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Dear R community, I want to estimate coefficients in a (non-linear) system of equations using 'mle2' from the "bbmle" package. Right now the whole data is read in as just one long time series, when it's actually 9 cross sections with 30 observations each. I would like to be able to test and correct for autocorrelation but haven't found a way to do this in this package.

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

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

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,

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 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 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:

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

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

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)

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

Hi everyone! I already posted http://r.789695.n4.nabble.com/Declaring-a-density-function-with-for-loop-td4635699.html a question on finding density values of a new Binomial like distribution which has the following pmf: http://r.789695.n4.nabble.com/file/n4636134/kb.png Thank fully http://r.789695.n4.nabble.com/template/NamlServlet.jtp?macro=user_nodes&user=124474 Berend Hasselman and

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

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))

Hi, I am using the mle2 method of the package 'bbmle'. The method is calling as far as I understood it the optim method "L-BFGS-B" (this is the method I use). The latter one allows the user to impose box constraints on the variables, i.e. to give lower and upper bounds. It is important that the initial values satisfy the constraints. In my problem, it is the case. I do not know

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

I am having difficulty with evaluation/environment construction for a formula to be evaluated by model.matrix(). Basically, I want to construct a model matrix that first looks in "newdata" for the values of the model parameters, then in "object at data". Here's what I've tried: 1. model.matrix(~f,data=c(newdata,object at data)) -- fails because something (terms()?)

Hello, After putting together interaction code that worked for a single pair of interactions, when I try to evaluate two pairs of interactions( flowers*gopher, flowers*rockiness) my computer runs out of memory, and the larger desktop I use just doesn't go anywhere after about 20 minutes. Is it really that big a calculation? to start: mle2(minuslogl = Lily_sum$seedlings ~ dnbinom(mu = a,

Hello R Help, I am trying solve an MLE convergence problem: I would like to estimate four parameters, p1, p2, mu1, mu2, which relate to the probabilities, P1, P2, P3, of a multinomial (trinomial) distribution. I am using the mle2() function and feeding it a time series dataset composed of four columns: time point, number of successes in category 1, number of successes in category 2, and

Dear R community, I would like to compare the degree of aggregation (or dispersion) of bacteria isolated from plant material. My data are discrete counts from leaf washes. While I do have xy coordinates for each plant, it is aggregation in the sense of the concentration of bacteria in high density patches that I am interested in. My attempt to analyze this was to fit negative binomial