similar to: unexpected results involving the skellam distribution

Dear All, I am trying to use the do.call command to avoid tedious loops in R when I need to call repetitively a function. I am experiencing a problem when the arguments of a function are given with a vector (I need to express then this way to be able later on to integrate them numerically with the adapt package). Consider the small script: remove(list=ls()) #library(adapt) # first a list of the

Dear All, I'd like to know if it is possible to create animations with R. To be specific, I attach a code I am using for my research to plot some analytical results in 3D using the lattice package. It is not necessary to go through the code. Simply, it plots some 3D density profiles at two different times selected by the user. I wonder if it is possible to use the data generated for different

I'm not aware of any routine that those the job, although I think that it could be relatively easily done by multiplication the manifest variable vector with the estimates for the specific effect. To make an example: v1; v2; v3; v4 are manifest variables that loads on one y latent variablein a data frame called "A" the code for the model should be like: model <-specifymodel( y

Dear 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 Help, I have two nested negative log-likelihood functions that I am optimizing with the spg function [BB package]. I would like to perform model selection on these two objective functions using AIC (and possibly anova() too). However, the spg() function returns a list and I need a fitted model object for AIC(), ICtab() [bbmle package], or anova(). How can I perform AIC-based model

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

Hi, The code below is giving me this error message: Error in while (err > eps) { : missing value where TRUE/FALSE needed In addition: Warning messages: 1: In dnbinom(x, size, prob, log) : NaNs produced 2: In dnbinom(x, size, prob, log) : NaNs produced I know from the help files that for dnbinom "Invalid size or prob will result in return value NaN, with a warning", but I am not able

Moved to R-help because there's no obvious financial content. Michael On Sat, Apr 6, 2013 at 10:56 AM, Stat Tistician <statisticiangermany at gmail.com> wrote: > Hi, > I want to implement the EM algorithm manually, with my own loops and so. > Afterwards, I want to compare it to the normalmixEM output of mixtools > package. > > Since the notation is very advanced, I

I am using JRI (Java R Interface) library in order to call R from within my Java application. But since the "rmu1" and "rmu2" ,see the following code, are objects of type S4 once i run the application the value of Null will be returned for both of them. On this regard, i would appreciate it if anyone can tell me how i am going to display and/ or convert these objects to Java

Hi, i would to know, if someone have ever write the code to estimate the parameter (mixing proportion, mean, a var/cov matrix) of a mixture of two multivariate normal distribution. I wrote it and it works (it could find mean and mixing proportion, if I fix the var/cov matrix), while if I fix anything, it doesn't work. My suspect is that when the algorithm iterates the var/cov matrix, something

Hi, i would to know, if someone have ever write the code to estimate the parameter (mixing proportion, mean, a var/cov matrix) of a mixture of two multivariate normal distribution. I wrote it and it works (it could find mean and mixing proportion, if I fix the var/cov matrix), while if I fix anything, it doesn't work. My suspect is that when the algorithm iterates the var/cov matrix, something

Dear All, I may look ridiculous, but I am puzzled at the behavior of the nls with a fitting I am currently dealing with. My data are: x N 1 346.4102 145.428256 2 447.2136 169.530634 3 570.0877 144.081627 4 721.1103 106.363316 5 894.4272 130.390552 6 1264.9111 36.727069 7 1788.8544 52.848587 8 2449.4897 25.128742 9 3464.1016 7.531766 10 4472.1360 8.827367 11

I'm trying to build a SEM using the sem package. I'll attach my model specification below. When I turn debug=TRUE, it seems as if I'm getting to convergence because I get this message: Successive iterates within tolerance. Current iterate is probably solution. However, at the end of the process I get this message: Warning message: In sem.default(ram = ram, S = S, N = N,

I fitted a gaussian mixture to my financial data. The data can be found here: http://uploadeasy.net/upload/32xzq.rar I look at the density with plot(density(dat),col="red",lwd=2) this has a skew of library(e1071) skewness(dat) -0.1284311 Now, I fit a gaussian mixture according to: f(l)=πϕ(l;μ1,σ21)+(1−π)ϕ(l;μ2,σ22) with:

On 8/3/07 22:12, "Gad Abraham" <g.abraham at ms.unimelb.edu.au> wrote: > Laura Hill wrote: >> >> >> On 7/3/07 00:15, "Gad Abraham" <g.abraham at ms.unimelb.edu.au> wrote: >> >>>> On 6 Mar 2007, at 08:54, Laura Hill wrote: >>>> >>>>> Hi, >>>>> >>>>> My name is

dear list, as a part my problem. I have to estimate some parameters using ML estimation. The form of the likelihood function is not straight forward and I had to use a for loop to define the function. I used "optim" to maximise the result but was not sure of the programme. To validate my results, I tried to write a function to obtain the MLE of a bivariate normal in the same manner. On

Today I tried to install the R-2.5.0 (currently running R-2.4.1) on Mandriva Linux. The ./configure and make commands seem to run fine, but "make check" gives the following messages: running regression tests make[3]: Entering directory `/home/ethomp04/R-2.5.0/tests' running code in 'reg-tests-1.R' ...make[3]: *** [reg-tests-1.Rout] Error 1 make[3]: Leaving directory

Hi: I'm trying to estimate a latent variable model in mnl discrete choice framework using R. I need to do first a uni dimensional integral within each observation (row) in the database and then sum over observations. I'm stacked in the point shown below. Apparently I have a dimensionality problem in the definition of the integral. Maybe it does not identify that what I need is only one

I am wanting to plot a 95% confidence band using segplot, yet I am wanting to have groups. For example if I have males and females, and then I have them in different races, I want the racial groups in different panels. I have this minor code, completely made up but gets at what I am wanting, 4 random samples and 4 samples of confidence, I know how to get A & B into one panel and C&D in to

Hi I have a couple of functions that work on the object created by another R command and then print out or summarise the results of this work. The main function is defined as: hotelling.t <- function(obj) { #internal commands } I then have print.hotelling.t() that takes the list returned by hotelling.t and prints it with some extra significance calculations, formatting, etc. I want to