similar to: [LLVMdev] eleminate unsigned long long in llc march=c

Sorry for the simple question, but I am trying to read an "unsigned long long" using the R readBin() function. Can someone point me in the right direction, or am I better off using C for such things? The file that I am reading will have been produced on the same machine that is doing the reading. Thanks, Sean

Asterisk Users, I am running Asterisk 1.2.13 on Debian Etch with McLeodUSA's T1 service. I have two Netgear switches on my T1 router, one for VOIP and another for data. I use a gigabit switch for all VOIP and a regular 10/100Mbps switch for all data. This morning I saw this message a few times on the Asterisk command line. The lagged cause garbled phone calls. Is my network to

Dear Community, I am new to R and have a question concerning the causality () test in the vars package. I need to test whether, say, the variable y Granger causes the variable x, given z as a control variable. I estimated the VAR model as follows: >model<-VAR(cbind(x,y,z),p=2) Then I did the following: >causality(model, cause="y"). I thing this tests the Granger causality of

Hello! I have a data frame A (below) with a grouping factor (group). I take my DV and create the new, lagged DV by applying the function lag.it (below). It works fine. A <- data.frame(year=rep(c(1980:1984),3), group= factor(sort(rep(1:3,5))), DV=c(rnorm(15))) lag.it <- function(x) { DV <- ts(x$DV, start = x$year[1]) idx <- seq(length = length(DV)) DVs <- cbind(DV, lag(DV,

Hello, I'm trying to model the entry of certain firms into a larger number of distinct markets over time. I have a short time series, but a large cross section (small T, big N). I have both time varying and non-time varying variables. Additionally, since I'm modeling entry of firms, it seems like the number of existing firms in the market at time t should depend on the number of firms at

Hi, I was wondering why the results from lm and dynlm are not the same for what I think is the same model. I have just modified example 4.2 from the Pfaff book, please see below for the code and results. Can anyone tell my what I am doing wrongly? Many thanks, Werner set.seed(123456) e1 <- rnorm(100) e2 <- rnorm(100) y1 <- ts(cumsum(e1)) y2 <- ts(0.6*y1 + e2) lr.reg <- lm(y2

Folks, I'm most confused in trying to do something that (I thought) out to be mainstream and straightforward R. :-) Could you please help? I am doing an ordinary linear regression. My goal is: After a regression, to make residuals, and make a new variable which is the lagged residuals (lagged by 1). I will use this variable in a 2nd stage regression (for an error-correcting model). This

hi guys, i have some trouble in creating lagged variables to use as external regressors. i'm trying to use lag(x) but it gives me as result the same time series (x), adding this part at the end: attr(,"tsp") [1] 0 2323 1 where do i wrong?are there other functions to be used? thanks sara -- View this message in context:

Hi, running Asterisk 1.6.2.0 and have started to see in messages: [Jan 14 05:43:43] NOTICE[29231] chan_sip.c: Peer '100' is now Lagged. (4007ms / 3000ms) [Jan 14 05:43:53] NOTICE[29231] chan_sip.c: Peer '100' is now Reachable. (9ms / 3000ms) [Jan 14 05:44:02] NOTICE[29231] chan_sip.c: Peer '100' is now Lagged. (5008ms / 3000ms) [Jan 14 05:44:12] NOTICE[29231] chan_sip.c:

Dear All, I am looking for a function to get lagged dates from an input vector of dates, whereas the lag is specified by another vector. Each date is lagged by a different number of days. (specified in vector n) Example: input <- as.Date( c( "2002-01-30", "2002-02-24", "2002-03-31") ) n <- c(10, 20, 31) Desired Result:

I'm currently working with some time series data with the xts package, and would like to generate a forecast 12 periods into the future. There are limited observations, so I am unable to use an ARIMA model for the forecast. Here's the regression setup, after converting everything from zoo objects to vectors. hire.total.lag1 <- lag(hire.total, lag=-1, na.pad=TRUE) lm.model <-

Hi all. I'm looking for robust ways of building lagged variables in a dataset with multiple individuals. Consider a dataset with variables like the following: ## set.seed(123) d <- data.frame(id = rep(1:2, each=3), time=rep(1:3, 2), value=rnorm(6)) ## >d id time value 1 1 1 -0.56047565 2 1 2 -0.23017749 3 1 3 1.55870831 4 2 1 0.07050839 5 2 2 0.12928774 6

Using zoiper on a nexus 4, asterisk 11.5.1, sometimes we see failed authentication. The secret seems correct, so we can't figure out why we're getting failed authentication. But at the same time the device shows as registered: [Oct 8 18:14:14] NOTICE[510]: chan_iax2.c:11071 socket_process_helper: Peer 'n4' is now REACHABLE! Time: 441 [Oct 8 18:15:58] NOTICE[519]:

Hi, I was wondering if there is a more efficient way of handling the following method of creating a lagged value in a data frame without using the recursive 'for(i in 1:n)' loop and without using as.ts #Steps to creating a lag variable in a data frame 'my.dat.fr' # with 275 columns, 2400 rows of numbers and factors . The #variable x is a factor of #with five different levels the

hi folks. we recently deployed 10 Cisco 7960G w/ SIP firmware 7.3 on our network and we start having problems of dropping calls (actually the calls wasn't dropped it just the sound was muted for about 5-10 seconds, but most users will think the call dropped and hangup/redial). i've check the console output. there was a lot of messages like the following: Sep 28 15:00:49 NOTICE[8182]:

Hi I'm computing the correlation between two time-series x_t and y_t-1 (time-series lagged using the lag(y,-1) function) using the cor() function and the returned value is different from the value of ccf() function at the same lag. Any ideas why this is so? Thanks in advance for any hints. Mihnea [[alternative HTML version deleted]]

Anyone having problems with voxee since last few days or is it just me ? In peek hours i get LAGGED when i do a iax2 show peers or even 1000 ms latency . Most of time it is 20 ms or so but when i start sending traffic to them latency increases to 1000 ms or even LAGGED ( also shows high in peak time even when no high latency ). No problems with any other provider . Anyone else having same problem

Hi! I am trying to get R to run regressions for me of a variable on lagged differences of itself. Specifically: x-x(-1) = a + b1(x(-1)-rx(-2))+b2(x(-4)-rx(-5))+e I need to do this a lot of times, altering the value of r. What I've been trying to do was to use the lag() command to create lagged versions of these variables and then constructing these differences by hand (i.e. creating

I am new to R and new to time series modeling. I have a set of variables (var1, var2, var3, var4, var5) for which I have historical yearly data. I am trying to use this data to produce a prediction of var1, 3 years into the future. I have a few basic questions: 1) I am able to read in my data, and convert it to a time series format using 'ts.' data_ts <- ts(data, start = 1988, end =

Hello! I have a data set: set.seed(123) y<-data.frame(week=seq(as.Date("2010-01-03"), as.Date("2011-01-31"),by="week")) y$var1<-c(1,2,3,round(rnorm(54),1)) y$var2<-c(10,20,30,round(rnorm(54),1)) # All I need is to create lagged variables for var1 and var2. I looked around a bit and found several ways of doing it. They all seem quite complicated - while in