similar to: how to increase speed for function?/time efficiency of below function

Hello , I have estimated the following model, a sarima: p=9 d=1 q=2 P=0 D=1 Q=1 S=12 In R 2.12.2 Call: arima(x = xdata, order = c(p, d, q), seasonal = list(order = c(P, D, Q), period = S), optim.control = list(reltol = tol)) Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.3152 0.8762 -0.4413 0.0152 0.1500 0.0001 -0.0413 -0.1811

I have the following FORTRAN code converted to a DLL: ! my_xmean.f90 ! ! FUNCTIONS/SUBROUTINES exported from my_function.dll: ! my_function - subroutine ! subroutine my_xmean(X,N,XMEAN) ! Expose subroutine my_function to users of this DLL ! !DEC$ ATTRIBUTES DLLEXPORT,C,REFERENCE,ALIAS:'my_xmean_'::my_xmean ! Body of my_function DOUBLE PRECISION X(N) XMEAN=0D0 DO J=1,N

Can someone please explain what the $minimum result of the optimize function actually is? I'm trying to optimize the function: fitIT<-function(ampFac,ts_wave1,ts_template){ template<-stretchWaveTime(ts_template,ampFac) fit<-calcFit(ts_wave1,template) return(fit) } with >optimize(f=fitIT,interval=c(0.5,4),ts_wave1=test.data[,1],ts_template=test.data[,1]) $minimum [1]

Dear R users, I am trying to fit the following ANCOVA model in R2.4.0 Y_ij=mu+alpha_i+beta*(X_ij-X..)+epsilon_ij Particularly I am interested in obtaining estimates for mu, and the effects alpha_i I have this data (from the book Applied Linear Statistical Models by Neter et al (1996), page 1020) y<-c(38,43,24,39,38,32,36,38,31,45,27,21,33,34,28)

Dear list, I have a question related to the correct interpretation of the relative convergence criterion used by 'optim'. In the help of the function is it written that: "reltol:Relative convergence tolerance. The algorithm stops if it is unable to reduce the value by a factor of reltol * (abs(val) + reltol) at a step." and I was wondering if the previous criterion is

Hi Everybody I use R2.8.0 on Mac OS X. I set up LyX 1.6.1 to use Sweave today. I can compile the test file I found on CRAN ( http://cran.r-project.org/contrib/extra/lyx/) without a problem and the output looks very nice. In the test file the following R code is used. <<myFirstChunkInLyX>>= xObs <- 100; xMean <- 10; xVar <- 9 x <- rnorm(n=xObs, mean=xMean, sd=sqrt(xVar))

Dear all, I'm new to R and time series analysis. I'd appreciate if you could shed light on my problem. Here is what I have been trying to do: 1. I fit the model ARIMA(1,0,0) with the training dataset xdata[1:100] fitit = arima(xdata, order=c(1,0,0) 2. I have some current observations in the buffer. Say that buf = xdata_new[1:20] 3. I'm trying to forecast the xdata_new[21] based on

Hello , I have estimated the following model, a sarima: p=9 d=1 q=2 P=0 D=1 Q=1 S=12 In R 2.12.2 Call: arima(x = xdata, order = c(p, d, q), seasonal = list(order = c(P, D, Q), period = S), optim.control = list(reltol = tol)) Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.3152 0.8762 -0.4413 0.0152 0.1500 0.0001 -0.0413 -0.1811

Dear @ll. I have to calculate numerical integrals for triangular and trapezoidal figures. I know you can calculate the exactly, but I want to do it this way to learn how to proceed with more complicated shapes. The code I'm using is the following: integrand<-function(x) { print(x) if(x<fx[1]) return(0) if(x>=fx[1] && x<fx[2]) return((x-fx[1])/(fx[2]-fx[1]))

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

Hello I would like to source an R script from within a C# .Net application equivalent to: source("my_r_code.r") I can get this to run but am not sure how to retrieve R objects defined with script my_r_code.r at runtime. For example, if "my_r_code.r" contains #-- contents of my_r_code.r--------------------- x <- 1:10 xmean <- mean(x)

Hello all, I have the code below to simulate samples of certain size from a particular distribution (here,beta distribution) and compute some statistics for the samples. betasim2<-function(nsim,n,alpha,beta) { sim<-matrix(rbeta(nsim*n,alpha,beta),ncol=n) xmean<-apply(sim,1,mean) xvar<-apply(sim,1,var) xmedian<-apply(sim,1,median)

Hello List: Does anyone know if there's a way to annotate the plots produced by the plot.xmean.ordinaly function in the rms package. I'd like to produce publication quality figures, but first need to annotate the axis labels. Is there a way to do that? I appreciate any advice. Chris [[alternative HTML version deleted]]

Hi, i have some problems to get means, but the data have got NA's which should not be replaced or delete ! tapply(data,var,mean) # makes me problem,because when one value is NA mean is NA,too. Another attempt is this, because the summary does what i want , but is not subsetable to summary[4] if i use tapply,table and the the function below gives me again all means as NA ? xmean

I would like to simulate some SARIMA models, e.g. a SARIMA (1,0,1)(1,0,1)[4] process. I installed the package 'forecast', where the function simulate.Arima should do what I am trying to do. I am not able to understand how it works Could somebody help me with an example? thank you Stefano Sofia AVVISO IMPORTANTE: Questo messaggio di posta elettronica pu? contenere informazioni

Hi, I apologize for the long message but the problem I encountered can't be stated in a few lines. I am having some problems with the function constrOptim. My goal is to maximize the likelihood of product of K multinomials, each with four catagories under linear constraints on the parameter values. I have found that the function does not work for many data configurations. #The likelihood

Thanks a lot to everybody that helped me out with this. Conclusions: (1) In order to edit arima in R: >fix(arima) or alternatively: >arima<-edit(arima) (2) This is not contained in the "Introduction to R" manual. (3) A "productive" fix of arima is attached (arma coefficients printed out and error catched so that it doesn't halt parent loops to search for

Hello to all, I have been using an optim with the following call: optim(param_ini,fun_errores2,Precio_mercado=Precio,anos_pagosE2=anos_pagos,control=list(maxit=10000,reltol=1e-16)) depending on the intial values I'm getting the same solution but once I get the convergence message=10 (no convergence) and for the others I get convergence message = 0 Solution1: $par beta1

Does anyone have an exemple of how to fit a SARIMA model , with a MA part, with the package dynlm? Best regards. --------------------------------- [[alternative HTML version deleted]]

I'm trying to fit a SARIMA(p,d,q)x(P,D,Q) with seasonal period s to some data. When dealing with these types of models one often looks at the ACF and PACF of the time series at lags that are multiples of s, to identify potential values of P, Q. How would I do this in R given the original time series? Secondly given a time series x acf(x) just gives me the plot of the acf. How would I actually