similar to: Forecast Package: Draw two lines on the same plot

Hi there, I've looked through the very helpful advice about adding fitted lines to plots in the r-help archive, and can't find a post where someone has offered a solution for my specific problem. I need to plot logistic regression fits from three differently-sized data subsets on a plot of the entire dataset. A description and code are below: I have an unbalanced dataset

Dear list, I've noticed a width/height scaling bug in multi-panel plots when you change the active plot using par mfg The underlying par setting that maps the full plot width (or height) to the numerical axis labels does not get updated when you change plots with par(mfg=...). Thus plotting a point at c(10,10) in the first plot will only be in the correct place IF the most recently

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

Ian No differences with Adobe X with the following windows(6,6) #pdf(file = "TestPlot.pdf", 6, 6) #{ plot(b, l, type = "l", ylim = c(y.min, y.max), lwd = 2, xlab = expression(beta), ylab = "", col = "green", yaxt = "n", xaxt = "n") points(b, p, type = "l", lty = "dotted", lwd = 2, col = "red") points(b,

Dear all, I am new to this list and, unfortunately, could not provide help to anyone as yet. I hope I can do so in the future, though! Until then, I am grateful for helpful hints from you more experienced users. For use in an upcoming publication, I generated an eps figure from my x11 window under Debian Linux with dev.copy2eps. It contains 28 line plots with 2 lines each, one solid and one

Hi, I've fitted a GARCH(1,1) for the residuals of my time serie (X). X is an ARMA(1,1) process. Now I want to do a n-step forecast for X, knowing these processes. How can I do this? I know that there's a command: predict() for ARIMA processes and so on, but what about GARCH? I've got: arma=arima(x, order=c(1,0,1)) (...) garch11<-garch(residuals(x),order = c(1, 1))

Hello All, I trying to use the function auto.arima(....) from package forecast but I have a problem. My steps after I used the function auto.arima(...) I create the time series like this: >bbrass = scan("C:/Program Files/R/data PTIN/my_file.dat") >regts.start = ISOdatetime(2006, 7, 1, hour=0, min=0, sec=0, tz="GMT") #2006 07 01 00 >regts.end = ISOdatetime(2006, 7,

Hello, I drew a plot of weight and height of people and fitted it with a polynominal regression x^2. (using curve()) Now I would like to draw the confidence interval line for the fitted curve. Please kindly advise the code for the purpose. Thank you. Elaine [[alternative HTML version deleted]]

Hi, Here are a few errors I found as well as a few comments. 1) In the man page of par: lty: The line type. Line types can either be specified as an integer (0=blank, 1=solid, 2=dashed, 3=dot- ted, 4=dotdash, 5=longdash, 6=twodash) or as one of the character strings `"blank"', `"solid"',

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

Dear All, I have loaded package(tseries), but when I run predict.garch(...) R tells me could not find function "predict.garch", however ?predict.garch shows me something. I am confused about this. How can I forecast garch volatility? I have tried: predict(...,n.ahead=...),give me fitted value predict(...,n),give me NA,NA

I am graphing longitudinal data from three time points. I'd like to draw a solid line from point 1 to point 2, and then a dashed line from point 2 to point 3. It works if I do it in two steps: > first.vector <- c(mean(year1$variable1), mean(year2$variable1)) > second.vector <- c(NA, mean(year2$variable1), mean(year3$variable1)) > plot(first.vector, type="b",

Dear R users, Please let me know how to plot the forecast in such a model: First I do it simple with ARIMA model that works ok with the codes provided to me at the lecture: arima<-arima(HCPIlong, order=c(1,1,0)) arima.predict<-predict(arima, n.ahead= 5 ) ts.plot(HCPIlong,arima.predict$pred,lty=1:2, main="Forecast of HCPI") But I need to include the additional variable in my

Hello, I have a question about forecast under model arima(1,1,1). I construct this model on 1000 observations and find the forecast for following, for example, 100 observations. But it' s necessary for me to get the predicted values of the previous 100 observations and compare it with actual values. Please, tell me what should be done. Thank you very much [[alternative HTML version

Hi, I've got a little problem using auto.arima. I run the following command auto.arima(drivers,ic="aic",d=1,D=1,max.order=10,max.p=5,max.q=5,max.P=5,max.Q=5,stepwise=FALSE,allowdrift=FALSE) and I get the following output : Series: drivers ARIMA(0,1,1)(5,1,1)[12] Coefficients: ma1 sar1 sar2 sar3 sar4 sar5 sma1 -0.6421

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

Hey, thank you for reading this post, maybe you can help me. I am trying to nest grids, but it does not work, see my lines (extracted and shorten): Just copy, save as html, and then you''ll see what I mean: the orange solid bordered list items should be in the red dotted list container, but that''s not the case. Can you help me with this? <html> <head>

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

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, I am trying to fit a multivariate time series model using DCC GARCH model and forecast it. The data looks like this: > head(datax) x vibration_x Speed 1 2017-05-16 17:53:00 -0.132 421.4189 2 2017-05-16 17:54:00 -0.296 1296.8882 3 2017-05-16 17:55:00 -0.572 0.0000 4 2017-05-16 17:56:00 -0.736 1254.2695 5 2017-05-16 17:57:00 0.000