Here is a bit of an exploration of your data but first a couple of notes. * the information about Excel is probably a bit superfluous here. Some of us have no idea about Excel, and rather hope it can stay that way.
* With such a short series, you don't stand much chance of fitting a time series model such as with arima. It's clearly not stationary, too. If you had multiple growth curves you may stand some chance of fitting a correlated model, but with just one, I don't think so. For now, I think you just may have to make the hopeful assumption of independence. You might like to look at this. ________________________________ weightData <- data.frame(weight = c(2.1,2.4,2.8,3.6,4.1,5.2,6.3), week = 1:7) plot(weight ~ week, weightData) plot(log(weight) ~ week, weightData) ### clearly the log plot seems to linearise things. ### Try an non-linear regression: wModel <- nls(weight ~ alpha + beta*exp(gamma*week), weightData, start = c(alpha = 0.0, beta = 1, gamma = 0.2), trace = TRUE) #### you should look at the residuals from this to see if the assumptions #### look reasonable. With only 7, you can't see much, though. #### now suppose you want to predict for another 3 weeks: newData <- data.frame(week = 1:10) newData$pweight <- predict(wModel, newData) plot(pweight ~ week, newData, pch = 4, col = "red", ylab = "Weight", xlab = "Week") with(weightData, points(week, weight)) #### looks OK to me (thought fish cannot keep on growing exponentailly #### forever - this is clearly a model with limitations and you have to #### be careful when pushing it too far). #### finally predict on a more continuous scale and add in the result as #### a blue line. lData <- data.frame(week = seq(1, 10, len = 1000)) with(lData, lines(week, predict(wModel, lData), col = "blue")) #### Now that we have over-analysed this miniscule data set to blazes, #### perhaps it's time for a beer! __________________________________ Bill Venables http://www.cmis.csiro.au/bill.venables/ -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Felipe Carrillo Sent: Sunday, 5 April 2009 4:13 PM To: r-h...@stat.math.ethz.ch Subject: [R] predicting values into the future Hi: I have usually used the GROWTH() excel function to do this but now want to see if I can do this with R. I want to predict values into the future, possibly with the predict.arima Function. I have the following weekly fish weight averages: weight <- c("2.1","2.4","2.8","3.6","4.1","5.2","6.3") week <- c("1","2","3","4","5","6","7") I would like to predict what the weight will be by week 10 based on my weight values and make a line plot of all the weights(including the predicted values). I have two questions: 1- Should the predicted values be linear or exponential? 2- Is the predict.arima function appropriate to do this? Thanks in advance. Felipe D. Carrillo Supervisory Fishery Biologist Department of the Interior US Fish & Wildlife Service California, USA ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.