similar to: random number generator

I would like to get the fortran code for the functions locpol and dpill in the kernsmooth library , the purpose is to incorporate other selection criterion for the bandwidth in which a varying bandwidths are replaced instead of the constant bandwidth. I will send you back the improved version as soon as I get the fortran code for the upper mention functions. ((LOCPOLY and DPILL )))

Buenas noches; Además del proyecto que comenté antes y con el que sigo discutiendo, también me estoy peleando con otro... con el que también tropiezo. Necesito reordenar los datos presentados en dos columnas a filas y columnas. Los datos ideales serían algo así: Ques <- c(rep("Q1",3),rep("Q2",3),rep("Q3",3)) Info <- rep(c("aca", "ahi",

Dear R-helpers, I am using confint() within a function, and I want to turn off the message it prints: x <- rnorm(100) y <- x^1.1+rnorm(100) nlsfit <- nls(y ~ g0*x^g1, start=list(g0=1,g1=1)) > confint(nlsfit) Waiting for profiling to be done... 2.5% 97.5% g0 0.4484198 1.143761 g1 1.0380479 2.370057 I cannot find any way to turn off 'Waiting for. .." I tried

Dear samba-users We are experiencing some rather long logon times using SAMBA 3.5.6 on Debian Squeeze + Windows 7 and while trying to find out why we raised the loglevel to 5 resulting in a rather large logfile. But this did give us a hint as to why we experience so long logon times. It seems that samba is going through the whole user-database with all usernames and machine-accounts. Eventually

I am trying to fit a rank-frequency distribution with 3 unknowns (a, b and k) to a set of data. This is my data set: y <- c(37047647,27083970,23944887,22536157,20133224, 20088720,18774883,18415648,17103717,13580739,12350767, 8682289,7496355,7248810,7022120,6396495,6262477,6005496, 5065887,4594147,2853307,2745322,454572,448397,275136,268771) and this is the fit I'm trying to do: nlsfit

Why fails nls with "singular gradient" here? I post a minimal example on the bottom and would be very happy if someone could help me. Kind regards, ########### # define some constants smallc <- 0.0001 t <- seq(0,1,0.001) t0 <- 0.5 tau1 <- 0.02 # generate yy(t) yy <- 1/2 * ( 1- tanh((t - t0)/smallc) * exp(-t / tau1) ) + rnorm(length(t))*0.01 # show the curve

Thank you for your time...we made some changes to our config file and the server doesn't want to start with the command icecast -c /path/to/icecast.xml giving the error: 'Could not create listener socket on port 8000 Server startup failed. Exiting' can you look at the config file bellow and let us know of any changes that might help. we need to be able to have 10,000 clients

Dear List, I want to compare the presence and absence of bird species based on the sites in a matrix. The matrix has 5 rows for Island A, B, C, D, and E. It has 100 columns for bird species D001-D100. In each cell of the matrix, the presence-absence of bird species will be recorded as 1 or 0. (For example, if species D001 is found on Island D, the matrix cell of species D001 and Island D

I am trying to simulate draws from a multivariate normal using mvrnorm, and am getting the following error message: Error in mu + eS$vectors %*% diag(sqrt(pmax(ev, 0)), p) %*% t(X) : non-conformable arrays I do not understand why I am getting this message, since the vector of means I am giving to the function is 13 by 1 and the variance matrix I am giving to the function is 13

Dear list, I am trying to use the 'mvrnorm' function from the MASS package for simulating multivariate Gaussian data with given covariance matrix. The diagonal elements of my covariance matrix should be the same, i.e., all variables have the same marginal variance. Also all correlations between all pair of variables should be identical, but could be any value in [-1,1]. The problem I am

Hi all, Recently our statistics students noticed that their Gibbs samplers were crashing due to some NaNs in some parameters. The NaNs came from mvrnorm (Ripley & Venables' MASS package multivariate normal sampling function) and with some more investigation it turned out that they were generated by function eigen, the eigenvalue computing function. The problem did not seem to happen

Hello, does anybody know another faster function for random multivariate normal variable simulation? I'm using mvrnorm, but as profiling shows, my algorithm spends approximately 50 % in executing mvrnorm function. Maybe some of you knows much faster function for multivariate normal simulation? I would be very gratefull for advices. -- View this message in context:

Hi all, I'm using the code below within a loop that I run thousands of times and even with the super-computing resources at my disposal this is just too slow. The snippet below takes about 10s on my machines, which is an order of magnitude or two slower than would be preferable; in the end I'd like to set the number of monte carlo experiments to 1e4 or even 1e5 to ensure stable

Hi All This might be something very trivial but I seem to miss something in the syntax or logic which makes me keep wandering around the problem without arriving at a solution. What I want to do is to simulate a sample data for performing cluster analysis. I tried to use x1= mvrnorm(10,rep(0.8,3),diag(3)) x2= mvrnorm(10,rep(0,3),diag(3)) x3= mvrnorm(10,rep(-0.5,3),diag(3)) x=rbind(x1,x2,x3)

Hello. Somebody knows how can I generate a set of n random vectors of a normal distribution of several variables? For example, I want to generate n=100 random vectors of two dimensions for a normal with mean c(0,1) and variance matrix: matrix(c(2,1,1,3),2,2). Thanks in advance, Arnau.

Hi all. I was having some trouble with a for loop and I found the problem is the following. Does anyone have some idea why I got the following R result? Since mone is equal to 3, why mu1 only have 2 components? library(MASS) > p0 <- seq(0.1, 0.9,by=0.1) > m <- 10 > > > p0 <- p0[7] > > ## data generation > > mzero <- p0*m > mone <- m-mzero >

Hello, 1) I am interested in performing a limited-dependent variable linear regression. By this I mean a classical linear regression, but for the case where the values of the dependent variable cannot vary from -infinity to +infinity, but are truncated and so are between two finite limits L1 and L2. Does R1.7 have this capability? If so what is (are) the relevant command(s)? 2) I am also

I have data vectors x and y both with 179 observations. I'm trying to fit a nonlinear model with five parameters using nls. The formula is only defined within a range of x-values, it should be zero otherwise, thus my attempted use of ifelse: > df<-data.frame(x,y) >

Hello everybody Sorry if such a question has already been posted somewhere, but I wasn’t able to find an answer yet. I’m trying to generate random numbers for illustration purposes. The random numbers should satisfy a multiple regression equation, for example: z = 0.3 + 0.05 * x + 1.5 * y + 3 * xy I was playing around with mvrnorm (MASS), but since I’m still a newbie to R and not an educated

Dear List: I am generating N datasets using the following Sigma<-matrix(c(400,80,80,80,80,400,80,80,80,80,400,80,80,80,80,400),4,4 ) mu<-c(100,150,200,250) N=100 for(i in 1:N) { assign(paste("Data.", i, sep=''), as.data.frame(cbind(seq(1:1000),(mvrnorm(n=1000, mu, Sigma))))) } With these datasets, I need to work on some of the variables and then run each dataset