similar to: using function instead of formula in plm

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.

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

Hello, I have posted this problem before, but thought I try to explain it a bit better. I'm using the function plm to create a fixed effects model for panel data, my method is therefor "within" my effect is "twoways". My Data contains unbalanced Panels due to missing Values, but contains 309 observation for 11 variables (incl. response), with no missing Values. These 309

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,

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:

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

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

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

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 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)

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()?)

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

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 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))

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, I've been trying to fit Arrellano-Bond model with pgmm but I am getting very strange errors. I've looked around and found no reference to them. I've specified the model in dozens of different ways, and each seems to give me a new kind of error. This leads me to believe this has to do with the way the data is specified, but I can't see anything thats wrong with. My

Hello, I am trying to use the plm package for panel econometrics. I am just trying to get started and load my data. It seems from most of the sample documentation that I need to use the pdata.frame function to get my data loaded. However, even after installing the "plm" package, my R installation cannot find the function. I am trying to follow the example in plmEN.pdf (