search for: extrapolize

Dear list members, When I create a simple scatterplot with a regression line (se below) the line is automatically extrapolated outside the range of data points. Why is this and how can I prevent R from extrapolating the regression line? Thank you in advance, Johan model<-lm(Herb~Para) plot(Para,Herb) abline(model)

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

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

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 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.

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 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

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

Dear R-help, I have fitted a glm logistic function to dichotomous forced choices responses varying according to time interval between two stimulus. x values are time separation in miliseconds, and the y values are proportion responses for one of the stimulus. Now I am trying to extrapolate x values for the y value (proportion) at .25, .5, and .75. I have tried several predict parameters, and they

Hi, I have been trying to do numerical differentiation using R. I found some old S code using Richardson Extrapolation which I managed to get to work. I am posting it here in case anyone needs it. ######################################################################## richardson.grad <- function(func, x, d=0.01, eps=1e-4, r=6, show=F){ # This function calculates a numerical approximation

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"

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

> Hello, Hi :) First off, could you try to set your email client to break long lines before transmitting? In my (somewhat outdated) pine, the lines appear as VERY long lines when I try to reply, making it hard to read :) Minor detail though, I should probably fix pine. Some day. > The way you describe how the jitter buffer should be implemented makes me > wonder: How does the

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 R users, i try to integrate lgamma from 0 to Inf. But here i get the message "roundoff error is detected in the extrapolation table", if i use 1.0e120 instead of Inf the computation works, but this is against the suggestion of integrates help information to use Inf explicitly. Using stirlings approximation doesnt bring the solution too. ## Stirlings approximation lgammaApprox

I am using a multiple additive model (in the quantreg package) and I would like to 'extract' the fitted model formulae ie- for a straight line the formula would be y= 'a+b*c' for my multiple model I would expect somthing more complex because the model is not linear (its a bit like a GAM) but given I can plot the model using # f<-fitted(model) #lines(f) there must be a formula

Hello, I'd like to generate some reports on users log in and log out times, is there a way to do this with/without using smbd log files? I was looking at the log files and it looks like I can determine the time of login by looking for : [2006/02/10 09:31:46, 3] libsmb/ntlmssp.c:ntlmssp_server_auth(615) Got user=[jsmith] domain=[CORP] workstation=[DHCP31247] len1=24 len2=24

I've already fixed the bugs, but as with the last one, this is not critical enough to allow breaking current R-devel's code freeze. I hope I will have corrected it for 1.5.1.. ## Here is code reproducing the problems; ## I use try(.) whenever I know the current versions of R would ## give an error: library(splines) x <- c(1:3,5:6) y <- c(3:1,5:6) (isP <- interpSpline(x,y))#

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,