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Dear R-Users I have the following matrix > out$desc [,1] [,2] [1,] "" "" [2,] "y_{01}(k-001)" "" [3,] "y_{01}(k-002)" "" [4,] "y_{01}(k-003)" "" [5,] "u_{01}(k-001)" "" [6,] "u_{01}(k-002)" "" [7,] "u_{01}(k-003)" "" [8,] "y_{01}(k-001)" "y_{01}...

...This is most likely only a minor technicality, but I saw the following: On page 6 of the 'parallel' vignette (http://stat.ethz.ch/R-manual/R-devel/library/parallel/doc/parallel.pdf), the random-number generator "L'Ecuyer-CMRG" is said to have seed "(x_n, x_{n-1}, x_{n-2}, y_n, y_{n-1}, y_{n-2})". However, in L'Ecuyer et al. (2002), the seed is given with 'increasing' indices, so should rather be "(x_{n-2}, x_{n-1}, x_n, y_{n-2}, y_{n-1}, y_n)" (or, even more intuitively, "(x_{n-3}, x_{n-2}, x_{n-1}, y_{n-3}, y_{n-2}, y_{n-1})"). The...

...only a minor technicality, but I saw the > following: On page 6 of the 'parallel' vignette > (http://stat.ethz.ch/R-manual/R-devel/library/parallel/doc/parallel.pdf), > the random-number generator "L'Ecuyer-CMRG" is said to have seed > "(x_n, x_{n-1}, x_{n-2}, y_n, y_{n-1}, y_{n-2})". However, in L'Ecuyer > et al. (2002), the seed is given with 'increasing' indices, so should > rather be "(x_{n-2}, x_{n-1}, x_n, y_{n-2}, y_{n-1}, y_n)" (or, even > more intuitively, "(x_{n-3}, x_{n-2}, x_{n-1}, y_{n-3}, y_{n-2}, >...

...do not know how to tell R what I want it to do^^ My situation is as follows: I have a data set containing a (dependent) vector Y and the regressor X. My aim is to check whether the two variables do not granger-cause each other in quantiles. I started to compute via quantreg for a single tau:= q: rq(Y_t~Y_(t-1)+Y_(t-2)+...+X_(t-1)+X_(t-2)+...,tau=q) This gives me the quantile regression coefficients. Now I want to check whether all the coefficients of X are equal to zero (for this specific tau). Can I do this by applying rq.anova ? I have already asked a similiar question but I am not sure if an...

...have the following problem to solve and I wonder if there are means in R that can help me. At irregular time intervals I observe a random walk process, Y, with time-varying drift. I assume that the drift, D, is a (linear) function of some parameter X. The goal is to estimate D(X). I could regress Y_{t+dt} - Y_{t} ~ X, but it's probably not appropriate since Var(Y_{t+dt} - Y_{t}) is not constant (actually in theory it is proportional to dt). Any suggestions? Thank you, Vadim -------------------------------------------------- DISCLAIMER This e-mail, and any attachments thereto, is inten...

Dear All: I need advice about efficient looping/vectorization. I am trying to bootstrap a regression model with one lag of the dependent variable in the RHS. Specifically, let error^b_(t) be the bootstrapped error of the regression y_(t) = gamma y_(t-1) + beta x +error_(t) at time (t), y_(t) is the original dependent variable, and y^b_(t) the bootstraped y_(t) using parameter estimates gamma and beta. My basic procedure is like this: 1. Get the first y^b value using y_(1): y^b_(2) = gamma y_(1) + beta x_(2) + error_(2).b 2. Get...

...substituting rbind for cbind, and sometimes I get a different error message, but I could not find a variation of this code that would work. Any help will be greatly appreciated. I am running R version 2.3, on Windows XP Professional. > library(sspir) > > # Create observation vector > y_ <- cbind(rep(1, 20), rep(2, 20)) > > mat <- rbind(c(1, 2), c(3, 4)) > > ##################################################################### > # Create model > model <- SS(y = matrix(y_), + Fmat = function(tt, x, phi) { + FM <- mat +...

Hi all, I have some difficulties with the lme function and so this is my problem. Supoose i have the following model y_(ijk)=beta_j + e_i + epsilon_(ijk) where beta_j are fixed effects, e_i is a random effect and epsilon_(ijk) is the error. If i want to estimate a such model, i execute >lme(y~vec.J , random~1 |vec .I ) where y is the vector of my data, vec.J is a factor object and vec.I is the vector for...

.... . . . . . . . . . . 2000 1 1 5.12 2000 1 2 2.87 2000 1 3 7.23 . . . . . . . . . . . . 2000 4 1 2.48 2000 4 2 3.93 2000 4 3 5.17 that is, I have data Y_{gtr} for g (gene) =1,...,2000 t (treatment) = 1,...,4 and r (replicate) = 1,...,3 I would like to fit the following linear mixed model using lme Y_{gtr} = \mu_{g} + W_{gt} + Z_{gtr} where the \mu_{g}'s are fixed gene effects, W_{gt} ~ N(0, \sigma^{2}) gene-treatment interactions, and...

