least squares estimator to account for heteroscedasticity in the data. A weights argument is available in nls that would appear to be appropriate for this purpose, but it is listed as 'not yet implemented'. Is there another package which could implement this procedure? Regards, Robert Brown

Hello, I tried to fit a poisson distribution but looking at the function fitdistr() it does not optimize lambda but simply estimates the mean of the data and returns it as lambda. I'm a bit confused because I was expecting an optimization of this parameter to gain a good fit... If I would use mle() of stats4 package or mle2() of bbmle package, I would have to write the function by myself

Odette Gaston <odette.gaston <at> gmail.com> writes: > > Dear all, > > I have a problem with accessing class attributes. > I was unable to solve this > yet, but someone may know how to solve it. My best guess at your immediate problem (doing things by hand) is that you're not using the whole vector. From your example: Delta <- c(m1 = 0, m2 = 1.8, m3 =

Hi all, I am trying to use plm to estimate coefficients in a model consisting of a system of equations. So far I used mle2 from the package "bbmle", but now I need to test for autocorrelation and mle2 does not provide for the necessary tests. mle2 needs a function as input that might as well consist of many different equations. plm however requires an object of class formula that needs

I'm trying to get started with maximum likelihood estimation with a simple regression equivalent out of Bolker (Ecological Models and Data in R, p302). With this code: #Basic example regression library(bbmle) RegData<-data.frame(c(0.3,0.9,0.6),c(1.7,1.1,1.5)) names(RegData)<-c("x", "y") linregfun = function(a,b,sigma) { Y.pred = a+b*x

Hi everybody, I am using the L-BFGS-B method of the mle2() function to estimate the values of 6 parameters. mle2 uses the methods implemented in optim. As I got it from the descriptions available online, one can use the parscale parameter to tell R somehow what the values of the estimated parameters should be . . . Could somebody please help me understand what one has to do actually with the

Hola! I am on debian testing, all updates up to date. I am running R via emacs and ess, so I canot know if this is an R or emacs/ess problem. What happens is that I want to read an vignette, and calls something like vignette("mle2", package="bbmle") the pdf file opens (for me in Foxit reader), no problem, but then emacs starts to spew a lot of spam, interfering with the

Hi all, I have some problems with the mle2 function > RogersIIbinom <- function(N0,attackR3_B,Th3_B) {N0-lambertW(attackR3_B*Th3_B*N0*exp(-attackR3_B*(24-Th3_B*N0)))/(attackR3_B*Th3_B)} > RogersII_B <- mle2(FR~dbinom(size=N0,prob=RogersIIbinom(N0,attackR3_B,Th3_B)/N0),start=list(attackR3_B=1.5,Th3_B=0.04),method="Nelder-Mead",data=dat) Error in dbinom(x, size, prob, log)

Dear Sir/ Madam, I'm having trouble de-bugging the following - which works perfectly well with optim or optimx - but not with mle2. I'd be really grateful if someone could show me what is wrong. Many thanks in advance. JSC: gompertz<- function (x,t=data) { a3<-x[1] b3<-x[2] shift<-data[1] h.t<-a3*exp(b3*(t-shift))