On Tue, Jul 24, 2012 at 2:06 PM, <bunnylove...@optonline.net> wrote: > Yes, why wouldn't I? It's a linear model between two sets of data: x and y.
Conventionally, one predicts y based on x -- which is specified y ~ x, not x ~ y. (Predictors on the RHS, predicted on the LHS) > > Also, what would the new data be if i want to predict into the future? So, > for example, the data goes from a month ago to today. I want to predict what > tomorrow's data would be. So what is "newdata"? Well, it sounds like you really need a time series model, not a linear regression. Linear regressions are more or less unordered in x: that is, there's no unique "next" value for "x" -- there's just what happens next. For instance, if I were doing a gas law experiment and I saw the pressure of some fixed amount and volume of helium at temps 30, 40 25, 50, 75, and 20 C -- what would I expect from my next experiment? Who knows -- you haven't told me the independent variable yet. Time series models on the other hand have a well defined "next observation". It is sometimes possible (though not advisable) to fake a time series model by regressing on the date (after conversion to a number) but you'll still have to say what the next input is by converting the date of the future obs to a number again. This is probably not statistically advisable. Incidentally, please keep posts on the list archives and cc R-help unless there's good reason to keep the discussion private. Michael ______________________________________________ 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.
