similar to: fivenum (PR#4586)

Damien, Here's a forwarded bug for you. Cheers, Phil. --[[message/rfc822]] Subject: Bug#52414: ssh-add uses ssh-askpass, but ssh doesn't Reply-To: David Huggins-Daines <dhd at plcom.on.ca>, 52414 at bugs.debian.org Resent-From: David Huggins-Daines <dhd at plcom.on.ca> Resent-To: debian-bugs-dist at lists.debian.org Resent-CC: Philip Hands <phil at hands.com>

Dear R-helpers, Happy New Year to all the helpful members of the list. Here is the behavior I'm looking for: > v1 <- c("a","b","c") > n1 <- c(0, 1, 2) > v2 <- c("c", "a", "b") > n2 <- c(0, 1 , 2) > (f1 <- data.frame(v1, n1)) v1 n1 1 a 0 2 b 1 3 c 2 > (f2 <- data.frame(v2, n2))

Deare R users I am new to R. What I want to do is explained below;- I have table called States.Prob which is given below Prob of States Changes State1 State2 State3 State4 A Pa1 Pa2 Pa3 Pa4 B Pb1 Pb2 Pb3 Pb4 C Pc1 Pc2 Pc3 Pc4 D Pd1 Pd2 Pd3 Pd4 and I have a dataframe called

Dear Users This is my dataset called mydata4. I want to sort the dataframe on the first column PxMid which is basically a column with dates. I've tried mydata4<-mydata4[order(mydata4$PxMid),] but it doesnt work. Could it be because these are dates? Please help I'm really stuck !! Thank you for your time. Regards Meenu PxMid EU0006MIndex.x DMSW1Curncy.x DMSW2Curncy.x DMSW3Curncy.x 1

Full_Name: David Leong Version: 2.7.2 OS: Windows XP Submission from: (NULL) (12.187.86.2) It appears for the fivenum function, there is a strait average between two ranked samples. This should be a linear interpolation between ranks. For the following data set #data

You can get anything you want, at Alice's Restaurant: > quantile(d,.75,type=1) 75% 913 > quantile(d,.75,type=2) 75% 981.5 > quantile(d,.75,type=3) 75% 913 > quantile(d,.75,type=4) 75% 913 > quantile(d,.75,type=5) 75% 981.5 > quantile(d,.75,type=6) 75% 1015.75 > quantile(d,.75,type=7) 75% 947.25 > quantile(d,.75,type=8) 75% 992.9167 >

I've just started using R and am still a neophyte, but I found the following curious result. I'm using the current version of R (2.5.1 (2007-06-27) ). Why are the results for the third quartile different in the output from the summary and fivenum commands? For the following data set 457 514 530 530 538 560 687 745 745 778 786 790 792

Hello everyone I'm trying to use Scheffe's complete cubic model (mixture design). In the bibliographies, they indicate that the term is of the type: A * B * (A-B). But I see that trying to adjust the three cubic terms results in singularities. I know this implies not having the inverse matrix: solve (t (X)% *% X) does not exist. The bibliographies show all three cubic terms. So my

I am struggling to generate p values for comparisons of levels (post-hoc tests) in a glm with a negative binomial distribution I am trying to compare cell counts on different days as grown on different media (e.g. types of cryogel) so I have 2 explanatory variables (Day and Cryogel), which are both factors, and an over-dispersed count variable (number of cells) as the response. I know that both

Full_Name: Jim Breaux Version: 1.9.0 OS: WinXP Submission from: (NULL) (209.78.110.135) According to the help for 'round' it is supposed to round to the even digit. However, see the following examples: In the following, R is rounding down: > round(0.3645, 3) [1] 0.364 > round(0.3655, 3) [1] 0.365 > round(0.3665, 3) [1] 0.366 > round(0.3675, 3) [1] 0.367 > round(0.3685,

Is there any R package that implements a bifurcating autoregression, aka the BAR(n) model? I've been reading the Huggins and Staudte paper, "Variance Components Models for Dependent Cell Populations", from the Journal of the American Statistical Association, 1994. Shawn Garbett <shawn.p.garbett at vanderbilt.edu> Vanderbilt Cancer Biology 220 Pierce Ave, PRB 715AA

We’re working on improving the online experience for community members and want to understand the various ways that you engage with the Puppet Labs community (Forge, Ask forum, Redmine, etc). If you want to make it easier for yourself and others to plug into Puppet Labs online community, talk with us! On Tuesday & Wednesday next week, we’re holding web meetings to talk individually with

Stock returns and other financial data have often found to be heavy-tailed. Even Cauchy distributions (without even a first absolute moment) have been entertained as models. Your qq function subtracts numbers on the scale of a normal (0,1) distribution from the input data. When the input data are scaled so that they are insignificant compared to 1, say, then you get essentially the

Hi, I've noticed that LLVM tends to generate suboptimal code and spill an excessive amount of registers in large functions, such as in those that are automatically generated by FFTW. LLVM generates good code for a function that computes an 8-point complex FFT, but from 16-point upwards, icc or gcc generates much better code. Here is an example of a sequence of instructions from a 32-point

I've been playing around with various table tools, trying to construct a fairly simple cross-tab. It shouldn't be hard, but for some reason it turning out to be (for me). If I want to see how many men and how many women agree with a agree/disagree question (coded 1,0), I can do this: >attach(mydata) >mytable <- table(male, q1.bin) # gender and a binary response variable

I'm puzzled by the behaviour of factors in rma models, see example and comments below. I'm sure there's a simple explanation but can't see it... Thanks for any input John Hodgson ------------------------------------- code/selected output ----------------- library(metafor) ## Set up data (from Lenters et al A Meta-analysis of Asbestos and Lung Cancer... ##

The method is here: https://github.com/rails/rails/blob/master/activerecord/lib/active_record/persistence.rb#L288. The method takes the in-memory attribute value and increments it by the specified amount. A safer approach (from an isolation standpoint) would be to let the database determine the value. Instead of telling the database what value to persist in the database, the SQL can written

Dear all, I am using poly() in lm() in the following form. 1> DelsDPWOS.lm3 <- lm(DelsPDWOS[,1] ~ poly(DelsPDWOS[,4],3)) 2> DelsDPWOS.I.lm3 <- lm(DelsPDWOS[,1] ~ poly(I(DelsPDWOS[,4]),3)) 3> DelsDPWOS.2.lm3 <- lm(DelsPDWOS[,1]~DelsPDWOS[,4]+I(DelsPDWOS[,4]^2)+I(DelsPDWOS[,4]^3)) 1 and 2 lead to identical but wrong results. 3 is correct. Surprisingly (to me) the residuals

Hi Experts, I am new to R, and was wondering how to do 3D linear regression in R. In other words, I need to Fit a 3-Dimensional Line to Data Points (input). I googled before posting this, and found that it is possible in Matlab and other commercial packages. For example, see the Matlab link:

Hello List: Please find uploaded to CRAN a new package, PwrGSD The package is intended for the design and analysis of group sequential trials There are two main functions, (1) GrpSeqBnds: computes group sequential stopping boundaries for interim analysis of a sequential trial based upon a normally distributed test statistic. This can be done via the Lan-Demets procedure with