similar to: multivariate lognormal distribution simulation in compositions

Dear All,   is there a function in R that would help me convert a covariance matrix built based on arithmetic returns to a covariance matrix from log-returns?   As an example of the means and covariance from arithmetic:   mu <-c(0.094,0.006,1.337,1.046,0.263) sigma

I have to calculate the logreturn variance in the Heston model. How can I do? Do you know some function that calculates it? Thank you Barbara -- ________________________________________________________ Le informazioni contenute in questo messaggio di posta elettronica sono strettamente riservate e indirizzate esclusivamente al destinatario. Si prega di non leggere, fare copia, inoltrare a

Mich besch?ftig folgende Fragestellung. Ich kenne die Verteilung (lognormal) zus?tzlich weiss ich das 99%, das 90% und das 1% Quantil. Gibt es in R eine M?glichkeit die Lognormalverteilung zu finden, das heisst den korrespondierenden logmean und logsd? Vielen Dank f?r ihre Hilfe Gruss Yvonne

Is there an R-language interface to the R API C-language functions Rf_logspace_add() and Rf_logspace_sub()? I don't see one but I may not looking under the right name. Various packages have functions which do that same sort of thing (log(exp(x)+exp(y)) and log(exp(x)-exp(y)) without unnecessary floating point errors). They have names like matrixStats::logSumExp(lx, na.rm=FALSE, ...)

Iulia, You need to check the contents of the hello.ll (or hello.bc) file rather than the input *.c file - ultimately that's what opt consumes. Try compiling without any optimisations: clang -O0 -emit-llvm -S hello.c -c -o hello.ll -Andrzej On 18/09/2019 11:34, iulia_s24 wrote: > Hi, > > Actually I copy-pasted step 5 from the article in which I found the > solution, in the text

Hi. While submitting SDR-0149 on ZFS bug I encountered problem with format utility. This is v240 with snv_29 with internal disks. On s0 slices there is zfs pool (which is not imported). I did unload zfs modules then moved zfs driver and run format. Now format quits just because there''s no zfs module (and not even one zfs pool is imported). It shouldn''t behave that

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I notice compositions was removed on CRAN, that's one I want to try out. I downloaded the last tarball, the build output doesn't look that bad. A warning, no errors. $ R CMD build compositions * checking for file ?compositions/DESCRIPTION? ... OK * preparing ?compositions?: * checking DESCRIPTION meta-information ... OK * cleaning src * checking for LF line-endings in source and make

Dear List, I am trying to fit Truncated Lognormal to a data set that is 'truncated' from above a certain value, say, 0.01. Below is what I was able to come up with. I would appreciate it if you could review and make any necessary changes. # This is modified off the code for 'dtnorm' of library(msm). dtlnorm <- function (n, mean = 0, sd = 1, lower = -Inf, upper = Inf) {

Hi everyone I am attempting to estimate a model with a frailty effect distributed as a lognormal variable.I am using the following code: frailtyPenal(formula, data, ..., RandDist = "LogN") I get the following error message: Error in frailtyPenal(Surv(,) ~ + , : unused argument(s) (RandDist = "") What can I do? Thanks for the help [[alternative HTML version deleted]]

Hi R Users, I'm going to estimate via. ML the parameters in Poisson Lognormal model. The model is: x | lambda ~ Poisson(lambda) lambda ~ Lognormal(a,b) Unfortunately, I haven't found a useful package allowing for such estimation. I tried to use "poilog" package, but there is no equations and it's hard to understand what exactly this package really does. Using it I get the

Hi, I am trying to learn lognormal mixture models with EM. I was wondering how does one compute the log likelihood. The current implementation I have is as follows, which perform really bad in learning the mixture models. __BEGIN__ # compute probably density of lognormal. dens <- function(lambda, theta, k){ temp<-NULL meanl=theta[1:k] sdl=theta[(k+1):(2*k)]

Full_Name: Anthony Gichangi Version: 1.90 OS: Windows XP Pro Submission from: (NULL) (130.225.131.206) The function rlnorm generates negative values for lognormal distribution. x- rlnorm(1000, meanlog = 0.6931472, sdlog = 1) Regards Anthony

I am trying to fit a lognormal distribution to a set of data and test its goodness of fit with regard to predicted values. I managed to get so far: > y <- c(2,6,2,3,6,7,6,10,11,6,12,9,15,11,15,8,9,12,6,5) > library(MASS) > fitdistr(y,"lognormal",start=list(meanlog=0.1,sdlog=0.1)) meanlog sdlog 1.94810515 0.57091032 (0.12765945) (0.09034437) But I would

Dear experts! Is there a package that enables to create the bivariate log-normal variables? Thanks a lot, Vicky Landsman. [[alternative HTML version deleted]]

I'm trying to produce empirical bayes estimates based on the lognormal model in disease mapping Is there a way this can be done in R? thanks Oarabile

Hi,R-list! I am a newbie to the R and what I want to know mostly now is as follow: Using "rnorm()",we could get the random numbers with normal distribution, but how to generate the random numbers with the lognormal distribution? Thank you in advance! 仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭guangxing at ict.ac.cn 仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭2005-11-18

Dear all, I want to compare some different models for a dataset by QQ plots and AIC. I get the following AICs: - linear model: 19759.66 - GAMLSS model: 18702.7 - linear model with lognormal response: -7862.182 The QQ plots show that the lognormal model fits better than the linear model, but still much worse than the GAMLSS. So, in my opinion, the AIC of the lognormal model should be between the

Dear R users I use simulated data to evaluate a model by sampling the parameters in my model from lognormal distributions. I would like these (lognormal distributed) parameters to be correlated, that is, I would like to have pairwise samples of 2 parameters with a given correlation coefficient. I have seen that a covariance matrix can be fixed when generating random variables from a

Dear list members, I have a set of claims data, which are in ranges and the shape of the distribution is relatively different. I have looked through R help threads and found out that an ideal way is suggested for the gamma distribution ML fitting for grouped data. I just wonder if there is any method that works for lognormal or pareto distribution? An example would be: