search for: ar4

...sp(label shortcirc_next.1.selectcont) shortcirc_next.1.selecttrue (0x8065cd0, LLVM BB @0x8063fb0): %gr3 = move 1 %gr3 = move %gr3 shortcirc_next.1.selectcont (0x8065d30, LLVM BB @0x8063f08): %gr3 = move %gr3 %gr3 = move %gr3 %gr4 = move <ga:.str_1> %gr4 = move %gr4 %ar7 = + %ar7, 8 %ar4 = - %ar7, 1 store %ar4, %gr4 %ar4 = - %ar7, 2 store %ar4, %gr0 %ar4 = - %ar7, 3 store %ar4, %gr1 %ar4 = - %ar7, 4 %gr0 = move 0 store %ar4, %gr0 %ar4 = - %ar7, 5 store %ar4, %gr2 %ar4 = - %ar7, 6 %gr0 = move 0 store %ar4, %gr0 %ar4 = - %ar7, 7 store %ar4, %gr3 call <ga:printf>...

Estimados quería realizarles una consulta: Trabajo con una tabla con 23 registros, la cual tiene en la columna "*ar4*" valores de una variable llamada ranking para cada registro, y una columna "percentil" donde le voy a colocar el nivel de percentil en el que se encuentra ese valor (ar4) de cada uno de esos 23 registros. La tabla es la siguiente:: código padre n ar4 percentil 1 27 -0,36...

I'm using the fixed argument in arima. Shouldn't ar4, ar5, and ar6 display as zero in the output? Call: arima(x = window(log(hhprice), start = c(1990, 1), end = c(2003, 3)), order = c(7, 1, 0), xreg = window(ts.union(exa1 = lag(exa, -1), exa12 = lag(exa, -12), exb1 = lag(exb, -1), exc1 = lag(exc, -1), exc12 = lag(exc, -12)), start = c(...

hey can anybody help me? i have to simulate the richardson Arms race model on R.. for my simulation class...

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 0.0646 s.e. 0.0865 0.0885 0.1141 0.1181 0.1196 0.1220 0.1120 0.0908 0.0865 ma1 ma2 sma1 -0.0221 -0.9779 -0.7635 s.e. 0.0539 0.0534 0.0834 sigm...

...argument but the ar3 coefficient is not set to zero: ============================================== > arima(Y, order=c(4,0,0), xreg=1:23, fixed=c(NA,0,0,NA,NA,NA)) Call: arima(x = Y, order = c(4, 0, 0), xreg = 1:23, fixed = c(NA, 0, 0, NA, NA, NA)) Coefficients: ar1 ar2 ar3 ar4 intercept 1:23 0.5370 0 0.4338 -0.8078 5.2991 -0.0421 s.e. 0.0735 0 0.0000 0.1096 0.1081 0.0079 sigma^2 estimated as 0.02665: log likelihood = 6.77, aic = -3.54 =============================================== Why? Thanks, Jenn? Brynjarsd?ttir

Hello! Is there an (semi)automatic way to save registers used by a function? For example, on my target I have to store ar0-ar4 and gr0-gr4, gr5, gr6. For now I just emit huge prologue code to push them all to stack -- even if they are not modified at all. Is there a way to tell LLVM which registers must be stored, and have it automatically issue pushes/pops? I can live with current design, just wondering. - Volodya

Dear all, The standard call to ARIMA in the base package such as arima(y,c(5,0,0),include.mean=FALSE) gives a full 5th order lag polynomial model with for example coeffs Coefficients: ar1 ar2 ar3 ar4 ar5 0.4715 0.067 -0.1772 0.0256 -0.2550 s.e. 0.1421 0.158 0.1569 0.1602 0.1469 Is it possible (I doubt it but am just checking) to define a more parsimonous lag1 and lag 5 model with coeff ar1 and ar5? Or do I need one of the other TS packages? thanks Gerard...

...with an arima and the fit of diff(x) with an arma (same coeff p & d) differ one from another here are the output of R: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% > modelarma<-arma(diff(x),c(7,5)) > modelarma Call: arma(x = diff(x), order = c(7, 5)) Coefficient(s): ar1 ar2 ar3 ar4 ar5 ar6 ar7 ma1 ma2 0.06078 -0.44774 0.41881 0.47624 0.01406 0.06565 -0.06167 -0.01294 0.31313 ma3 ma4 ma5 intercept -0.49027 -0.55461 -0.11520 -0.10692 > modelarima<-arima(x,c(7,1,5)) > modelarima Call: arima(x = x, order = c(7, 1, 5)) Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ma1 ma2...

