similar to: Forcing coefficents in lm(), recursive residuals, etc.

dear R community: i have been looking but failed to find the following: is there a function in R that updates a plain OLS lm() model with one additional observation, so that I can write a function that computes recursive residuals *quickly*? PS: (I looked at package strucchange, but if I am not mistaken, the recresid function there takes longer than iterating over the models fresh from

Hello R users: I'm trying now to apply the package strucchange to see whether there is a structural change in linear regression. I have noted the following problem that arises in my case with recursive-based CUSUM: generic function recresid() in efp() generates an error, since (probably) it cannot compute the inverse matrix of (X^(i-1)^T)*(X^(i-1)) at each step (i-1), because the matrix

How for the love of god can I prevent the lm() function from padding on to my factor variables? I start out with 2 tables: Table1 123123 124351 ... 626773 Table2 Count,IS_DEAD,IS_BURNING 1231,T,F 4521,F,T ... 3321,T,T Everything looks fine when I import the data. then we get a oh_crap <- lm(table1 ~ Count + IS_DEAD + IS_BURNING, table2) Magically when I look at my oh_crap coefficents

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 <-

Good morning to all, I am encountering a blocking issue when using the function 'breackpoints' from package 'strucchange'. *Context:* I use a data frame, 248 observations of 5 variables, no NA. I compute a linear model, as y~x1+...+x4 x4 is a dummy variable (0 or 1). I want to check this model for structural changes. *Process & issues:* *First, I used function Fstats.* It

When I type library() to see what is installed the following list in RED comes up. Packages in library '/home/jason/R/i686-pc-linux-gnu-library/2.13': abind Combine multi-dimensional arrays aplpack Another Plot PACKage: stem.leaf, bagplot, faces, spin3R, and some slider functions biglm bounded memory linear and

hi: I am trying to fit prediction intervals for an arima object. My search led me to the link: http://finzi.psych.upenn.edu/R/library/forecast/html/forecast.Arima.html which has the function "forecast", as I wanted. However, when I try to run it in R, I get the message: Error in plot(forecast(fit)) : could not find function "forecast" Even the example provided on the page

Good day all, I'm trying to plot a continuous line plot, which is followed by two forecast points eg. one forecast point is 12 months out, and another 24 months out from the last date of the line plot. In my attempts so far, the second plot (the forecast points) is scaled against a new axis scale, thus the two plots are not directly comparable (I need the forecast points to be scaled

I have a large Excel file with SKU numbers (stock keeping units) and forecasts which can be mimicked with the following: Period <- c(1, 2, 3, 1, 2, 3, 4, 1, 2) SKU <- c("A1","A1","A1","X4","X4","X4","X4","K2","K2") Forecast <- c(99, 103, 128, 63, 69, 72, 75, 207, 201) PeriodSKUForecast <-

There appears to be a problem with name matching in list assignment: Version 0.63.0 (November 14, 1998) ... > r <- list() > r$forecast.cov.trend <- 1:12 > r$forecast.cov.zero <- 1:12 > r$forecast.cov <- 1:2 > length(r$forecast.cov) [1] 0 #should be 2 > But note that this works correctly: > r <- list() >

Hello, I am using forecast() in the forecast package to predict future values of an ARIMA model fit to a time series. I have read most of the documentation for the forecast package, but I can't figure out how to obtain the forecast variance for the predicted values. I tried using the argument "se.fit=TRUE," hoping this would work since forecast() calls predict(). Is there an easy

I have a data frame that reads client ID date transcations 323232 11/1/2010 22 323232 11/2/2010 0 323232 11/3/2010 missing 121212 11/10/2010 32 121212 11/11/2010 15 ................................. I want to order the rows by client ID and date and using a black-box forecasting method create the data fcst(client,date of forecast, date for which forecast applies). Assume that I

Dear all, I stumbled upon what appears to be a troublesome issue when sampling from an ARIMA model (from Rob Hyndman's excellent 'forecast' package) that contains a seasonal AR component. Here's how to reproduce the issue. (I'm using R 2.9.2 with forecast 2.19; see sessionInfo() below). First some data: > x <- c( 0.132475, 0.143119, 0.108104, 0.247291, 0.029510,

hello everybody, I would like to fit a model to a times series (testing set) for out of sample predictions using garchFit(). I would like to keep the coefficients of ARMA/GARCH model fixed (as found by fitting the model to my training set). The arima fitting function has such an option for that (fixed=NULL) but the garchFit() doesnt. It is very important for me to keep the same coefficients

Hi, I would like to create variables in R containing parameters of summary(*Forecast Results*). Using the following code: library(forecast) data <- AirPassengers xets <- ets(data, model="ZZZ", damped=NULL) xfor <- forecast(xets,h=12, level=c(80,95)) summary(xfor) the output is: Forecast method: ETS(M,A,M) Model Information: ETS(M,A,M) Call: ets(y = data, model =

hi all. i'm busy with some time series data, starting from an earlier period until the current day. i have created a time series forecast taking into account the entire data from the earlier date up until 2007, using the "forecast" package for R. i am comparing this forecasted data to the actual/ observed data (which starts from the earlier date up until the current day). my

Hello everybody! I have an ARIMA model for a time series. This model was obtained through an auto.arima function. The resulting model is a ARIMA(2,1,4)(2,0,1)[12] with drift (my time series has monthly data). Then I perform a 12-step ahead forecast to the cited model... so far so good... but when I look the plot of my forecast I see that the result is really far from the behavior of my time

Hi Don, wow, you are so right. I picked that piece up from the bloggers tutorial and since I am R naive yet, I thought it was all one step moving_average = forecast(ma(tdat[1:31], order=2), h=5) Truly, I usually print and check at every step I can, as painful as it is sometimes. Great lesson for this novice usR. So the first and last values are NA in each case? Do you know why? Should I replace

Hi all, We followed some books and sample codes and did some EMM estimation, only to find it won't be able to generate forecast. This is because in the stochastic volatility models we are estimating, the volatilities are latent variables, and we want to forecast 1-step ahead or h-step ahead volatilities. So it is nice to have the system estimated, but we couldn't get it to forecast at

(My apologies if this is a repeated posting. I couldn't find any trace of my previous attempt in the archive.) I'm having trouble with forecast() in the dse2 package. It works fine for me on a model without a trend, but gives me NaN output for the forecast values when using a model with a trend. An example: # Set inputs and outputs for the ARMA model fit and test periods