search for: f_t

...xecutable was still broken. I guess the reason is due to fastcall on function pointers, which Clang does not recognize. Consider the following snippet. #include <stdio.h> void __attribute__((fastcall)) f(int i) { printf("%d\n", i); } typedef void (*__attribute__((fastcall)) f_t)(int i); //typedef void __attribute__((fastcall)) (*f_t)(int i); int main() { f(42); f_t fp = f; fp(42); } Clang does not catch the attribute on f_t; instead it produces a warning "'fastcall' attribute only applies to function types". so the function pointer might b...

I am trying to make the following Kalman filter equations work and therefore produce their graphs. v_t=y_t - a_t a_t+1=a_t+K_t*v_t F_t=P_t+sigma.squared.epsilon P_t+1=P_t*(1-K_t)+sigma.squared.eta K_t=P_t/F_t Given: a_1=0,P_1=10^7,sigma.squared.epsilon=15099, sigma.squared.eta=1469.1 I have attached my code,which of course doesnt work.It produces NAs for the Fs,Ks and the a. Can somebody tell me please what am I doing wrong in t...

...ta(Nile) > fit <- StructTS(Nile, type = "level") > fit$loglik [1] -367.5194 When computing the log-likelihood with other packages such as KFAS and FKF, the loglikelihood value is around -645. For the local level model, the likelihood is defined by -0.5*n*log(2*pi) - 0.5*sum(log(F_t) + v_t^2/sqrt(F_t)) (see for example Durbin and Koopman (2001, page 30). But in StructTS, the likelihood is computed like this: loglik <- -length(y) * res$value + length(y) * log(2 * pi), where the first part coincides with the last part of the definition, but the constant part has wrong sign...

...) > n<-25 > X<-runif(n,0,1) > V<-runif(n,0.75,1) > for (i in 1:n){ > while (X[i]>V[i]){ > X[i]<-runif(1,0,1) > V[i]<-runif(1,0.75,1) > }} > res<-lynden(X=X,U=NA, V=V, boot=TRUE) > attach(res) > temps = time > M_i = n.event > L_t = res > F_t=1-L_t > F_t=1-L_t Error in 1 - L_t : non-numeric argument to binary operator L_t is a list. You cannot subtract a list (at least in R). I'm not sure what you think F_t is supposed to be and you don't seem to use it. If you are attempting to calcul...

On Fri, Apr 3, 2009 at 12:07 PM, Chris Lattner <clattner at apple.com> wrote: > It is impossible to tell with this amount of detail.  Does it work > correctly if you build with -O0 ? Yes, with -O0 the resulting executable looks fine. --enable-debug actually sets -O0 (otherwise -O2). Clang can build/test php 5.2.9 with either -O0 or -O2, but not for php 5.3RC0 with -O2. I further

What version of clang are you using? It could be a regression between head and the version I used. (some days old) - Anders On Fri, Apr 3, 2009 at 6:37 PM, Xi Wang <xi.wang at gmail.com> wrote: > On Fri, Apr 3, 2009 at 12:07 PM, Chris Lattner <clattner at apple.com> wrote: >> It is impossible to tell with this amount of detail.  Does it work >> correctly if you build

Hello! Is it possible to use R time series to identificate a process which is subjected to known input? I.e. I have 2 sequences - one is measurements of black box's state and the second is the "force" by which this black box is driven (which is known too) and I want to fit thist two series with AR-process. The "ar" procedure from stats package expects that the force is

...ch are right truncated library(DTDA) library(splines) library(survival) n<-25 X<-runif(n,0,1) V<-runif(n,0.75,1) for (i in 1:n){ while (X[i]>V[i]){ X[i]<-runif(1,0,1) V[i]<-runif(1,0.75,1) }} res<-lynden(X=X,U=NA, V=V, boot=TRUE) attach(res) temps = time M_i = n.event L_t = res F_t=1-L_t??????????????????????????????????? ? par(mfrow=c(1,1)) plot(L_t$time,L_t$survival,type="s",lty=2:3,lwd=2,las=1,cex.lab=1.1,font.lab=2,col="red",xlab="temps",ylab="L(t)",main="Esitmation de la Fonction de Survie L(t)") i need to calculate the...

Hi, I am trying to figure how to use dlm with constant terms (possibly time-dependent) added to both equations y_t = c_t + F_t\theta_t + v_t \theta_t = d_t + G_t\theta_{t-1} + w_t, in the way that S-PLUS Finmetrics does? Is there any straightforward way to transform the above to the default setup? Thanks, Tsvetan -------------------------------------------------------- NOTICE: If received in error, please destroy and no...

