similar to: opimization problem

hello, i have run around 65000 regressions and stored them in a list. then i stored the session with save.image on my hard disk. the file is almost 1GB. when i now want to load the image it took tons of time. even after 12h of loading it was not done, although the saving was done fairly fast. i fear i have to run the regressions again and store them in a database ... can i load this file?

hello, i need the data from hist() but i do not want the plot. e.g. z=hist(data)$counts #returns absolute frequency but when i execute this command the plot occurs also. is it possible to suppress the plot? many thanks, best regards gg -- --------------------------------------------------- Gottfried Gruber mailto:gottfried.gruber at terminal.at www: http://gogo.sehrsupa.net

hi, i calculate the log-returns in return1 and i want to get the performance for the security. with only one security i have the following code # create matrix to keep performance return100=matrix(rep(100,length(return1)+1)) # matrix for the sum z1=matrix(rep(0,length(return1)+1)) # suming up the returns from current index to start for (i in 1:length(return1)) {z1[i+1]=sum(return1[c(1:i)]) }

Hi everybody, I'm using Windows XP Prof, R 2.5.1 and a Pentium 4 Processor. Now, I want to solve a quadratic optimization program (Portfolio Selection) with the quadprog package I want to minimize (\omega'%*%\Sigma%*%\omega) Subject to (1) \iota' %*% \omega = 1 (full investment) (2) R'%*%\omega = \mu (predefined expectation value) (3) \omega \ge 0 (no short sales). Where

Dear all, I searched the mail archives and the R site and found no guidance (tried "merge", "cbind" and terms like "coalesce" with no success). There surely is a way to coalesce (like in SQL) columns in a dataframe, right? For example, I would like to go from a dataframe with two columns to one with only one as follows: From Name.x Name.y nx1 ny1 nx2 NA

Hello. I am new R user, so this question is probably quite stupid, but for the life of me I cannot figure out how to get predications using multivariate linear regression analysis. Single variable predictions work fine. I am trying the following: -- Known y's for known x's1 and x's2 ys <- c(133890, 135000, 135790, 137300, 138130, 139100, 139900, 141120, 141890, 143230, 144000,

Hi all, I have a nx1 logical array of zeros and ones and I want to calculate the individual lengths of all 1-consecutive sequences contained in it. Is there an easy quick way to do this in R? So, if I have a vector such as 111001101000011111110 I would like to get (1) 3, (2) 2, (3) 1, (4) 7 Any help would be appreciated! thanks! Jorge [[alternative HTML version deleted]]

[[this bounced first, because it has 'help' in the Subject line ... -- Martin Maechler ]] I am a complete novice R programmer. (Though I know C quite well) I am trying to write some R code to do the following simulation. There is a 2-frame "movie" of noise and signal dots. the noise dots have random positions in each frame. The signal dots are placed randomly in frame 1,

Hello, Consider this function for generalized ridge regression: gre <- function (X,y,D){ n <- dim(X)[1] p <- dim(X)[2] intercept <- rep(1, n) X <- cbind(intercept, X) X2D <- crossprod(X,X)+ D Xy <- crossprod(X,y) bth <- qr.solve(X2D, Xy) } # suppose X is an (nxp) design matrix and y is an (nx1) response vector p <- dim(x)[2] D<- diag(rep(1.5,p)) bt

Hello, Is there a chance that either ices0 will get support for live-streaming from line-in or ices2 for streaming mp3? I tried darkice, but this didn't work out with a decent sampling rate on my box (see [1])... Also tried liveice, but this does not seem to work at all with icecast2 (when changing the port to 8001 I got this simple UI but icecast2 didn't notice any new mount). I looked

On Tue, 27 Feb 2024 13:51:25 -0800 Jeff Newmiller via R-help <r-help at r-project.org> wrote: > The fundamental data type in Matlab is a matrix... they don't have > vectors, they have Nx1 matrices and 1xM matrices. Also known as column vectors and row vectors. :) > Vectors don't have any concept of "row" vs. "column". They do in (numerical) linear

Greetings, I'm working on some analyses where I need to calculate wilcox tests for paired samples. In my current literature search I've found a few papers on sample size determination for the wilcox test notably: Sample Size Determination for Some Common Nonparametric Tests Gottfried E. Noether Journal of the American Statistical Association

I'm trying to make multiple line plots, each with a different color, using the xyplot command. Specifically, I have an NxK matrix Y and an Nx1 matrix x. I would like the plot to contain a line for each (x, Y[,i]), i=1:K. I know something like xyplot(Y[,1] + Y[,2] + Y[,3] ~ x, type='l') will work, but if Y is large, this notation can get very awkward. Is there a way to do something

i am trying to duplicate R's computation of standard errors but having some trouble. i loaded some data into R and ran summary(lm(y~x1+x2+x3+0, data=data)), but i am not sure how the "Std. Error" values are computed. let y be the nx1 vector of dependent variables and X be the nx3 matrix of independent variables. let T(.) denote the transpose of a matrix/vector, and let I(.) denote

Hello, I am trying to estimate a multivariate regression of Y on X with regression splines. Y is (nx1), and X is (nxd), with d>1. I assume the data is generated by some unknown regression function f(X), as in Y = f(X) + u, where u is some well-behaved regression error. I want to estimate f(X) via regression splines (tensor product splines). Then, I want to get the predicted values for some new

Hi There, While looking through the mailing list archive, I did not come across a simple minded example regarding the creation of dummy variables. The Gauss language provides the command "y = dummydn(x,v,p)" for creating dummy variables. Here: x = Nx1 vector of data to be broken up into dummy variables. v = Kx1 vector specifying the K-1 breakpoints p = positive integer in the range

Why anything but sweep? The fundamental data type in Matlab is a matrix... they don't have vectors, they have Nx1 matrices and 1xM matrices. Vectors don't have any concept of "row" vs. "column". Straight division is always elementwise with recycling as needed, and matrices are really vectors in row-major order: 1 2 3 4 5 6 is really 1 4 2 5 3 6 and when you do

Am Mo., 22. Juli 2019 um 10:50 Uhr schrieb Doerfert, Johannes <jdoerfert at anl.gov>: > Why introduce a new intrinsic (family)? It seems that would require us > to support GEPs and GEP + "multi-dim" semantics in various places. What is > the benefit over a GEP extension? Adding an intrinsic is easier than adding or extending an existing instruction, as suggested by

Dear: Continuing the issue of 'ifelse'! I selecting the data whose 'x2'=1 for maximizing likelihood. I used two way to do this but the results are different. 1.Way one I use the data for x2=1 and run the program. It works for me. Tthe program is described as below: function (parameters,y1,x11) { p<-parameters[1] alpha1<-parameters[2] beta1<-parameters[3]

In the documentation, it says that optimize "should only be called when the index will no longer be updated very often, but will be read a lot". Does this mean it actually has a detrimental impact on updates and inserts? In my project there will be many more reads than updates, but there will still be a lot of updates. So should I be calling Optimize once a day or something like that,