similar to: Lag vector of dates by vector of days

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,

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

Dear all, I am trying to apply the aggregate() function to calculate correlations for subsets of a dataframe. My argument x is supposed to consist of 2 numerical vectors, which represent x and y for the cor() function. The following error results when calling the aggregate function: Error in FUN(X[[1L]], ...) : supply both 'x' and 'y' or a matrix-like 'x'. I think the

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

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 =

Is it possible to fit a lagged regression, "y[t]=b0+b1*x[t-1]+e", using the function "lag"? If so, how? If not, of what use is the function "lag"? I get the same answer from y~x as y~lag(x), whether using lm or arima. I found it using y~c(NA, x[-length(x)])). Consider the following: > set.seed(1) > x <- rep(c(rep(0, 4), 9), len=9) > y <-

lag is a very confusing function i try to plot lag(x,-h) and y , the results are the same figures for all the h lags. then i saw online people use lag(x,-h) as xreg in arima that won't work in my eyes if lag function is consistent, isnce it returns the same value for all the lags i don't know in which occasion people will use this function besides I suppose Lag(x,h) is the same as

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:

Dear R-help, How could a cross-correlation plot be optimized such that the relationship between seasonal time-series can be studied? We are working with strong seasonal time-series and derived a cross-correlation plot to study the relationship between time-series. The seasonal variation however strongly influences the cross-correlation plot and the plot seems to be ?rather? symmetrical (max

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

Hello, I want to use lag on a time variable but I have to take date into consideration ie I don't want days to overlap ie: I don't want my first time of today to match my last time of yeterday. In SAS I would use : data x; set y; by date tim; previous=lag(tim); if first.date then do; previous=.; end; run; How can I do something similar in R? I can't find

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

Hi all, I?d love to get your feedback on the conflicted package, which provides an alternative strategy for resolving ambiugous function names (i.e. when multiple packages provide identically named functions). conflicted 0.1.0 is already on CRAN, but I?m currently preparing a revision (<https://github.com/r-lib/conflicted>), and looking for feedback. As you are no doubt aware, R?s default

Perhaps I am making this too hard, but how does one regress y(t) on a constant, x(t-1) and y(t-1)? I've tried the manuals and until I get Dalgaard's book (just ordered on Amazon), I am stuck! Thanks to all in advance for your patience and consideration.

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]]

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 <-

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

Suppose we observe N individuals, for each of which we have a time-series. How do we correctly create a lagged value of the time-series variable? As an example, suppose I create: A <- data.frame(year=rep(c(1980:1984),3), person= factor(sort(rep(1:3,5))), wage=c(rnorm(15))) > A year person wage 1 1980 1 0.17923212 2 1981

Hi, I would like to regress y (dependent variable) on x (independent variable) and y(-1). I have create the y(-1) variable in this way: ly<-lag(y, -1) Now if I do the following regression lm (y ~ x + ly) the results I obtain are not correct. Can someone tell me the code to use in R in order to perform a regression using as explanatory variable a lagged dependent variable? My best regards,

Dear all, I am having difficulties using the seemingly-simple function lag. I have a dataframe with several weather variables (maxitemp, windspeed, rainfall etc), and the response variable (admissions). The dataset is fairly large (1530 observations). I simply want to model the response against a lag of a couple of the explanatory variables, say maxitemp and rainfall. I would like to look at