similar to: typo in R online manual (PR#7200)

Just a few documentation "bugs" that I've noticed recently. 1. In the help for (dpqr)weibull(), the formula given for the variance of a Weibull is wrong. The correct formula is b^2 * sqrt(gamma(1 + 2/a) - (gamma(1 + 1/a))^2)) Note that I've also changed Gamma to gamma, which I think is preferable since this is actually the name of the gamma() function

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:

Hi, Im trying to find the bridge source code in my kernel implementation. Im running a SUSE Linux kernek 2.4.21-99-default, and I couldnt file the codes I wanted. Maybe I havent installed it correctly, but I dont know then what should I install to have the source codes in my computer. Any help would be appreciated. Thanks -- Francisco Trindade fmtrindade@inf.ufrgs.br

Is there some reasonable way to generate random data from a distribution that has some degree of skewness and/or kurtosis, but would otherwise be normal? thanks, -------------- next part -------------- A non-text attachment was scrubbed... Name: greiff.vcf Type: text/x-vcard Size: 398 bytes Desc: Card for Warren R. Greiff Url :

In the optim() documentation, the control parameter "maxit" says "There is no other stopping criteria." That should be "are", or "criterion" ... While I'm at it -- poking at the code a little bit, it looks as if the initial simplex is set from the initial point by displacing each parameter value by max(0.1,0.1*pmax(fabs(Bvec))), where Bvec is the

Hi , Im new in this list, and Im trying to find a Spanning Tree source code, the kind that runs in bridges and switches. Does anybody know wher I can find it? Any help would be appreciated. Thanks, -- Francisco Trindade fmtrindade@inf.ufrgs.br

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).

Hi, I was wondering if someone can help me interpret the results of running weibreg. I run the following and get the following R output. > weibreg(Surv(time, censor)~covar) fit$fail = 0 Call: weibreg(formula = Surv(time, censor)~covar) Covariate Mean Coef Rel.Risk L-R p Wald p covar 319.880 -0.002 0.998 0.000 log(scale) 0.000 8.239

Hi guys, I have a data-set that fits well into a Weibull model y = a-b*exp(-c*x^d). I want to estimate the parameters of the coefficients a, b, c and d, given x and y. Can you guys help me? Just as an example, I fit the data y <- c(1,2,3,4,10,20) and x <- c(1,7,14,25,29,30) According to this model above, and using the software CurveExpert, and I got the estimates of a (2.95),b (2.90),c

================================================= This is an automated summary of the status of the R-bugs repository. Note that this may be neither complete nor perfectly correct at any given instance: Not all bugs are reported, and some reported bugs may have been fixed, but the repository not yet updated. Some bug fixes are difficult to verify because they pertain to specific hardware or

I have found the "simulate" method (incorporated in some packages) very handy. As far as I can tell the only class for which simulate is actually implemented in base R is lm ... this is actually a little dangerous for a naive user who might be tempted to try simulate(X) where X is a glm fit instead, because it defaults to simulate.lm (since glm inherits from the lm class), and the

Dear all, I wanna to do multiple comparison test for proportions (multiple chi squre ?), could someone tell me how in R, thank you!

================================================= This is an automated summary of the status of the R-bugs repository. Note that this may be neither complete nor perfectly correct at any given instance: Not all bugs are reported, and some reported bugs may have been fixed, but the repository not yet updated. Some bug fixes are difficult to verify because they pertain to specific hardware or

================================================= This is an automated summary of the status of the R-bugs repository. Note that this may be neither complete nor perfectly correct at any given instance: Not all bugs are reported, and some reported bugs may have been fixed, but the repository not yet updated. Some bug fixes are difficult to verify because they pertain to specific hardware or

Dear R help list, I am modeling some survival data with coxph and survreg (dist='weibull') using package survival. I have 2 problems: 1) I do not understand how to interpret the regression coefficients in the survreg output and it is not clear, for me, from ?survreg.objects how to. Here is an example of the codes that points out my problem: - data is stc1 - the factor is dichotomous

1. The weibull is the only distribution that can be written in both a proportional hazazrds for and an accelerated failure time form. Survreg uses the latter. In an ACF model, we model the time to failure. Positive coefficients are good (longer time to death). In a PH model, we model the death rate. Positive coefficients are bad (higher death rate). You are not the first to be confused

Dear r-help subscribers, A couple of weeks ago I sent the following message to the r-help mail list. It hasn't generated any response, and I could really use some help on this. Anyone able to help? Thanks again, Roger Dungan >> I am working on some survival analysis of some interval censored failure time data in R. I have done similar analysis before using PROC LIFEREG in SAS. In

Does anyone have an address for a maintainer, or know what's going on? cheers Ben Bolker ben at bolker-lap2:~$ ping wiki.r-project.org PING econum.umh.ac.be (193.190.194.5) 56(84) bytes of data. ^C --- econum.umh.ac.be ping statistics --- 8 packets transmitted, 0 received, 100% packet loss, time 6999ms ben at bolker-lap2:~$ traceroute wiki.r-project.org traceroute to

Dear all, I have a question regarding setIs and method dispatch in S4 classes: Consider the following example: ##################################################### ## A02 "contains" A01 by setIs: setClass("A01", representation(a="numeric",b="numeric")) setClass("A02", representation(a="numeric",d="numeric"))

I am trying to generate predictions from a weibull survival curve but it seems that the predictions assume that the shape(scale for survfit) parameter is one(Exponential but with a strange rate estimate?). Here is an examle of the problem, the smaller the shape is the worse the discrepancy. ### Set Parameters scale<-10 shape<-.85 ### Find Mean scale*gamma(1 + 1/shape) ### Simulate Data