similar to: Frequency of Data

Hello, I've written a function that plots a few functions in a diagram. The xlim and or ylim is not always the same, and set automatically by R. A legend is part of this object. Now the problem is: where to put the legend? Me would help a function that returns the limits and scaling of the axis. Thanks for your help. Carsten [[alternative HTML version deleted]]

Hi, I've made a function that executes a monte-carlo simulation. It always needs a lot of time until e.g. 1Mio simulation steps are done. So I would like to know, how many percent of the work is already done. In an Excel/VBA Solution I could easily implement a status bar or status window. How could an R-Solution look like? Carsten

Hello, I tried to fit a lognormal distribution by using optim. But sadly the output seems to be incorrect. Who can tell me where the "bug" is? test = rlnorm(100,5,3) logL = function(parm, x,...) -sum(log(dlnorm(x,parm,...))) start = list(meanlog=5, sdlog=3) optim(start,logL,x=test)$par Carsten. [[alternative HTML version deleted]]

Hello, I want to fit a tree parameter distribution to given data. I tried it with sample data using the "fitdistr" function. Here my workflow that didn't had any result: I started with the generalized gamma distr, which is: r*dgamma(x^r,shape,rate) The R-function is: ggamma = function (x,r,shape,rate) r*dgamma(x^r,shape,rate=rate) For the first step I assumed r = 1 and I

Hello, today I've updated on the newest R-Version. But sadly a function I needed didnt want to work: The input is e.g. days(as.Date("21-07-2005","%d-%m-%y")) the error is: Fehler in Math.Date(dts): floor nicht definiert f??r Date Objekte (Error in Math.Date(dts): floor not defined for date objects) Same for year. Only months gives me the correct output. In Version

Hello, I see that the more I work with R and the more the code gets larger I would like to have some graphic support in my quellcode. Is there a browser that could be easily implemened in R? And how do I call it from R? It would be nice if the browser replaces the "fix()" function. Carsten

Hello, maybe that my Question is a "beginner"-Question, but up to now, my research didn't bring any useful result. I'm trying to fit a distribution (e.g. lognormal) to a given set of data (ML-Estimation). I KNOW about my data that there is a truncation for all data below a well known threshold. Is there an R-solution for an ML-estimation for this kind of data-problem? As

Hi all, I'm using the function "fitdistr" (library MASS) to fit a distribution to given data. What I have to do further, is getting the log-Likelihood-Value from this estimation. Is there any simple possibility to realize it? Regards, Carsten

Hello, I have to generate a couple of random datasets to validate an actuarial software. Each set has to contain at least two columns: (1) Date of the loss events occurrence; (2) Severity of loss I'm generating the dates on a monthly basis by, for example: ts(matrix(rpois(36,10)), start=c(2003, 1), frequency=12). >From that output I generate dates "by hand", what is REALLY time

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Hello, I want to fit a distribution to a dataset. Important is not the "overall" fitting but the fitting in the tail (e.g. all observations > x or the n highest values). Standard ML-estimation sometimes doesn't work here very well. We see that especially when we have truncated datasets the algorithms won't converge. In the case of lognormal distribution: It seems that the

Hello, I want to find the parameters mu and sigma that minimize the following function. It's important, that mu and sigma are strictly positive. ----------------- optimiere = function(fmean,smean,d,x,mu,sigma) { merk = c() for (i in 1:length(d)) merk=c(merk,1/(d[i]^2)*(d[i]-1/(fmean*(1-plnorm(x[i],mu,sigma))))^2) return(sum(merk)) } ----------------- To do that I'm using the nlm

Hi, my problem is the following: I have a large database of insurance-damage data and want to model the frequency of these events. So to fit a distribution on my frequency-data I want to count the number of events in each month via the date of occurrence. Therefor I use this command which works very well: count_table <- table(months(date_occ),years(date_occ)) But there is another

Hello, I want to solve some two dimensional equation system with R. Some systems are not solvable analytically. Here is an example: (I) 1/n*sum{from_i=1_to_n}(Xi) = ln lambda + digamma(c) (II) mean(X) = x / lambda I want to find lambda and c, which R-function could do that task? Carsten [[alternative HTML version deleted]]

Hello Dennis! Please send me the links to your streams. I'm interested in the audioquality. regards Gagarin M. Stockholm www.mrs.se Dennis Heerema <Dennis at Heerema.net> wrote: >Hello Jordan, > >I run a test stream in OPUS format. >I find the quality of OPUS way better at low bitrates then VORBIS. >Thus better quality at lower bitrates, but also on higher bitrates the

Hi, two questions - I think simple to solve for you ... (1) I've written a function containing some loops. Each loop will generate a few outputs. Finally I have to combine them to get something like a "spreadsheet" that my colleagues can import in EXCEL. Up to now I'm doing it as follows: With each loop-step I assign new values for each "column" of my desired output

Hello, I've the following setting: (1) Data from a source without truncation (x) (2) Data from an other source with left-truncation at threshold u (xu) I have to fit a model on these these two sources, thereby I assume that both are "drawn" from the same distribution (eg lognormal). In a MLE I would sum the densities and maximize. The R-Function could be:

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Hello, for a frequency modelling problem I want to combine expert knowledge with incoming real-life data (which is not available up to now). The frequency has to be modelled with a poisson distribution. The parameter lambda has to be normal distributed (for certain reasons we did not NOT choose gamma althoug it would make everything easier). I've started with the subsequent two functions to

Hello, I want to estimate the parameters of a binomial distributed rv using MLE. Other distributions will follow. The equation system to solve is not very complex, but I've never done such work in R and don't have any idea how to start... The system is: (1) n*P = X (2) [sum {from j=0 to J-1} Y{j} /(n-j)] = -n * ln (1-X / n) where * only X is given (empirical mean)