similar to: Slack variable in OR

Dear R Users, Suppose we are interested for generating a new vector ( x ) from a current vector (y) of length 1000 so that x includes the sum of every 5 values in y respectively from the first to the end of length y. The same length of y for x is desired, so that other 4 positions (indices) in x are filled out with NA. For generating such a new vector, I have no idea. I tried in some

Dear R Users, For running wavelet functions using dwt( ), modwt( ), and mra( ), a wavelet filter algorithm is applied. For all these functions, default is "la8" and other possibility is "haar". In related documents, another possibilities like as symlet and coiflet ... are not cited. Besides "la8" and "haar", which wavelet filters can be used? Thank

Hi R Users, I don't know how much is difficult my problem and even it is possible to solve in R or not. Given a vector with 2000 observations. I want to creat a new vector from that vector so that new vector be the sum of every 5 observations sequently. That is , each new observation is sum of every 5 sequent observations, from the first observation of previous vector to the end. So

Dear R Friends , I want to name my variables( more than 2 variables in a single plot) within a plot to distinct them from each other, but I cann't. How it is possible? I don't mean x and y axis using xlab or ylab. At the below , it follows some lines, only as an example that you could try please, if it is possible. I really thanks for your attention. Amir library(graphics) y<-

Dear R Users, Suppose we want to creat a new vector ( x ) from a current vector (y) of length 1000. The current vector y includes negative, zero and positive values. We want our new vector x includes the negative values in y, otherwise NA with the same length as y. For this, we have x=y[y<0] . Now x includes a subset of y with shorter length than y. With x=match(y,x) we would

Dear R users, Sometimes it is needed to read only some columns from a table, in particulare for high frequency data. How it is possible to read just some certain columns using read.table ( ). The reason could be keeping space in R and in particular accelerating in reading data when the number of rows are huge and some of them are not needed. Thank you very much, Amir

Dear All , Since dissimilarity is one of quality measures in clustering , I'm trying to access to the sum of dissimilarity as a whole measure. But after running my data using CLARA I obtain : 1128 dissimilarities, summarized : Min. 1st Qu. Median Mean 3rd Qu. Max. 0.033155 0.934630 2.257000 2.941600 4.876600 8.943700 But I can not find the sum of dissimilarity.How can i

Hello folks, Any ideas how to do this? data.frame is a data frame with column names "x1",...,"xn" y is a response variable of length dim(data.frame)[1] I want to write a function function(y, data.frame){ lm(y~x1+...+xn) } This would be easy if n was always the same. If n is arbitrary how could I feed the x1+...+xn terms into lm(response~terms)? Thanks Richard -- Dr.

Dear all, I am running a panel regression with time and location fixed effects: ### reg1 <- lm(lny ~ factor(id) + factor(year) + x1+ I(x1)^2 + x2+ I(x2)^2 , data=mydata, na.action="na.omit") ### My goal is to use the estimation for prediction. However, I have 8,500 IDs, which is resulting in very slow computation. Ideally, I would like to do the following: ### reg2 <-

Hello, I am trying to "paste" together a formula to use in the mob function of party. This means the formula will be of the form y ~ x1+ ...+xM | z1+..zN. I am doing some preliminary fits of y ~ x1+ ...+xM, then want to add the conditional part of the equation using update(). Here's the test code: var1 <- 1:78 x1 <- paste("x", var1, sep="") f1 <-

Dear all, I am trying to find a solution satisfying the below equations in R. Set up the problem 9 X1+ X2 + X3 = 2 X1+ X2 + X3 = 1 which is subjected to 0 < X1 < X2 < X3 < 2. I have downloaded the packages \'linprog\' and \'lpSolve\' but can not see how to solve the question. Thank you for your help. With

I have no direct experience with such horrific models, but your formula is a mess and Google suggests the biglm package with ffdf. Specifically, you should convert your discrete variables to factors before you build the model, particularly since you want to use predict after the fact, for which you will need a new data set with the exact same levels in the factors. Also, your use of I() is

I'm currently implementing A rtp header info and using speex for audio. can someone tell me what the rtp payload type value is for speex. i've checked the submitted speex draft and it says its beyond its scope and i can't find anything in RFC3550 that might suggest what it is. any help would be appreciated thank you, Greg --------------------------------- Boardwalk for $500?

Hi Sir I am very new user of R for the research project on multilevel logistic regression. There is confusion about bugs() function in R and BUGS software. Is there any relation between these two? Is there any comprehensive package for Multilevel Logistic modelling in R? Please guide in this regard. Thank You RAZA --------------------------------- Boardwalk for

Hi List; If I set nat=yes, then asterisk will send the packets to the public IP address or to the private IP address (which will be for the endpoint that is behind the nating)? And by setting the nat=yes, then what exactly will be ignored at asterisk side when reading the registeration messages from the endpoint? Any help. Regards Bilal

Dear friends, I am testing glm as at page 514/515 of THE R BOOK by M.Crawley, that is on proportion data. I use glm(y~x1+,family=binomial) y is a proportion in (0,1), and x is a real number. I get the error: In eval(expr, envir, enclos) : non-integer #successes in a binomial glm! But that is exactly what was suggested in the book, where there is no mention of a similar warning. Where am I

Dear list, I would like to know how the function residuals.lm calculates the partial residuals from an lm object with more than one predictor variable. In other words what is residuals.lm(...,type="partial") doing behind the scenes? According to the help file for residuals.lm (?residuals.lm), "The partial residuals are a matrix with each column formed by omitting a term from

Hi, dear R users I am a newbie in R and I wantto use the method of meximum likelihood to fit a Weibull distribution to my survival data. I use "optim" as follows: optim(c(1, 0.25),weibull.like,mydata=mydata,method="L-BFGS-B",hessian = TRUE) My question is: how do I setup the constraints so that the two parametrs of Weibull to be pisotive? Or should I use other function

Hello, I have a self-defined function to be computed on each column in a matrix. The basic idea is to ignore the elements that have value of 0 during computation. I should be able to write my own function but it could be computational expensive, so I'd love to ask if anyone may have suggestions on how to implement it more efficiently. Thanks in advance. For example, there are three

He, I used the "coxph" function, with four covariates. Let's say something like that > model.1 <- coxph(Surv(Time,Event)~X1+X2+X3+X4,data=DATA) So I obtain the 4 coefficients B1,B2,B3,B4 such that h(t) = h0(t) exp(B1*X1+ B2*X2 + B3*X3 + B4*X4). When I use the function on the same data > predict.coxph(model.1,type="lp") how it works in making the prediction?