similar to: vectorize a power analysis?

I want to sample within groups, and when a group has only one associated number to just return that number. If I use this code: groups <- c(1, 2, 2, 2, 3) numbers <- 1:5 tapply(numbers, groups, FUN = sample) I get the following output: > groups <- c(1, 2, 2, 2, 3) > numbers <- 1:5 > tapply(numbers, groups, FUN = sample) $`1` [1] 1 $`2` [1] 3 2 4 $`3` [1] 2 3 5 1 4

Hello, The function barplot automatically creates a y-axis that doesn't necessarily cover the range of y-values to be plotted. I know how to manually create my own y-axis so that it does cover the range, but I was wondering if there is some parameter to change so that the scale of the y-axis is automatically taller than the tallest bar. I thought setting xpd=F would do it, since it says that

I have a data set of many rows and many columns in which both the rows and the columns have associated grouping factors. Is there a way to do what 'aggregate' does but in the other dimension? The way I have been doing this is to use 'aggregate' on the data in the usual way and then rotate the result and apply 'aggregate' again. This works but is a little messy and I was

I have a Cisco 7940 when you call in from outside and dial the Cisco phone extension I get this Read_channel ## vpb/1-3: Setting record mode, bridge = 0 WARNING[18451]: File chan_sip.c, Line 1111 (sip_write): Asked to transmit frame type 8, while native formats is 4 (read/write = 4/4) == Spawn extension (default, 1004, 1) exited non-zero on 'vpb/1-3' -- hangup on vpb (vpb/1-3)

Hello folks, I am a beginner R user. I have been able to make a 3D surface plot using 'persp'. The surface is made by a grid of lines emanating perpendicularly from each of the x and y axes at regular intervals. I can get rid of that grid by setting 'border=NA'. Can anyone suggest some ways to replace the grid with contour lines, to create a 3-dimensional contour map? Thanks

I am trying to define a general R function that has a function as the output that depends on the user's input arguments (this may make more sense by looking at the toy example below). My real use for this type of code is to allow a user to choose from many parameterizations of the same general model. My "issue" is that when I compile a package with this type of code in it I get a

> This is not sad. > Science marches on... The project was never about science, but about freedom. jack.

Dear R-Help list, I have a nonlinear least squares problem, which involves a changepoint; at the beginning, the outcome y is constant, and after a delay, t0, y follows a biexponential decay. I log-transform the data, to stabilize the error variance. At time t < t0, my model is log(y_i)=log(exp(a0)+exp(b0)) at time t >= t0, the model is log(y_i)=log(exp(a0-a1*(t_i - t0))+exp(b0=b1*(t_i -

Happy New Year ! XLSolutions February 2010 R courses schedule is now available online at 9 USA cities for with 13 new courses: *** Suggest a future course date/city (1) R-PLUS: A Point-and-Click Approach to R (2) S-PLUS / R : Programming Essentials. (3) R/S+ Fundamentals and Programming Techniques (4) R/S-PLUS Functions by Example. (5) S/R-PLUS Programming 3: Advanced Techniques and

Hi guys, I have a weird failing spec, for which I just cannot figure out the reason of failure. I''m now rewriting my controller specs based on the advice of David and Ashley, and I got stuck on this (see: [rspec- users] specing rescue, ensure and else blocks of an Exception). http://pastie.caboo.se/111221 I''ve tried everything, like stubbing out :update_attributes! , even

I'm playing around with a machine that has 2 GB of memory. Indexing about 5GB of data average of 2MB per document. The documents are plain text. I notice the omindex's memory fott print get's biger an bigger then the machine starts to swap and it all slows down to a crawl. In regards to export XAPIAN_FLUSH_THRESHOLD I know the default is 10000 Am I right in saying that for my setup

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 ~ turns out I don't need to look at the C code. ~ if one uses the mu/size parameterization of the negative binomial, R computes size/(size+mu) to switch parameterizations. If size>>mu this gets rounded to 1 ... should be easy enough to test and return NA under these circumstances? function (x, size, prob, mu, log = FALSE) { ~ if

Hi, I am wondering how I should interpreate the output of optFederov() in AlgDesign. Specially I want to know what is $D, $A, $Ge and $Dea, which one I can use as an efficiency to say how good the optimal design is. I only know when a orthogonal design comes, $D = 1. I red the pdf document -- vignette("AlgDesign") [Just type: vignette("AlgDesign") in R, you will get

System info: Mandrake 9.0 R Version 1.6.1 ESS 5.1.21 Emacs 21.2.1 ------------------- Colleagues I've been calling R-code embedded in my LaTex document using Sweave, but would like to make things more convenient. At present as I understand it you first process the R chunks of code using the Sweave function called from within R to process a "precursor file" e.g. foo.sw to get a

System info: Mandrake 9.0 R Version 1.6.1 ESS 5.1.21 Emacs 21.2.1 ------------------- Colleagues I've been calling R-code embedded in my LaTex document using Sweave, but would like to make things more convenient. At present as I understand it you first process the R chunks of code using the Sweave function called from within R to process a "precursor file" e.g. foo.sw to get a

Hi, Before starting my proposal, I wanted to know what is the expected output of Letor module. Is it for transfer learning (i.e you learn from one dataset and leverage it to predict the rankings of other dataset) or is it for supervised learning? For instance - Xapian currently powers the Gmane search which is by default based on BM25 weighting scheme and now suppose we want to use LETOR to rank

Hi Dear all, I have some gene expression data samples from different tissue types ----------------------------------------------- - 120 samples from blood (B) - 20 samples from Liver (L) - 15 samples from Kidney (K) - 6 samples from heart (H) ----------------------------------------------- All the samples are from different individuals, so there are in total 161 individuals from which the DNA was

Dear R-users I have some datasets, all left-censoring, and I would like to fit distributions to (weibull,exponential, etc..). I read one solution using the function survreg in the survival package. i.e survreg(Surv(...)~1, dist="weibull") but it returns only the scale parameter. Does anyone know how to successfully fit the exponential, weibull etc... distributions to left-censoring

Dear R Community, I am using the package DRC ( to fit a boltzman model to my data. I can fit the model and extract the lower limit, upper limit, and ED50 (aka V50), but I cannot figure out how to get the slope of the curve at ED50. Is there a simple way to do this? I've searched the mailing list and looked through the package documentation, but could not find anything. I am new to r, and

Hello, I like to fit data against a negative binominal distribution x2<-c(rep(10,14),rep(9,8),rep(8,13),rep(7,11),rep(6,6),rep(5,18),rep(4,7),re p(3,21),rep(2,33),rep(1,55),rep(0,225)) f2<-fitdist(x2,"nbinom",method="mle") plot(f2) summary(f2) gofstat(f2) I receive the following result: Fitting of the distribution ' nbinom ' by maximum