similar to: apply( )

I apologize if this has been addressed before; recently I read an article in Forbes which discussed how SCO was going after companies that have been using Linux. The article made the point that the ideas behind GPL are under attack precisely because no one is making sure that the code being put into the freely avail. packages isn't owned by someone else. Here's my question: Is R

Dear Community, i have read in a table with two column from a *txt-File. What i get is a data-frame wit two column. After that i allocate this 2 column to x and y. Plotting this, i obtain a picture that looks like a bar plot (something like a grid). Is that because x and y is in factor-formatted? A conversion with "asnumeric" doesnt work. I get a vector with "NA". Here is my

Trying to start one of my vms, a centos one at that, but am getting the following message: [root@vmhost ~]# virsh start voip --paused error: Failed to start domain voip error: cannot open file '/var/tmp/FreePBX-5.211.65-3-x86_64-Full-1388073872.iso': No such file or directory [root@vmhost ~]# But, virsh dumpxml voip shows no info onto the .iso <disk type='file'

Hello, i have a question calculating the shaped area between the two curves (see image). http://r.789695.n4.nabble.com/file/n4597813/test.png I try to use a Simpson-Integral but it doesn't work. "R" doesn't know the command! It is possible, that i need a library? Curve 1: y Curve 2: z q<-abs(y-z); est<-sintegral(x,z) Greetings -- View this message in context:

Hello, does anybody know, how to reverse the elements of the x-axis. At now my x-axis for example goes from 0, 0.5, 1, 1.5, ... to 99.5, 100.0 Is there a way to change the x-axis, like this: 100.0, 99.5, 99.0, ..., 0.5, 0. The 100.0 should start at left x-axis and the 0 shold be on the right. Greeting -- View this message in context:

I apologize if this question is not completely appropriate for this list. I have been using SAS for a while and am now in the process of learning some C and R as a part of my graduate studies. All of the statistical packages I have used generally yield p-values as a default output to standard procedures. This week I have been reading "Testing Precise Hypotheses" by J.O. Berger

I have been learning about some outlier tests -- Dixon and Grubb, specifically -- for small data sets. When I try help.start() and search for outlier tests, the only response I manage to find is the Bonferroni test avaiable from the CAR package... are there any other packages the offer outlier tests? Are the Dixon and Grubb tests "good" for small samples or are others more

I am a relative newcomer to both the R and C/C++ software worlds -- I'm taking a C Programming class currently. I noticed the other day that the C:\Program Files\R1_8_1\src\include\R_ext directory on my WinXP box has the header files BLAS.h Lapack.h Linpack.h RLapack.h I am interested in (perhaps) using one or more of these header files in a straight C program I'm working on in Visual

R 2.2, WinXP. I am having problems getting the right kind of xyplot( ) to be generated. The first of these works fine, but doesn't overlay a reference grid (which I need): xyplot(Y ~ X | Factor1, type = 'b', groups = GROUP, col = c(1,13), pch = c(16,6), lty = 1, lwd = 2, cex = 1.2, data = FOO.Frame, between = list(x = .5, y = .5), scales = list(alternating = TRUE)) The second

R2.2, WinXP: I am using xyplot( ) to generate time plots of plasma concentration data. The following is an edited version of my code: xyplot(log.conc ~ time| group, data = foo, groups = subject, panel = function(x, y, panel.number, ...) { panel.superpose(x, y, subscripts = TRUE, groups = foo$group, type = 'l', col

R2.3, WinXP Dear all, I am using the following functions: f1 = Phi1+(Phi2-Phi1)/(1+exp((log(Phi3)-log(x))/exp(log(Phi4))) f2 = Phi1+(Phi2-Phi1)/(1+exp((log(Phi3)-log(r)-log(x))/exp(log(Phi4))) subject to the residual weighting Var(e[i]) = sigma^2 * abs( E(y) )^(2*Delta) Here is my question, in steps: 1. Function f1 is separately fitted to two different datasets corresponding to

All, I have been reading Dr. Harrell's excellent "Regression Modeling Strategies" book and trying out the exercises. I understand that contrast( ) is used to obtain contrasts between two variables for given levels of other nuisance variables; is there a way to use contrast( ) to obtain, for example, Scheffe confidence intervals / hypothesis tests for many post hoc contrasts at

Hello, I am using WinXP, R version 2.3.1, and SAS for PC version 8.1. I have mostly used SAS over the last 4 years and would like to compare the output of PROC DISCRIM to that of lda( ) with respect to a very specific aspect. My data have k=3 populations and there are 3 variates in the feature space. When using using the code PROC DISCRIM DATA = FOO OUT = FOO_OUT OUTSTAT = FOOSTAT

NOTE: WinXP, R2.2.0 All, a while back I posted a question about using relative filereferencing. The responses have allowed me to successfully set up the following directory structure: ...\data\raw ...\data\derived ...\prog ...\lst ...\log In the \prog directory I have put an RGui.exe shortcut and "pointed it" at \prog as the "Start In" location. In the same

Hi I have dataset that consists of two columns AB 0.102 AC -0.002 BA -0.102 BC 0.270 CA 0.002 CB -0.270 I wish to create a matrix so that I can eventually plot the data. A B C A 1 0.102 -0.002 B -0.102 1 0.27 C 0.002 -0.27 1 Any help or guidance would be greatly

R2.2 for WinXP, Splus 6.2.1 for HP 9000 Series, HP-UX 11.0. I am trying to get a handle on why the same lme( ) code gives such different answers. My output makes me wonder if the fact that the UNIX box is 64 bits is the reason. The estimated random effects are identical, but the fixed effects are very different. Here is my R code and output, with some columns and rows deleted for space

All, I have been able to successfully use the optim( ) function with "L-BFGS-B" to find reasonable parameters for a one-compartment open pharmacokinetic model. My loss function in this case was squared error, and I made no assumptions about the distribution of the plasma values. The model appeared to fit pretty well. Out of curiosity, I decided to try to use nlminb( ) applied to a

Hello list, I am dealing with a noisy function (gradient,hessian not available) with simple boundary constraints (x_i>0). I've tried constrOptim() using nelder mead to minimize it but it is way too slow and the returned results are not satisfying. simulated annealing is so hard to tune and it always crashes R program in my case. I wonder if there are any packages or functions can do

http://www.ncbi.nlm.nih.gov/pubmed/21418051 for the full reference. I don't have an electronic copy, but I do have that issue of Biometrics in my office. I'll have a copy sent over. Terry On 07/10/2012 04:08 PM, r-help-request at r-project.org wrote: > Send R-help mailing list submissions to > r-help at r-project.org > > To subscribe or unsubscribe via the World Wide

Dear R-users, This should be simple but still eludes me: Given the following tmp<-as.data.frame(matrix(c(44, 10, "abc", 1, 44, 10, "def", 1, 44, 12, "abc", 2), 3, 4, byrow=T)) I want to expand the data to the following form: V1 V2 V3 V4 1 44 10 abc 1 2 44 10 def 1 3 44 12 abc 1 4 44 12 abc 1 The last row of the original df was duplicated the row by the