I have ussed lm to generate a basic line correlation: fit = lm(hours.of.sleep ~ ToSleep)
Note: From "Bayesian Computation with R", Jim Albert, p. 7 I understand the simple y = mx + b line that this fits the data to. Now apparently I don't understand formulas. The documentation indicates that there is an implied "intercept" in the formula so now I want to try and fit the line to a second degree polynomial so I tried: ft = lm(hours.of.sleep ~ ToSleep ^ 2 + ToSleep) and I still seem to get results that indicate a slope intercept, y = mx + b, type of fit. Can anyone give me a short tutorial on the formula syntax? I would like to fit the data to 2nd and higher order polynomials, 1 / x, log(x), etc. I am sorry but I could not glean this information from the help page on lm. Thank you. Kevin ______________________________________________ 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.
