similar to: stochastic process transition probabilities estimation

Hello! In the example below the Reduce() function by default assigns "a" to be the last accumulated value and "b" to be the current value in "x". I could not find this documented anywhere as the default settings for the Reduce() function. Does any sort of documentation for this behavior exist? x <- c(0,0,0,0,0,1,0,0,0,5,0,0,0,7,0,0,0,8,5,10)

For 2 or 3 stage least squares, what do you R folks do? Follow-up question. My student wants to estimate this. 2 variables are governed by a system of difference equations. His theory is like so. Y_t and X_t are state variables, we want estimates for a, g, b, and h. X_(t+1) = 1 + a X_t + (a/K)* (X_t)^2 - g Y_t X_t Y_(t+1) = b Y_t + h* X_t * Y_t K is perhaps something to estimate, but it

Hi, I noticed a significant performance regression (up to 40%) on some internal CUDA benchmarks (a reduced example presented below). The root cause of this regression seems that IndVarSimpilfy widens induction variables assuming arithmetics on wider integer types are as cheap as those on narrower ones. However, this assumption is wrong at least for the NVPTX64 target. Although the NVPTX64 target

I saw a Help e-mail related to MLE. Does R have a probability weighted method (PWM) estimator function? I can't seem to find anything on PWM, unless my eyes are playing trick on me. [[alternative HTML version deleted]]

Vince Carey found this (thank you!). Since the fix to the problem is not entirely obvious, I post this to R-devel as RFC: help(dist) says: >> Missing values are allowed, and are excluded from all computations >> involving the rows within which they occur. If some columns are >> excluded in calculating a Euclidean, Manhattan or Canberra >> distance, the sum is

I'm trying to use the following command. arima (x, order = c(p,d,q), seasonal =list(order=c(P,D,Q), period=s) How can I write an estimated seasonal ARIMA model from the outputs. To be specifically, which sign to use? I know R uses a different signs from S plus. Is it correct that the model is: (1-ar1*B-ar2*B^2-...)(1-sar1*B^s-sar2*B^2s-....)(1-B)^d(1-B^s)^D

Dear R users, I have to call fortran program from within R (R 2.8.1 on ubuntu 8.10 machine). Suppose I have a fortran code like this (this is only a toy model, my working model is far more complex, but input/output is similar) DOUBLE PRECISION FUNCTION model(times, alfa, beta) DOUBLE PRECISION alfa, beta, times model=beta*sin(times)+alfa*cos(times) END FUNCTION which

Full_Name: Massimo santini Version: 1.0.1 OS: Linux Submission from: (NULL) (159.149.147.89) I've just dl the .rpm of R 1.0.1, installed it and then launched insatll.packages("akima") after re-launching R [--vanilla], the command library(akima) has the only effect to coredump the program. Here is the output of gdb and strace... --- GDB (commands given can be seen from the

Hello, I would like, if it is possible, to compare the effect of a variable across regression models. I have looked around but I haven't found anything. Maybe someone could help? Here is the problem: I am studying the effect of a variable (age) on an outcome (local recurrence: lr). I have built 3 models: - model 1: lr ~ age y = \beta_(a1).age - model 2: lr ~ age + presentation

Hi R users, where can I find the equations used by acf function to calculate autocorrelation? I think I misunderstand acf. Doesn't acf use following equation to calculate autocorrelation? [image: R(\tau) = \frac{\operatorname{E}[(X_t - \mu)(X_{t+\tau} - \mu)]}{\sigma^2}\, ,] If it does, then the autocorrelation of a sine function should give a cosine; however, the following code gives a

Dear R People: Suppose I want to estimate the parameters of the following AR model: (1 - phi_1 B - phi_2 B^2 - phi_9 B^9) x_t = a_t and I want to use the arima0 command from the ts library. How would I use the order subcommand, please? R Version 1.4.1 for Windows. Thanks! Sincerely, Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston -

  Hi, All,   This is the first time I seriously use this package. However, I am confused that the result is quite unstable. Maybe I wrote something wrong in the code? So could anybody give me some hint? Many thanks.   My test model is really simple. Y_t = X_t * a_t + noise(V)，(no Intercept here) a_t = a_{t-1} + noise(W)   I first run the following code: (I shall provide data at the end of the

This has ABSOLUTELY nothing to do with R but I was hoping that someone might know because there are obviously a lot of very bright people on this list. Suppose I had a time series of data and at each point in time t, I was calculating x bar + plus minus sigma where x bar was based on a moving window of size n and so was sigma. So, if I was at time t , then x bar t plus minus sigma_t would be

Hello. I don't understand a particular output of portfolio.optim (tseries). I have 4 assets and the portfolio.optim returns an asset with weight equals to zero. If I do a portfolio.optim with 3 assets, without the asset with weight equals to zero, it returns a completely different result. That's I would expected the same weights as the run with 4 assets. Below the code. Thanks in

Hello, Guys: I'm from China, my English is poor and I'm new to R. The first message I sent to R help meets some problems, so I send again. Hope that I can get useful suggestions from you warm-hearted guys. Thanks. I builded a multiplicative seasonal ARMA model to a series named "cDownRange". And the order is (1,1)*(0,1)45 The regular AR=1; regular MA=1; seasonal AR=0; seasonal

This is not an R question but if anyone can help me, it's much appreciated. Suppose I have a series ( stationary ) y_t and a series x_t ( stationary )and x_t has variance sigma^2_x and epsilon is normal (0, sigma^2_epsilon ) and the two series have the relation y_t = Beta*x_t + epsilon My question is if there are particular values that sigma^2_x and sigma^2_epsilon have to take in

Dear R user, I'm trying to find a solution for optimizing my code. I have to run a 50.000 iteration long simulation and it is absolutely necessary to have an optimized code. I have to do this operation *sum_t ( t(X_t) %*% A %*% X_t )* where X_t is a (d*k) matrix which changes in time and A is a constant in time (d*d) matrix. I have put all my X_t in a three dimensional array X of dimension

Dear all, I am trying to write an R function that can estimate Transfer functions *with additive noise* i.e. Y_t = \delta^-1(B)\omega(B)X_{t-b} + N_t where B is the backward shift operator, b is the delay and N_t is a noisy component that can be modelled as an ARMA process. The parameters to both the impulse response function and the ARMA noisy component need to be estimated simultaneously. I

Hello, I have a big table with 3 columns and 103918 rows. This is the example,            time             species                 dbh 5 1 4.9377297 575 1 11.64127213 575 1 109.8182438 575 1 8.029809521 5 1 24.32501874 575 1 4.895992119 575 1 11.40567637 575 1 2.795090562 575 1 21.79281837 575 1 52.57476174 575 1 27.7290919 575 1 3.23262083 575 2 19.30612651 575 1

Dear R users, I have used the following function (in blue) aiming to find the linear regression between MOE and XLA and nesting my data by Species. I have obtained the following results (in green). model4<-lme(MOE~XLA, random = ~ XLA|Species, method="ML")summary(model4) Linear mixed-effects model fit by maximum likelihood Data: NULL         AIC     BIC   logLik  -1.040187 8.78533