similar to: Plotting extrapolation with R like AUTOBOX does

Dear R-Experts, Here below my R code working but I don't know how to complete/finish my R code to get the final plot with the extrapolation for the10 more years. Indeed, I try to extrapolate my data with a linear fit over the next 10 years. So I create a date sequence for the next 10 years and store as a dataframe to make the prediction possible. Now, I am trying to get the plot with the

Dear all, I'm trying to fit a curve to some 1 year failure-time data, so that I can extrapolate and predict failure rates up to 3 years. The data is in the general form: Treatment Time Status Treatment A 28 0 Treatment B 28 0 Treatment B 28 0 Treatment A 28

Hi all, I used R (vegan package) to make rarefaction curves and I calculated the Chao index for each curve. However, the plateau is far from reached. What I want to do now is the following: Based on the Chao index, I want to extrapolate the curve so I get an x-value which gives me an estimation of the total number of clones I'd have to pick up and sequence in order to have a full coverage of

Hi, I am facing a problem in extrapolation of data series. It is a series of Bond yields, I am having the yield for 1 year to 30 years. I want to find the yield for 0.5 year and 30.5 years. I used the Langrange's Extrapolation but the extrapolation deviates from the normal trend ( as we can see in theoritical yield curves) very sharply, as go on increasing my years from 30 years to 35 years

Dear?R experts, ? I?am trying to do linear extrapolation on a dataset?like the attached document. I looked at the approx and approxfun function that seem to do this function, but not fully understand them. I was wondering if someone could help with writing commands to do the following based on the attached file's example: ? ID#1 and ID?#2 both have response parameters ("MEASUREMENT"

Readers, Data was imported using the read csv command: dataimport<-read.csv("/path/to/dataimport.csv") 10,2000 12,2001 13,2002 15,2003 17,2004 Using the help contents for 'predict.lm' (i.e. ?predict.lm) a new data frame was created dataimportextra<-data.frame(x=seq(1990,2010,1)) predict(lm(dataimport),dataimportextra[,2],se.fit=TRUE)

Hi all, I'm fitting a line to my dataset. Later I want to predict missing values that exceed the [min,max] interval of my empirical data, therefore I choose surface="direct" for extrapolation. l1<-loess(y1~x1,span=0.1,data.frame(x=x1,y=y1),control=loess.control(surface="direct")) In my application it is highly important that the fitted line intercepts at the point of

Hi, We have an experiment where individuals responses were measured over 5 days. Some responses were not obtained because we only allowed individuals to respond within a limited time-frame. These individuals are given the maximum response time as they did not respond, yet we feel they may have done if given time (and by looking at the rest of their responses over time, the non-response days stand

Dear Community, this is my first programming in R and I am stuck with a problem. I have the following code which automatically calculates Granger causalities from a variable, say e.g. "bs" as below, to all other variables in the data frame: log.returns<-as.data.frame( lapply(daten, function(x) diff(log(ts(x))))) y1<-log.returns$bs y2<- log.returns[,!(names(log.returns) %in%

DeaR list, I'm running an external program that computes some electromagnetic response of a scattering body. The numerical scheme is based on a discretization with a characteristic mesh size "y". The smaller y is, the better the result (but obviously the computation will take longer). A convergence study showed the error between the computed values and the exact solution

Dear Community, For my masters thesis I need to perform a multivariate granger causality test. I have found a code for bivariate testing on this page (http://www.econ.uiuc.edu/~econ472/granger.R.txt), which I think would not be useful for the multivariate case. Does anybody know a code for a multivariate granger causality test. Thank you in advance. Best Regards -- View this message in context:

Hello to all- 1. When I run a regression which implements the augmented Dickey-Fuller test, I am confused about the names given to the regressors in the output. I understand what "xGE" stands for in a standard "lm" test involving an independent variable GE for instance, but if I lags and or differences are included in the model, what do the following "output" stand

Hi folks, I am trying to perform a Granger causality analysis with panel data. There are some packages around for panel data analysis and Granger causality. However, I have found neither a package for both panel data and Granger causality nor any R procedures (homogenous/heterogenous causality hypotheses, related tests such as Wald, unit root tests etc.). Of course, someone must have

Hello, Dovecot keeps crashing at login time. Things were fine on 2.2.2 Fatal: master: service(imap): child 5014 killed with signal 11 Here is the trace: Core was generated by `imap'. Program terminated with signal 11, Segmentation fault. #0 0x00000000105cdfdf in mailbox_list_get_storage (list=0x7fffffffe310, vname=0x10a04ab0 "INBOX/spam", storage_r=0x7fffffffe308) at

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 generated predicted risk of death for each subject in the study by means of Cox proportional hazards model at 8 year of follow-up, a time point at which follow-up was more than 90% complete. It is possible to extrapolate to 10-year the predicted risk of each subjet by assuming an exponential distribution? Any help would be greatly appreciated. Thanks for your consideration.

Dear R experts: I need to get this plot, but also with 95% confidence interval bands: hour <- c(1, 2, 3, 4, 5, 6) millivolts <- c(3.5, 5, 7.5, 13, 40, 58) plot(hour, millivolts, xlim=c(1,10), ylim=c(0,1000)) pm <- lm(millivolts ~ poly(hour, 3)) curve(predict(pm, data.frame(hour=x)), add=TRUE) How can the 95% confidence interval band curves be plotted too? Sincerely,

Dear R Users, The granger.test command in the MSBVAR package estimates all possible bivariate Granger causality tests for m variables. If one passes a data frame with 3 rows, it returns 6 granger tests in two rows, one for the F-statistic and another for the p-value. For example: > a<-rnorm(1:10) > b<-c(lag(a),rnorm(1)) > c<-c(lag(b),rnorm(1)) >

Hello dudes. I'm developing VAR analysis based on suggestions made by Horváth in its paper Canonical Correlation Analysis and Wiener-Granger Causality Tests. That's the reason I'm looking for if there's any R package to develop Wiener - Granger Causality Test. Thanks a lot for your unvaluable help. Regards from Mexico [[alternative HTML version deleted]]

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