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 0.0646 s.e. 0.0865 0.0885 0.1141 0.1181 0.1196 0.1220 0.1120 0.0908 0.0865 ma1 ma2 sma1 -0.0221 -0.9779 -0.7635 s.e. 0.0539 0.0534 0.0834 sigm...

On Wed, 2004-06-09 at 04:56, Vladimir Prus wrote: > Hello! > Is there an (semi)automatic way to save registers used by a function? For > example, on my target I have to store ar0-ar4 and gr0-gr4, gr5, gr6. For now > I just emit huge prologue code to push them all to stack -- even if they are > not modified at all. > > Is there a way to tell LLVM which registers must be stored, and have it > automatically issue pushes/pops? I can live with current design, just...

Alkis Evlogimenos wrote: > On Wed, 2004-06-09 at 04:56, Vladimir Prus wrote: > > Hello! > > Is there an (semi)automatic way to save registers used by a function? For > > example, on my target I have to store ar0-ar4 and gr0-gr4, gr5, gr6. For > > now I just emit huge prologue code to push them all to stack -- even if > > they are not modified at all. > > > > Is there a way to tell LLVM which registers must be stored, and have it > > automatically issue pushes/pops? I can live with...

...n how the standard error in arima() is calculated? Also, how can I extract it from the Arima object? I don't see it in there. > x <- rnorm(1000) > a <- arima(x, order = c(4, 0, 0)) > a Call: arima(x = x, order = c(4, 0, 0)) Coefficients: ar1 ar2 ar3 ar4 intercept -0.0451 0.0448 0.0139 -0.0688 0.0010 s.e. 0.0316 0.0316 0.0317 0.0316 0.0296 sigma^2 estimated as 0.9775: log likelihood = -1407.56, aic = 2827.12 > names(a) [1] "coef" "sigma2" "var.coef" "mask" &quot...

...ima(y, order = c(7, 0, 7), xreg = isr) : possible convergence problem: optim gave code=1 In this case, when you print the results from the arima model, the error message appears again: > reg Call: arima(x = y, order = c(7, 0, 7), xreg = isr) Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ma1 ma2 -0.3356 0.0282 -0.0124 0.0604 0.1608 0.7949 0.2286 0.4461 0.0099 s.e. NaN 0.2140 0.1711 0.2545 0.1547 0.0725 NaN NaN 0.1842 ma3 ma4 ma5 ma6 ma7 intercept isr 0.0841 0.0773 -0.054 -0.7227 -...

...2004-06-09 at 05:26, Vladimir Prus wrote: > Alkis Evlogimenos wrote: > > On Wed, 2004-06-09 at 04:56, Vladimir Prus wrote: > > > Hello! > > > Is there an (semi)automatic way to save registers used by a function? For > > > example, on my target I have to store ar0-ar4 and gr0-gr4, gr5, gr6. For > > > now I just emit huge prologue code to push them all to stack -- even if > > > they are not modified at all. > > > > > > Is there a way to tell LLVM which registers must be stored, and have it > > > automatically issue pus...

...pute correlation matrix Coefficients: Estimate Std. Error z value Pr(>|z|) d 4.745e-01 0.000e+00 Inf <2e-16 *** ar1 8.897e-01 0.000e+00 Inf <2e-16 *** ar2 -3.386e-01 0.000e+00 -Inf <2e-16 *** ar3 3.339e-01 2.044e-17 1.634e+16 <2e-16 *** ar4 -4.406e-01 0.000e+00 -Inf <2e-16 *** ar5 3.924e-02 6.349e-18 6.182e+15 <2e-16 *** ar6 -5.184e-01 2.558e-17 -2.026e+16 <2e-16 *** ar7 8.988e-01 0.000e+00 Inf <2e-16 *** ar8 -7.568e-01 3.112e-16 -2.432e+15 <2e-16 *** ar9 3.442e-01 2.175e-22 1.582e+21...

Hello R People: When using the arima function with the seasonal option, are the seasonal options only good for monthly and quarterly data, please? Also, I believe that weekly and daily data are not appropriate for seasonal parm estimation via arima. Is that correct, please? Thanks, Sincerely, Laura Holt mailto: lauraholt_983 at hotmail.com download!

This patch set is intended for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops. The binary patch optimization is important on native case because the paravirt_ops overhead can be reduced by converting indirect

This patch set is intended for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops. The binary patch optimization is important on native case because the paravirt_ops overhead can be reduced by converting indirect

This patch set is intended for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops which depends on the patch series I sent out, ia64/pv_ops, xen: more paravirtualization. The binary patch optimization is important on