Dear R users, I have been trying to obtain the MLE of the following model state 0: y_t = 2 + 0.5 * y_{t-1} + e_t state 1: y_t = 0.5 + 0.9 * y_{t-1} + e_t where e_t ~ iidN(0,1) transition probability between states is 0.2 I've generated some fake data and tried to estimate the parameters using the constrOptim() function but I can't get sensible answers using it. I've t...

Ok I made Jarque-Bera test to the residuals (merv.reg$residual) library(tseries) jarque.bera.test(merv.reg$residual) X-squared = 1772.369, df = 2, p-value = < 2.2e-16 And I reject the null hypotesis (H0: merv.reg$residual are normally distributed) So I know that: 1 - merv.reg$residual aren't independently distributed (Box-Ljung test) 2 - merv.reg$residual aren't indentically

Hi R-helpers, I'm new to time series modelling, but my requirement seems to fall just outside the capabilities of the arima function in R. I'd like to fit an ARMA model where the variance of the disturbances is a function of some exogenous variable. So something like: Y_t = a_0 + a_1 * Y_(t-1) +...+ a_p * Y_(t-p) + b_1 * e_(t-1) +...+ b_q * e_(t-q) + e_t, where e_t ~ N(0, sigma^2_t), and with the variance specified by something like sigma^2_t = exp(beta_t * X_t), where X_t is my exogenous variable. I would be very grateful if somebody could point me in the di...

...sides = 2, circular = FALSE, init) # init: for recursive filters only. Specifies the initial values of # the time series just prior to the start value, in reverse # time order. The default is a set of zeros. but looks as if it should be in usual order as x is e.g. init y_0 y_-1 y_-2: 3, 2, 1 filter f_1 f_2 f_3: 1, .5, .25 x: 4, 5, 6, 7, 8 y_1 = 4 + 1*3 + .5*2 + .25*1 = 8.25 y_2 = 5 + 1*8.25 + .5*3 + .25*2 = 15.25 ... but > filter(4:8,c(1,.5,.25),method="recursive", init=3:1) Time Series: Start = 1 End = 5 Frequency = 1 [1] 6.7500 12.7500 22.3750 37...

...nce parameters as they can be put out into a SAS data set. How do I do the same with lme-objects? For example, I can see the estimated variance matrix when typing the name of the lme-object, but that does not give me a "handle" on the matrix. 2) I am trying to fit a mixed model, say y_{ij} = X_{ij}\beta + U_i + e_{ij} where Y_{ij}, U_i and e_{ij} all are D-dimensional, and U_i ~ N_D(0,G), and e_{ij} ~ N(0,R). This is easy to do in proc mixed by including a factor keeping track of how the elements of y_{ij} are "stacked" on top of each other. This factor is then used in...

...e in which the "teacher effect" applies to only one year. One can think of the first score on the student as a score from a prior year (for which I have no teacher links), and the second score is from the current year and is linked to the teacher. The model for student j in class i is: Y_{ij0} = a_0 + e_{ij0} Y_{ij1} = a_1 + b_i + e_{ij1} with Var(b_i) the teacher variance component and Cov(e_{ij0},e_{ij1}) unstructured. That is, if the data are organized by student, the "Z" matrix in the usual linear mixed model notation has every other row equal to a row of zeros. I a...

Hello - Here's what I'm trying to do. I want to fit a time series y with ARMA(1,1) + GARCH(1,1), there are also an exogeneous variable x which I wish to include, so the whole equation looks like: y_t - \phi y_{t-1} = \sigma_t \epsilon_t + \theta \sigma_{t-1} \epsilon_{t-1} + c x_t where \epsilon_t are i.i.d. random variables \sigma_t^2 = omega + \alpha \sigma_{t-1}^2 + \beta y_{t-1}^2 I looked through documentation of garchFit() from the fGarch library but didn't find a way to...

Hi, I need help regarding estimating a linear model where restrictions are imposed on the coefficients. An example is as follows: Y_{t+2}=a1Y_{t+1} + a2 Y_t + b x_t + e_t restriction a1+ a2 =1 Is there a function or a package that can estimate the coefficient of a model like this? I want to estimate the coefficients rather than test them. Thank you for your help Ahmad Abu Hammour -------------- next part -------------- An HTML...

Here is a test case: extern double floor(double); extern double floor_(double); double x = 1.5; double y, y_; void foo() { double y = floor(x); double y_ = floor_(x); } If I compile this for Mips16, it calls the proper helper function for floor_ but not for floor, because the signature for floor in callee info is wrong. Args[0] = void RetTy = void /local/llvmpb_config/install/bin/clang -targe...

Hi, I'm trying to estimate the parameters of a state space model of the following form measurement eq: z_t = a + b*y_t + eps_t transition eq y_t+h = (I -exp(-hL))theta + exp(-hL)y_t+ eta_{t+h}. The problem is that the distribution of the innovations of the transition equation depend on the previous value of the state variable. To be exact: y_t|y_{t-1} ~N(mu, Q_t) where Q is a diagonal matrix with elements equal...

I'm getting some problems because it seems that the compiler is treating "floor" differently from other math library functions like "sin". The Args and RetVal have the parameter and return types marked as void. For mips16, it's important that I be able to know the original signature for floating point functions. In some cases, need to create calls to helper