similar to: Double Exponential

Does R have a bootstrap command like SPlus, or do we have to form our own code if we want to do bootstrapping in R? Jason Parcon Western Michigan University e-mail addresses: s0parcon at wmich.edu parcon at stat.wmich.edu -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

If so, I''ve had trouble finding it. Can anyone help?

Dear List, I'd like to run a discriminate analysis on a data set, but have no idea how to go about this in R. I have attempted to locate info in the manuals, but may not be consulting the right sections or documents. Can anyone point me to appropriate documentation if such exists. Many thanks, David S. David White sdavidwhite at bigfoot.com Columbus, Ohio

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

Here's a nasty one. The following has caused a segmentation fault and possibly also a bus error. fred <- 1:6 dim(fred) <- 6 dimnames(fred) <- list(LETTERS[1:6]) mm <- matrix(1:12, 2, 6) mm %*% fred # segmentation fault here In the case without the dimnames assignment the result is OK. Cheers, Jonathan. --please do not edit the information below-- Version: platform =

Is there any R function that computes the convolution of the double exponential distribution? If not, is there a good way to integrate ((q+x)^n)*exp(-2x) over x from 0 to Inf for any value of q and for any positive integer n? I need to perform the integration within a function with q and n as arguments. The function integrate() is giving me this message: "evaluation of function gave a

Dear RHelpers, Does anyone know of any R code to perform bivariate Poisson regression (including random effects)? Best wishes Simon Simon D.W. Frost, M.A., D.Phil. Department of Pathology University of California, San Diego Antiviral Research Center (Formerly: UCSD Treatment Center) 150 W. Washington St., Suite 100 San Diego, CA 92103 USA Tel: +1 619 543 8080 x275 Fax: +1 619 298 0177 Email:

Dear R Users, I am trying to obtain a best-fit analytic distribution for a dataset with 11535459 entries. The data range in value from 1 to 300000000. I use: fitdistr(data, "gamma") to obtain mle's for the parameters. I get the following error: Error in optim(start, mylogfn, x = x, hessian = TRUE, ...) : non-finite finite-difference value [1] And the following warnings:

Dear R-help, Can some one tell me how to do the following (if it's possible)? Suppose I have a function like this: f <- function(x, y, ...) { ## some code g(x, y, ...) ## some more code } The problem is that g() may not understand everything that comes through in "...". Is there a way to delete some component of "..." and then pass it to g()? Here's

Hi ! I've used the example given in the documentation for the prcomp function both in R and SPAD to compare the results obtained. Surprisingly, I do not obtain the same results for the coordinates of the principal composantes with these two softwares. using USArrests data I obtain with R : > summary(prcomp(USArrests)) Importance of components: PC1 PC2

I have two CSV files (exported from Excel), say file1 and file2. The have the same number of rows, and each has several columns, with names on the first line; and some of the columns in file1 are repeated in file2. Using the "foreign" package, I can read these in separately to dataframes say d1 and d2 with > d1<-read.csv("file1") >

Please, I would like to know how to generate a truncated multivariate normal distribution k - dimensional, X ~ NT(mu, Sigma), where the elements of X to be non-negative (except the first), and the first dimension is strictly larger than zero. Example: X ~ NT_2(mu, Sigma), where mu=c(0.5, 0.5) and Sigma=c([120, 191], [191,154]), with X_1>0 and X_2>=0 Could anybody help

Dear all, I found that the new feature of the comment.char="#" argument in the read.table function truncated my data while the data set actually contains '#'. We analyze lot of data that contain '#'. This is really annoying and it is also not compatible earlier version of R. I searched the R archive and found the following message for the scan function. Is it

Dear R-experts I would like to read all files from a directory, the files have names "myname0001.txt" etc. I paste the directory plus file names and use "read.delim()". My problem is that some file names are missing, so I get an error and my program stops. Is there a way to check for a null pointer analogous to C, so that I can simply skip non-existent filenames? Please do

How do I add error bars to an interaction plot of means? Thanks

Hey, all I am going to plot several 3-dimensional ellipsoids in R. For example, A is such a ellipsoid with mean u=[1 1 1]', covariance matrix C=[1 0 .5;0 1 .6;.5 .6 1]; So how can I plot such object in 3D space? Is there some function to achieve this? Thanks. Fred

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:

I'm probably missing something very basic here, but: I've written some C code that I load into R dynamically. In the course of this C code, I generate some multinomial random deviates. I initially used the publically available "randlib" library, which also implements its own random number generator and binomial deviates (which are used to generate the multinomial deviates).

Dear all, I have the following output generated by linear regression. Since there is only one regression intercept and one slope for one set of data, what is the meaning of std. error for intercept and that of slope? Thanks in advance. Sincerely, Minghua > data(thuesen) > attach(thuesen) > lm(short.velocity~blood.glucose) Call: lm(formula = short.velocity ~ blood.glucose)

I'm trying to get a grasp of maximum-likelihood estimation and would like to find a package that performs mle (hopefully a simple example). It seems as if there are plenty of packages that make use of different types of likelihood estimators, but none are of a simple, "newbie" type. Does anyone have a suggestion for which package would be the best for a mle example? Thanks, Jeff.