similar to: Compare two Power law or Exponential distributions

Dear R Users, Does anyone know of a package which can generate random realisations of a double-exponential jump diffusion process with a drift ? Something where I can specify the likelihoods of an up or a down jump, the drift rate, and the mean size, and get back a vector of realisation of the process (for purposes of a Monte-Carlo). Kind regards, Tolga

Hello! I need to test the adjustment of a (Negative) Exponential Distribution to a dataset. The parameter of the distribution is unknown. What is the appropriate test to do? I've tried the ks.test, although I think this isn't the appropriate one, as I don't know the parameter. Can anybody help me? Thanks in advance, Janete -- Janete da Silva Borges janeteborges at gmx.net Ab

Dear R-users! I would like to fit exponential, Weibull and log-logistic via glm() like functions. Does anyone know a way to do this? Bellow is a bit longer description of my problem. Hm, could family() be adjusted/improved/added to allow for these distributions? SAS procedure GENMOD alows to specify deviance and variance functions to help in such cases. I have not tried that option and I do not

Hello! I must estimate the parameters of a exponential power density. There is the normalp package, but this works only for a shape parameter bigger than 1. But what should i do if the shape parameter is less than 1? (Sorry for my english) Thank you very much for help! -- View this message in context: http://www.nabble.com/Parameters-of-exponential-power-density-tp20774495p20774495.html Sent from

Hi all, Just a random shot in the dark. Does anyone have/know of a function for the CDF of a generalized error dist? -- Elliot Williams (ewilliams at ucsd.edu) Economics Department, UC San Diego -------------- next part -------------- An embedded message was scrubbed... From: Elliot Williams <ewilliams at ucsd.edu> Subject: [R] Generalized Error Distribution (Exponential Power) CDF?

Dear All I am trying to replicate a numerical application (not computed on R) from an article. Using, ks.test() I computed the exact D value shown in the article but the p-values I obtain are quite different from the one shown in the article. The tests are performed on a sample of 37 values (please see "[0] DATA" below) for truncated Exponential, Pareto and truncated LogNormal

Dear All, I have two questions regarding distribution fitting. I have several datasets, all left-truncated at x=1, that I am attempting to fit distributions to (lognormal, weibull and exponential). I had been using fitdistr in the MASS package as follows: fitdistr<-(x,"weibull") However, this does not take into consideration the truncation at x=1. I read another posting in this

Hi list, I have a group of data. It looks like they follow a exponential distribution. In R, how can I esimate lamda, that is the rate in pexp, of the distribution and can I use Kolmogorov-Smirnov for hypothesis testing in such a situation? I have read the "8.2 Examing the distribution of a set of data" of "An Introduction to R" but I did not find any clues on this issue.

Any one know is there any package or function to generate bivariate exponential distribution? I gusee there should be three parameters, two rate parameters and one correlation parameter. I just did not find any function available on R. Any suggestion is appreciated. -- View this message in context:

Hi everybody, while performing ks.test for a standard exponential distribution on samples of dimension 2500, generated everytime as new, i had this strange behaviour: >data<-rexp(2500,0.4) >ks.test(data,"pexp",0.4) One-sample Kolmogorov-Smirnov test data: data D = 0.0147, p-value = 0.6549 alternative hypothesis: two.sided >data<-rexp(2500,0.4)

Thanks for pointing me out "aggregate", that works fine! There is one complication though: I have mixed types (numerical and character), So the matrix is of the form: A 1.0 200 ID1 A 3.0 800 ID1 A 2.0 200 ID1 B 0.5 20 ID2 B 0.9 50 ID2 C 5.0 70 ID1 One letter always has the same ID but one ID can be shared by many letters (like ID1) I just want to keep track of the ID, and get

Hi all, I am attempting to fit, and test the goodness of fit of, a mixed exponential distribution to my dataset which consists of 15minute rainfall intensity data. FYI, the dataset spanning approx.2 years and 7 rainfall stations consists of some three hundred thousand 15min data records, of which some 30 thousand are non-zero rainfall amounts. Could anyone please tell me how i could do

Good morning, Is there anyway to do Mixed Exponential Distribution in R? I am trying to load some lag-weighted empirical survival distribution into R and run a mixed exponential on that data. Thanks, Jacob Fazekas Jacob Fazekas Assistant Actuary Auto-Owners Insurance Company 517-703-2543 fazekas.jacob@aoins.com [[alternative HTML version deleted]]

Dear R-users, I have to plot a exponential distribution like the plot in the pdf attached. I've write this code but I don't know how to draw the two lines.. Can anyone help me please? Thank you very much Pippo http://r.789695.n4.nabble.com/file/n3394476/exponential_smoothing.pdf exponential_smoothing.pdf -- View this message in context:

I am learning glm function, but how do you fit data using exponential distribution with glm? In the help file, under "Family Objects for Models", no ready made option seems available for the distribution as well as for other distributions satisfying GLM requirements not listed there.

Hi, I need to analyze the influences of several factors on a variable that is a measure of fecundity, consisting of 73 observations ranging from 0 to 5. The variable is continuous and highly positive skewed, none of the typical transformations was able to normalize the data. Thus, I was thinking in analyzing these data using a generalized linear model where I can specify a distribution other than

Dear, R help How can I generate random numbers of the multivariate power exponential distribution. Regards, Jeremias Leão. ____________________________________________________________________________________ [[elided Yahoo spam]] [[alternative HTML version deleted]]

Dear all, I've tried to solve the Es. 12, cap 4 of "Introduction to GLM" by Annette Dobson. It's about the relationship between survival time of leukemia patients and blood cell count. I tried to fit a model with exponential distribution, first by glm (family gamma and then dispersion parameter fixed to 1) and then with survreg. They gave me the same point estimates but the

Hi, That sounds simple but I cannot think of a really fast way of getting the following: c(1,1,2,2,3,3,4,4) would give c(1,3,5,7) i.e., a function that returns the indexes of the first occurrences of numbers. Note that numbers may have any order e.g., c(3,4,1,2,1,1,2,3,5), can be very large, and the vectors are also very large (which prohibits any loop). The best I could think of is: tmp =

Hi, I've had this problem for a while and tackled it is a quite dirty way so I'm wondering is a better solution exists: If we have two vectors: v1 = c(0,1,2,3,4) v2 = c(5,3,2,1,0) How to remove one instance of the "3,1" / "1,3" double? At the moment I'm using the following solution, which is quite horrible: v1 = c(0,1,2,3,4) v2 = c(5,3,2,1,0) ft <-