similar to: Problems in programming a simple likelihood

R Users: I am trying to estimate a model of fertility behaviour using birth history data with maximum likelihood. My code works but is extremely slow (because of several for loops and my programming inefficiencies); when I use the genetic algorithm to optimize the likelihood function, it takes several days to complete (on a machine with Intel Core 2 processor [2.66GHz] and 2.99 GB RAM). Computing

Hi R users, I have a couple of questions about some problems that I am facing with regard to numerical integration and optimization of likelihood functions. Let me provide a little background information: I am trying to do maximum likelihood estimation of an econometric model that I have developed recently. I estimate the parameters of the model using the monthly US unemployment rate series

Hi I need help figure out how to fix my code. When I call into R >optimize(llik,init.params=F) I get this error message ####Error in optimize(llik, init.params = F) : element 1 is empty; the part of the args list of 'min' being evaluated was: (interval)#### My data and my code looks like below. R_j R_m 0.002 0.026567296 0.01 0.003194435 . . . . . . . . 0.0006

Hello, I am having trouble using "optim". I want to maximalise a function to its parameters [kind of like: univariate maximum likelihood estimation, but i wrote the likelihood function myself because of data issues ] When I try to optimize a function for only one parameter there is no problem: llik.expo<-function(x,lam){(length(x)*log(lam))-(length(x)*log(1-exp(-1*lam*

Hi I have developed the code below. I am worried that the parameters I want to be estimated are "not being found" when I ran my code. Is there a way I can code them so that R recognize that they should be estimated. This is the error I am getting. > out1=optim(llik,par=start.par) Error in pnorm(au_j, mean = b_j * R_m, sd = sigma_j) : object 'au_j' not found #Yet

Hi May you help me correct my loop function. I want optim to estimates al_j; au_j; sigma_j; b_j by looking at 0 to 20, 21 to 40, 41 to 60 data points. The final result should have 4 columns of each of the estimates AND 4 rows of each of 0 to 20, 21 to 40, 41 to 60. ###MY code is n=20 runs=4 out=matrix(0,nrow=runs) llik = function(x) { al_j=x[1]; au_j=x[2]; sigma_j=x[3]; b_j=x[4]

Hi I have a code that calculate maximisation using optimx and it is working just fine. I want to extend the code to run several colomns of R_j where j runs from 1 to 200. If I am to run the code in its current state, it means I will have to run it 200 times manually. May you help me adjust it to accomodate several rows of R_j and print the 200 results. ***Please do not get intimidated by the

Good Afternoon, I am relatively new to R and have been trying to figure out how to estimate regression coefficients using the MLE method. Some background: I am trying to examine scenarios in which certain estimators might be preferred to others, starting with MLE. I understand that MLE will (should) produce the same results as Ordinary Least Squares if the assumption of normality holds. That

I have had some weird results using the optim() function. I wrote a probit likelihood and wanted to run it with optim() with simulated data. I did not include a gradient at first and found that optim() would not even iterate using BFGS and would only occasionally work using SANN. I programmed in the gradient and it iterates fine but the estimates it returns are wrong. The simulated data work

Hello, the more general thing I'd like to learn here is how to compute Function of Data on the basis of grouping determiend by n variables. In terms of the reason why I am interested in this, I need to compute the average of my data based on the value of the month and day across years. I have come up withy the code below which, as far as I can see, does what I need but getting either a more

Hi, I tried to compute maximum likelihood under gamma distribution, using nlm function. The code is this: __BEGIN__ vsamples<- c(103.9, 88.5, 242.9, 206.6, 175.7, 164.4) mlogl <- function(alpha, x) { if (length(alpha) > 1) stop("alpha must be scalar") if (alpha <= 0) stop("alpha must be positive") return(- sum(dgamma(x, shape = alpha, log = TRUE)))

Hi, I have a problem in programming for bootstrapping. I don't know why it show the error message. Please see my code below: #'st' is my original dataset. #functions of 'fml.mlogl','pcopula.fam4','ltd','invltd' are already defined boot.OR<-function(data,i) { E=data[i,] ml1<-glm(c_VAsex90_bf ~ trt,family=binomial,data=E) ml2<-glm(c_VAsex90_bm ~

Hello all, Is AIC calculated incorrectly in lmer? It appears as though it uses AIC = -2*logLik - 2*#parms, instead of -2*LogLik + 2*#parms? Below is output from one of many models I have tried: Generalized linear mixed model fit using PQL Formula: cswa ~ pcov.ess1k + (1 | year) Data: ptct50.5 Family: poisson(log link) AIC BIC logLik deviance 224.8466 219.19 -114.4233 228.8466

I am trying to maximise a complicated loglikelihood function with the "optim" command. Is there some way to get to know the estiamtes at each iteration? When I put "control=list(trace=TRUE)" as an option in "optim", I just got the initial and final values of the loglikelihood, number of iterations and whether the routine has converged or not. I need to know the

Hi! As per Thomas' advice, I upgraded R by using "update.packages()" and got the following warning messages: Warning messages: 1: installation of package 'lmtest' had non-zero exit status in: install.packages(update[, "Package"], instlib, contriburl = contriburl, 2: installation of package 'quadprog' had non-zero exit status in: install.packages(update[,

Hi All, I have a data frame with a group of variables named b1, b2, b3, ..., b18. These variables take the value 1, 2 or NA. For each observation, I want to do some computation by looping over the values for the group of variables: b1 to b18. In STATA I would do: forval i=1/18 { --- use b`i' for computation ---- } How can this be done in R? Deepankar

Anyone got this working over a LAN under CentOS? The instructions claim it is easy to connect after pressing the WPS button on printer and router. Sadly, I don't see a WPS button on my Linksys WRT54GL router. When I press the WPS button on the printer the WiFi icon flashes, but I don't see any attempt to connect in /var/log/messages on my CentOS-5.5 server. Any advice or suggestions

I tried to install the package "tseries" but could not do so. Any suggestions are welcome. I am using Ubuntu Linux and here is what I did: $ sudo R Then once in R, I did > install.packages("tseries", dep=TRUE) which gave a long list of errors. Going through them, I saw that the basic problem was that the package "grid" was not found by the packages (like

Dear all, I'm using igraph for some analysis about the network I have. I have a question about the function "power.law.fit". I wonder if there is any test for checking whether the "power.law.fit" is good for the input, i.e., under which situation, could we use this function to get a reliable result. I'm afraid even I input a random graph without any property of

I am trying to get R to run on a Red Hat 8 system, I am running R version 1.6.2 with Tcl/Tk version 8.3.5 installed. When I try to test R from the data_sets directory I get the error message; Error in firstlib(which.lib.loc, package) : Tcl/Tk support is not available on this system Error in fileBrowser(textToShow = "Choose one CDF file" , nSelect = 1, : tcl/tk library not available