...hink it makes sense, but I'm not sure. Here are the observed cell means: conflict noconflict control 0.6896552 0.9568966 high 0.6935484 0.9677419 low 0.8846154 0.9903846 GLMER gives me: summary(glmer(accuracy~f_power*f_type + (1|subject), family=binomial,data=syllogisms)) Generalized linear mixed model fit by the Laplace approximation Formula: accuracy ~ f_power * f_type + (1 | subject) Data: syllogisms AIC BIC logLik deviance 406 437.7 -196 392 Random effects: Groups Name Variance Std.Dev. sub...

  Hello, fellow R users,   I recently need to estimate a dynamic linear model in the following form:   For the measurement equation:   Y_t = F_t * a_t + v_t   where Y_t is the observation. It is a 1 by q row vector for each t. F_t is my forecasting variable. It is a 1 by p row vector. a_t is my state variable. It is a p by q MATRIX of parameters with each column of the matrix being regression coefficient of a random variable in Y_t. And v i...

I am having a problem with the package SSPIR. The code below illustrates it. I keep getting the message: "Error in y - f : non-conformable arrays." I tried to tweak the code below in many different ways, for example, 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

...2 and 3 factor models. When I started to read the functions on R, I got to the function ss on the library sspir. From what I understood this function is similar to SsfFit from S-PLUS. But for my models purpose there is something left to be desired. Its formulation follow these equations: *Y_t = F_t^T * theta_t + v_t, v_t ~ N(0,V_t)* *theta_t = G_t * theta_{t-1} + w_t, w_t ~ N(0,W_t)* Actually I wanted to add these two functions (-logA and a) Y(t) = -log(A(t))/tau + (B(t)/tau) * X(t) + Error (measurement equation) X(t) = a(t) + b(t) * X(t-1) + Error (Transition Equ...

Dear all, I am currently trying to use the "dlm" package for Kalman filtering. My model is very simple: Y_t = F'_t Theta_t + v_t Theta_t = G_t Theta_t-1 + w_t v_t ~ N(0,V_t) = N(0,V) w_t ~ N(0,W_t) = N(0,W) Y_ t is a univariate time series (1x1) F_t is a vector of factor returns (Kx1) Theta_t is the state vector (Kx1) G_t is the identity matrix My first challenge is to get the Maximum Likelihood estimators of V and W assuming they are time-invariant (homoscedastic) through the dlmMLE function. In the example provided in the user guide,...

...mate the coefficient of the utilization rate in time-varying terms and all the other coefficients of the investment function in constant terms. The issue I have now is that the package "dlm" only allows for specifications of the form (5) b_t = 1 * b_t-1 + w_t w_t ~ N(0,W) (6) y_t = F_t * b_t + v_t v_t ~ N(0,V) where b_t is the vector of parameters. Hence, it does not allow me to take parameters as time-invariant. In terms of my investment specification this reads (7) (a1_t, a2_t, a3_t)' = diag(3) * (a1_t-1, a2_t-1, a3_t-1)' + w_t w_t ~ N(0,W) (8) g_t = (1, P_t, u...

We need some idea of the problem. http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example http://adv-r.had.co.nz/Reproducibility.html On Thursday, May 10, 2018, 11:07:30 a.m. EDT, malika yassa via R-help <r-help at r-project.org> wrote: Hello Do You help me, i have the problem in the package DTDA for ?find the probability of truncation

...f(n,0.75,1) > > for (i in 1:n){ > > while (X[i]>V[i]){ > > X[i]<-runif(1,0,1) > > V[i]<-runif(1,0.75,1) > > }} > > res<-lynden(X=X,U=NA, V=V, boot=TRUE) > > attach(res) > > temps = time > > M_i = n.event > > L_t = res > > F_t=1-L_t? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? > > par(mfrow=c(1,1)) > > plot(L_t$time,L_t$survival,type="s",lty=2:3,lwd=2,las=1,cex.lab=1.1,font.lab=2,col="red",xlab="temps",ylab="L(t)", > > > main="Esitmation de la Fonction de Survie L...

Dear R Users, I am new to state-space modeling. I am using SSPIR package for Kalman Filter. I have a data set containing one dependent variable and 7 independent variables with 250 data points. I want to use Kalman Filter for forecast the future values of the dependent variable using a multiple regression framework. I have used ssm function to produce the state space (SS)

Hi! Using HEAD, and after the 7193:e5af14239137 changeset (Store physical message sizes also to dovecot-uidlist if needed.) I'm getting this: (gdb) bt full #0 0x0a0a80dd in kill () from /usr/lib/libc.so.41.0 No symbol table info available. #1 0x0a0e04a7 in abort () at /usr/src/lib/libc/stdlib/abort.c:65 p = (struct atexit *) 0x84992000 cleanup_called = 0 mask =