similar to: Converting factors to bounded random numerical data

I'm trying to figure out how to repeat a series of commands in R and have the outputs added to a dataframe after each iteration. My code starts this way... a<-read.csv("File1.csv") b<-read.csv("File2.csv")

Hello you all from the R Help mailing list! I am working on a PowerBook with Mac Os X and use R 2.5.0. I used the distance function from the analogue package to perform a similarity analysis using the Gowers Index and weighted Variables. My variables are bivariate data and measurements as well as interval data transformed into minimum and maximum variables. I used this Code:

I have a vector of size 217 called "A". the values of A are not sorted and range from 0 to 1 (normalized) I am having difficulty writing a program to create a new vector "B" where if A's value is 0< A <=0.333 then B is 0 if A's value is 0.333< A <=0.666 then B is 1 if A's value is 0.666< A <=1 then B is 2 so if A is 0.22 0.999 0.444 0 B would

Perhaps some of you might be interested in this: -- Carsten ################################################################## *---------------------------------------------------* * LLBMC: The Low-Level Bounded Model Checker * * for C (and C++) programs is now available! * * Version 2012.1 * *

On Tue, Feb 7, 2012 at 4:58 AM, Carsten Sinz <carsten.sinz at kit.edu> wrote: > Perhaps some of you might be interested in this: > -- Carsten > > > ################################################################## > > *---------------------------------------------------* > * LLBMC: The Low-Level Bounded Model Checker * > * for C (and C++)

This looks very interesting. Do you plan to make the source code publicly available? -Hal On Tue, 2012-02-07 at 01:58 +0100, Carsten Sinz wrote: > Perhaps some of you might be interested in this: > -- Carsten > > > > > ################################################################## > > > *---------------------------------------------------* > *

Dear R experts, density estimation on a 2 dimensional bounded domain --------------------------------------------------------------------- I am currently trying to estimate the probability density (PD) of cancers within the breast using the sm library with the routine sm.density Of course a practical PD must be limited by the curve of the breast outline. I don't have a clue after perusing

I'm using Kernel Utilization distributions to estimate overlap in animal territories. There are various ways to calculate this overlap and one of the equations is : V.I. = ??? min (f1 (x,y,z), f2 (x,y,z)) dxdy where f1 = utilization distribution for bird 1 in 3 spatial dimensions (x,y,z). I can produce these already, but no idea how to do the rest. Thoughts? -- View this message in

The desired result is that user A's phone rings; when he picks it up, user B is dialled, and user A's channel is connected to that. (This is to be a back-end for a web-based address book.)

R-3.0.1 rev 62743, binary downloaded from CRAN just now; macosx 10.8.3 Hello, eigen(symmetric=TRUE) behaves strangely when given complex matrices. The following two lines define 'A', a 100x100 (real) symmetric matrix which theoretical considerations [Bochner's theorem] show to be positive definite: jj <- matrix(0,100,100) A <- exp(-0.1*(row(jj)-col(jj))^2) A's being

Hi Petr, The following is different line of thought which is posted in different form, maybe you have some wise input on it. "I need to find Efficient(tracktable) deterministic algorithm for Matching Weighted Graphs with bounded degree. Now we all know Graph matching is non-tractable but when degree of vertex has upper bound are there any tractable algorithm? Does this special case comes

Hi all, I want to substract vector B from A's each column... how can R do that smartly without a loop? > A=matrix(c(2:7), 2, 3) > A [,1] [,2] [,3] [1,] 2 4 6 [2,] 3 5 7 > B=matrix(c(1, 2), 2, 1) > B [,1] [1,] 1 [2,] 2 > A-B Error in A - B : non-conformable arrays [[alternative HTML version deleted]]

Hello all, I have a matrix whose column names look like a1 a2 b1 b2 b3 c1 c2 1 2 3 7 1 3 2 4 6 7 8 1 4 3 Now, I can have any number of a's. not just two as shown above and same goes for b's and c's. I need to extract all the a's columns and put them in another matrix, extract all b's columns and put them in some matrix

The following AMI command works well for all of the information I want: action: Getvar actionid: act1 channel: PJSIP/Twilio-NA-W-3-In-00000028 Variable: CHANNEL(pjsip,XXXX) Where XXXX can be one of the many available items. However, when I create a call from A to B, all of the items return properly except: local_addr and remote_addr. More specifically, they return correctly for the A leg (that

As mentioned in the title, I want to do some control-flow and dataflow analysis. I can only find the assignment statement in a function, but the assigned variable is just a parameter of this function. For example, **************************************** int foo(struct A* a, int b, ...){ ... a->Int_field = b; ... return 0; } ***************************************** in the code

On Tue, Jul 4, 2023 at 7:52 PM TTT <lists at telium.io> wrote: > Building on my last message, I am trying to get CHANNEL data using getvar > (through the AMI). And although I'm getting responses, some values > returned seem illogical. For example, phone 111 calls phone 222 via the > PBX. Here's the data I get back > > > > > > Channel A:

Can anyone recommend a good nonparametric density approach for data bounded (say between 0 and 1)? For example, using the basic Gaussian density approach doesn't generate a very realistic shape (nor should it): > set.seed(1) > dat <- rbeta(100, 1, 2) > plot(density(dat)) (note the area outside of 0/1) The data I have may be bimodal or have other odd properties (e.g. point mass

Dear R's! I am strugling with cancor procedure in R. I cannot figure out the meaning of xcoef and of yxcoef. Are these: 1. standardized coefficients 2. structural coefficients 3. something else? I have tried to simulate canonical correlation analysis by checking the eigenstructure of the expression: Sigma_xx %*% Sigma_xy %*% Sigma_yy %*% t(Sigma_xy). The resulting eigenvalues were the same

Channel A: "1688509741.112" , name: "PJSIP/111-00000064" , is originator: Y , call-Id: "u.l6kcou25cax60 at mydomain.com <mailto:u.l6kcou25cax60 at mydomain.com> " , local_uri: "<sip:222 at mydomain.com <mailto:sip%3A222 at mydomain.com> ;user=phone>" , local_tag: "1734d973-c4da-4ae8-a37d-5f7065f1fe54" , local_addr:

Building on my last message, I am trying to get CHANNEL data using getvar (through the AMI). And although I'm getting responses, some values returned seem illogical. For example, phone 111 calls phone 222 via the PBX. Here's the data I get back Channel A: "1688509741.112" , name: "PJSIP/111-00000064" , is originator: Y , call-Id: "u.l6kcou25cax60 at