similar to: Plot for 10 years extrapolation

I have the following code #################################################################### library(zoo) miedate <- yearmon((2006)+seq(0,23)/12) tab <- zoo(cbind(A = c(79.47, 89.13, 84.86, 75.68, 72.82, 78.87, 93.46, 78.18, 82.46, 77.25, 80.95, 84.39, 81.7, 74.76, 65.29, 60.3, 66.59, 73.19, 92.39, 65.76, 77.45, 74.22, 101.36, 100.01), B = c(77.95, 76.73, 51.2, 51.86, 51.29, 49.45,

Need Help in Debugging below script:-------------------------------- dat <- get_majorlandmarks(dat,Dmin,Per) fit_xts <- xts(dat$fit,order.by = dat$Date,frequency = 365) close_xts <- xts(dat$Close, order.by = dat$Date, frequency = 365 ) majorlandmarks_xts <-xts(dat$Close[dat$majorlandmarks==TRUE], order.by = dat$Date[dat$majorlandmarks==TRUE], frequency = 365 ) minorlandmarks_xts

Hello, I have a few questions, regarding the smoothScatter function. I have a scatter plot with more than 500,000 data points for two samples. So, I am wanting to display the density in colors to convince people that my good correlation coefficient is not due to an "influential point effect" and plus, I also want to make my scatter plot look pretty. Anyway ... I have been able to

http://bugs.freedesktop.org/show_bug.cgi?id=11935 Summary: DMA queue hang Product: xorg Version: 7.2 Platform: x86 (IA32) OS/Version: Linux (All) Status: NEW Severity: normal Priority: medium Component: Driver/nouveau AssignedTo: nouveau at lists.freedesktop.org ReportedBy: drcosquared at

Am 08.10.2013 14:46, schrieb Stefan G. Weichinger: >> Try enabling the flag, re-emerging the package, setting the logs and >> then reproduce it again. Check the logs and you should see why it's >> disconnecting. The docs say that libvirtd has to listen on the TCP port ... checked that: # netstat -alnp | grep libv tcp 0 0 0.0.0.0:16509 0.0.0.0:*

I have written a function to emulate minitab's QQ plotting output (with SW test and AD test results on the graph): mtab.norm<-function(x) { library(nortest) library(lattice) x<-as.numeric(x) x<-as.vector(x) plot.ht<-4.6 plot.wd<-4.6 pt.ht=plot.ht/5 txt.sz<-(plot.ht/7.5) X11(width=plot.wd, height=plot.ht, bg='gray96') qqplot(x, pch=16, cex=pt.ht,

I would like to hear from some one who has ecb, multiple modes with ruby mode + html mode, rails mode all working together and playing well. I had ecb working with Ruby syntax highlighting. That was a no-brainer since I just had to apt-get them on my Debian Sarge box. It got a bit more comlex after I got most of the .el files in the articles http://www.emacswiki.org/cgi-bin/emacs/RubyOnRails

days=Sys.Date()-1:70 price=abs(rnorm(70)) regular=rep(c(0,0,0,0,1,0,1,0,0,1),c(7,7,7,7,7,7,7,7,7,7)) y=data.frame(cbind(days,price,regular)) y is like days price regular 1 14990 0.16149463 0 2 14989 1.69519358 0 3 14988 1.57821998 0 4 14987 0.47614311 0 5 14986 0.87016180 0 6 14985 2.55679229 0 7 14984 0.89753533 0 the output I want:

Hello all, I am very new to R and i am facing two problems. First i didn't succeed changing the konsole language in english even after trying the line command set language='en'. I would like to plot ROC curves. I have a serie of 10 threshold tests that i do for 10 patients. The prediction for the patients is always the same but the status can change given to the considered threshold.

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"

Dear R-experts, I write to you to know if somebody is aware of a R package (or function) able to plot graphs for extrapolation. I need to be clear on what extrapolation really is to me. It is when we use the model for X variables outside the range of X variables that were used to construct the model and estimates.? What I am really looking for is that beyond confidence intervals for predictions

I am getting the following error on boot on a new install Centos 51: #/bin/sh ro no such file or directory then a kernel panic I have traced it to the call ''switchroot'' in the init script in the initrd. the exact same build without zen has no problems. the boot messages look like: Attaching to iSCSI storage iscsistart: transport class version 2.0-724. iscsid version 2.0-865

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,

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

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 am using the libvirt version that comes with Ubuntu 18.04.3 LTS. I have written a script that backs up my virtual machines every night. I want to limit the amount of memory that this backup operation consumes, mainly to prevent page cache thrashing. I have described the Linux page cache thrashing issue in detail here:

Hi R folks, I am using R version 2.2.1 for Unix. I am exploring the rpart function, in particular the rpart.control parameter. I have tried using different values for xval (0, 1, 10, 20) leaving other parameters constant but I receive the same tree after each run. Is the10 fold cross-validation default still running every time? I would expect the trees to change at least a little when I