similar to: statistical tests under serial dependence

Hello, I have a zoo object that I would like to plot with lattice, because I need the legend outside the plots. However, I don't want to use the strips, because these figures are for publication. I noticed that if I choose strip=FALSE but add the ylab argument, the xyplot.zoo functions plots all the strips. set.seed(1) z <- zoo(cbind(a = 1:5, b = 11:15, c = 21:25) + rnorm(5))

Does anyone know of a package that includes the Modified Sims test [Gewerke, 1983, Sims, 1972]? This test is used in econometrics and is a kind of alternative to the Granger test [Granger, 1969], which is in the package lmtest. Thanks in advance, chris Refernces: Gewerke, J., R. Meese, and W. Dent (1983), "Comparing Alternative Tests of Causality in Temporal Systems: Analytic Results and

Dear All, I'm trying to use waldtest to test poolability (parameter stability) between two logistic regressions. Because I need to use robust standard errors (using sandwich), I cannot use anova. anova has no problems running the test, but waldtest does, indipendently of specifying vcov or not. waldtest does not appear to see that my models are nested. H0 in my case is the the vector of

Hi all, I have been working through the examples in one of the vignettes associated with the 'mlogit' package, 'Kenneth Train's exercises using the mlogit package for R.' In spite of using the code unchanged, as well as the data used in the examples, I have been unable to run a Wald test to test two models. Specifically, I have run the following command, where mc and mi2 are

Hello. I am still a newbie in R. Excuse me if I am asking something obvious. My efforts to get an answer through browsing the mailing archives failed. I want to perform an augmented Dickey-Fuller test and to obtain AIC and BIC and to be able to impose some linear restrictions on the ADF regression so as to decide the correct order of autoregression. However I could find no obvious way to impose

Hello all, I am trying to use the tapply function to sum some values and change the column names of the resulting vector. I input Emp Et 1 10565 ACC 2 7515 ADM 3 625 AGF 4 6243 CNS 5 12721 EDU 6 3924 FIN 7 18140 HLH 8 3686 INF 9 15841 MFG 10 243 MIN 11 1864 MNG 12 4664 OSV 13 5496 PRF 14 4988 PUB 15 2166 REC 16 2153 REL 17 16082 RTL 18 3582 TRN 19 757 UTL 20

I would like to try a likelihood ratio test in place of waldtest. Ideally I'd like to provide two glm models, the second a submodel of the first, in the style of lrt (http://www.pik-potsdam.de/~hrust/tools/farismahelp/lrt.html). [lrt takes farimsa objects] Does anyone know of such a likelihood ratio test? Chris Elsaesser, PhD Principal Scientist, Machine Learning SPADAC Inc. 7921

I'm getting dmesg flooded with messages like this one: usbdevfs: usb_submit_urb returned -28 What's wrong? I'm using Linux kernel 2.4.32 and nut-2.0.3. The /etc/ups.conf contains [mgeups] driver = newhidups port = auto desc = "MGE UPS" Cheers, Rodrigo -- *** Rodrigo Martins de Matos Ventura <yoda@isr.ist.utl.pt> *** Web page:

Hi, I am trying to run a panel regression using 88 observations and 9 variables. In-built Hausman Test did not work, then I found a code for auxiliary regression method for the Hausman test. The panel models are: fe=plm(gd ~ l+g+o+c+g1+h+n+r, model = "within", data = new.frame,index = c("id")) re=plm(gd ~ l+g+o+c+g1+h+n+r, model = "random", data = new.frame,index =

Dear all, I have been trying to connect a usb external disk to a Windows Server 2008 guest through Xen 3.2. The guest is working properly and its .cfg file is: ******************************************* import os, re arch = os.uname()[4] if re.search(''64'', arch): arch_libdir = ''lib64'' else: arch_libdir = ''lib'' kernel =

Not sure how off-topic this is, but should we consider/have we considered porting our InstCombines to Alive? The PLDI '15 paper even demos C++ extraction from Alive theorems. I think it'd be a small step from that to extracting tightly optimized VM code, not unlike what Tablegen emits. Everything would be so clean and readable and organized. And edge cases can still be handled manually,

Hi, Today I found a nice bug in the JIT global variable emitter. The problem may lead to an assert() failure when doing the following: 1) compile some function 2) emit a global variable 3) compile another function. an assert() may trigger in the JIT memory manager This happens because the JIT global variable emitter is using the MachineCodeEmitter::allocate() function, which uses memory

Hi Sanjay, You used Alive correctly, of course :) At this moment we cannot give you the best precondition. It’s on the todo list, but it’s not even started yet. It’s a much harder problem to solve. We do have a mode to compute the best set of nsw/nuw/exact attributes in the transformed expression, but it’s not enabled on the web interface yet (InstCombine was missing quite a few cases last

Hello, What options are available for me to debug my R scripts? For example I normally do something like >source("myfunctions.R") >function1("height", "weight") myfunctions.R is a large R source file that contains many functions. function1 is the "main" function in myfunctions.R. It calls many other user-written functions that are also in

Hi, As a follow up of this thread I've made a patch that implements a simple approach to propagate the function return values as described previously. It can transform e.g. the following program: define i32 @f(...) nounwind { (...) %cond = select i1 %tobool, i32 2, i32 3 ; <i32> [#uses=1] ret i32 %cond } define i32 @g(...) nounwind { entry: %call = call i32 (...)* @f() ;

Ok, makes sense. My suggestion is that we patch the IR Verifier to ensure that the mask is indeed a vector of constants and/or undefs. Right now it only runs the standard checks for instructions. We will also run Alive2 on the test suite to make sure undef is never replaced in practice. Thanks, Nuno -----Original Message----- From: Eli Friedman <efriedma at quicinc.com> Sent: 27 de

On Oct 13, 2008, at 2:42 PMPDT, Nuno Lopes wrote: > Hi, > > Today I found a nice bug in the JIT global variable emitter. > The problem may lead to an assert() failure when doing the following: > 1) compile some function > 2) emit a global variable > 3) compile another function. an assert() may trigger in the JIT memory > manager > > This happens because the JIT

>> Today I found a nice bug in the JIT global variable emitter. >> The problem may lead to an assert() failure when doing the following: >> 1) compile some function >> 2) emit a global variable >> 3) compile another function. an assert() may trigger in the JIT memory >> manager >> >> This happens because the JIT global variable emitter is using the

Hi all-- We are planning an intervention study for adolescent alcohol use, and I am planning to use simulations based on a hurdle model (using the hurdle() function in package pscl) for sample size estimation. The simulation code and power code are below -- note that at the moment the "power" code is just returning the coefficients, as something isn't working quite right. The

I am calculating cox propotional hazards models with the coxph function from the survival package. My data relates to failure of various types of endovascular interventions. I can successfully obtain the LR, Wald, and Score test p-values from the coxph.object, as well as the hazard ratio as follows: formula.obj = Surv(days, status) ~ type coxph.model = coxph(formula.obj, df) fit =