similar to: writing sample values in to a file

Hi, I am newbie started learning ruby on rails i had an application which used to run pretty well latter i upgraded from webbrick to mongrel 1.1.5 then I am getting the foloowing errors and a default application runs on port 3000 by name iphone configuration utility i am unable to attach the screen shot as it is saying file to large. Error which i get in terminal is listed below => Booting

Hi, I am newbie started learning ruby on rails i had an application which used to run pretty well latter i upgraded from webbrick to mongrel 1.1.5 then I am getting the foloowing errors and a default application runs on port 3000 i have attached its screen shot as an attachement and error which i get in terminal below => Booting Mongrel (use ''script/server webrick'' to force

Hello All, I am working on a microblogging application.(similar to twitter) In my controller there is a method called replypopUp.It is a method that runs when the reply link on timeline is clicked.When I a click on the link a pop-up appears in which there is a text area and a post button.Inside the text area,the name of the user to whom I am replying should appear in the form

I get the following error when trying to run a spec command on a model test. The command is :- C:\Rspec Test\spec\models>spec bank_account_spec.rb The error is :- c:/jruby-1.5.0/lib/ruby/gems/1.8/gems/rspec-rails-1.3.0/lib/spec/rails/ matchers/ ar_be_valid.rb:2: uninitialized constant Spec::Rails (NameError) from c:/jruby-1.5.0/lib/ruby/gems/1.8/gems/rspec- rails-1.3.0/lib/spec/ra

hi could you help me to solve this issue Question: Using command rweibull(100,8,15), simulate n = 100 realizations from Weibull(8; 15) distribution. Using the simulated sample, compute the sample mean, variance and standard deviation of these observations. I am trying like this sim<-rweibull(100,8,15) # simulated sample SM<-mean(sim) # simulated sample mean var(sim) # variance

I am interested in creating multiple (say 1000) time series, from a given stochastic process, of length 250. I want to refer to each realization with its own variable name, of the format say, tsn, where n is the n'th simulation. i.e. ts1, ts2, ts3, ts4, .... , ts1000 The way I am thinking of doing this is placing the following code within another loop, and the 'tsn' assignment should

Dear Members, I am working with 6-dimensional Student-t distribution with 4 degrees of freedom truncated to [20; 60]. I have generated 100 000 samples from truncated multivariate Student-t distribution using rtmvt function from package ?tmvtnorm?. I have also calculated mean vector using equation (3) from attached pdf. The problem is, that after summing all elements in one column of rtmvt result

I have a data matrix X (n x k, say) each row of which constitutes an observation of a k-dimensional random variable which I am willing, if not happy, to assume to be Gaussian, with mean ``mu'' and covariance matrix ``Sigma''. Distinct rows of X may be assumed to correspond to independent realizations of this random variable. Most rows of X (all but 240 out of 6000+ rows)

Hi, sorry for bothering your guys again. I want to simulate 100 AR(1) data with cor(x_t, x_t-1)=rho=0.3. The mean of the first 70 data (x_1 to x_70) is 0 and the mean of the last 30 data (x_71 to x_100) is 2. Can I do it in the following way? x <- arima.sim(list=(ar=0.3), 100) mean <- c(rep(0, 70), rep(2, 30)) xnew <- x+mean If the above code to simulate 100 AR(1) data is right, what

Venables and Ripley describe a speedup where you take a structure like x<-NULL for(i in sequence) y<-c(y,function(x,i)) and convert it to one like x<-numeric(its length) for(i in sequence) y[i]<-function(x,i) I tried this speedup on some simple examples and it made them twice as fast. But now I am hitting a snag with some real code. This original version works:

Adding Support For Vectorcall Calling Convention ===================================================== Vectorcall Calling Convention for x64 ---------------------------------------------------- The __vectorcall calling convention specifies that arguments to functions are to be passed in registers, when possible. __vectorcall uses more registers for arguments than __fastcall or the default x64

I?m working on an R-implementation of the simulation-based finite-sample null-distribution of (R)LR-Test in Mixed Models (i.e. testing for Var(RandomEffect)=0) derived by C. M. Crainiceanu and D. Ruppert. I'm in the beginning stages of this project and while comparing quick and dirty grid-search-methods and more exact optim()/optimize()-based methods to find the maximum of a part of the

Hi , I am trying to get the estimate of smooth functions from a gam model by in the library(gam). What I get by constructing this model below are " edf. values ...and p-values" for the smooths functions and intercept. model <- gam(y~ s(height)+ s(weight)+s(time)+s(pol)) and I also get the smoothing parameters estimation by typing coef(model). The difficulty I am having

Hello I have several replicates (30) of a -- short -- time series (19 obs) and I'd like to estimate the first few lags autocorrelation function. Is there an immediate way of handling multiple realizations when computing the ACF in R? I'm using R-1.4.1 on a Linux Debian box. Thanks in advance claudia ------------------------------------------------------------------------- claudia

Hi, I'm currently working with a SARIMA model from which I want to make simulations. As I understand, neither sarima.Sim nor the functions in the gsarima package use historic realizations of the time series to simulate future values. However, I want to use historic values as input and simulate future values based on the history. Anyone who know whether such a function is available

Dear R-users, I would like to compute a robust covariance matrix of two series of realizations of random variables: ###Begin Example### data <- cbind(rnorm(100), rnorm(100)) model <- lm(data ~ 1) vcov(model) library(sandwich) NeweyWest(model) #produces an error ###End Example### NeweyWest() produces an error but sandwich(), vcovHAC(), kernHAC, weave(),... do not produce any errors. It

Dear r list, I posted this on the S list last week since i'm using some of the FinMetrics functions on copula. Knowing there is a copula package in R, I figure this would be an appropriate forum to ask this question. I want to model inverse relationship between two (non-normal, non-symmetric) marginals with the gumbel copula, or with any copula. Say, x is lognormal and y is norm. Since

hello R-masters. i have an R-issue here that i don't know if you'd wish to help me? about it: briefly i'd like to generate many (say hundred) realizations of a random walk, execute a few operations on each of them (mean time of return), and graph each realization on the same plot. IN OTHER WORDS I'D LIKE TO IMPOSE A LOOPING CYCLE TO THE COMMAND NOT THE ARGUMENT OF THE COMMAND.

I've been programming maximum likelihood estimation models using the function "optim." My current research requires modeling a particular parameter as a categorical variable (what R calls a "factor"), not as a continuous parameter. (The research question is, at what level of X does a subject in our experiment choose Y=1 instead of Y=0? So this is a "light switch"

A mutable interface to object files would be great to have but doesn't exist in any meaningful sense in LLVM today. David's hack and similar tricks are what's necessary right now. I'd love to fix that as its a question that comes up not infrequently. Sent from my iPhone > On Aug 25, 2015, at 2:44 AM, David Chisnall via llvm-dev <llvm-dev at lists.llvm.org> wrote: >