similar to: Question regarding extrapolation

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?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

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

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)

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'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 am trying to generate some Pareto random variates using the inverse method. This is really straightfoward and my R function looks as below : pareto <- function(c, a, cnt=1000) { u <- runif(cnt) x <- (c / ((u ^ (1 / a)))) mean.theo <- ((c * a) / (a - 1)) mean.gen <- mean(x) cat('Pareto mean : theoritical', mean.theo, 'generated', mean.gen,

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)

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

Hi all: Using the example below, is there a way to add Y axis titles to each graphic instead of sharing the same title? library(ggplot2) RT = matrix(c(814, 500, 424, 394, 967, 574, 472, 446),4,2) colnames(RT) = c('repetition','alternation') rownames(RT) = c('7-yrs','11-yrs','15-yrs','21-yrs') rt <- melt(RT) names(rt) <- c("age",

Hi all. This is more of a stats question, I suppose. Let's say I have two separate simple regressions of weight on year from two different datasets. I want to combine the regressions so that I can come up with a single equation for the total weight regressed on year. In reality, there is missing data, so I can't just sum the data across datasets and come up with a regression on the

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,

I have now committed a version "fulfilling" your wish, partly at least, to R-devel . In the new approx(*, na.rm=FALSE) cases, the result of how NA's are treated does depend on the 4 different extrapolation rules {1, 2, 1:2, 2:1} The main reason was that I kept the low level code in C to do +- what it did before which automatically was using 'rule' to determine these

Hi all, This could be naivety/stupidity on my part rather than a problem with model output, but here goes.... I have fitted a fairly simple model m1<-glm(count~siteall+yrs+yrs:district,family=quasipoisson,weights=weight,data=m[x[[i]],]) I want to know if yrs (a continuous variable) has a significant unique effect in the model, so I fit a simplified model with the main effect ommitted...

I've helped a lot of people on the mailing lists and on IRC #asterisk. and wanted to let people know that I will be in Europe between May 19 and June 21. Stockholm (VON 2005), Brussels (holiday/vacation), Amsterdam (holiday/vacation), and Madrid (Astricon). There are several weeks during my trip that I have no current plans for and may add other cities to my itinerary. I'm looking

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 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-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

Folks, I call the function calcAmorts like so: calcAmorts(prevAm, amort, myDates) Note that I use the package lubridate. The last line where do.call is called to first divide all the rows by the first row and then rbind gives the following error: Error in do.call("rbind", apply(amortsByYears, 1, "/", amortsByYears[, : second argument must be a list By contrast if